# ElementX — Full content
> Applied AI engineering consultancy founded in 2013 in Auckland, New Zealand. ElementX designs, builds, and deploys enterprise AI.
This file is the full text of the ElementX site for AI ingestion. For a curated index, see /llms.txt.
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# Services
## Conversational AI
URL: https://www.elementx.ai/services/conversational-ai
Type: Solution · Conversational AI
**Understand your customers and meet them in their channel.**
Conversational AI brings together the latest advancements in deep learning and natural language processing to understand and assist your customer with common issues or queries. It can help you rapidly scale your communication channels by freeing up your customer success team to focus on complex issues.
### Where it fits
Online chatbots and digital humans on your website or social channels triage issues and resolve common queries directly. Call-centre voice automation answers questions over the phone or surfaces relevant knowledge-base articles in real time so your agents resolve issues faster.
### How we can help
A simple chatbot is easy. Crafting one that understands the queries that matter, and gracefully handles the ones it doesn't, takes experience. We have deployed conversational AI into chatbots, digital humans, and contact centres across a wide range of industries.
- Develop a strategy for leveraging conversational AI in your business
- Design intuitive and effective conversations
- Implement a high-performing natural language understanding engine using the latest advancements in AI
---
## Computer Vision
URL: https://www.elementx.ai/services/computer-vision
Type: Solution · Computer Vision
**Pictures contain a thousand words, and millions of pixels.**
Deep learning made it practical to extract meaning from images and video at scale. We train and deploy custom vision models that turn pixels into measurable business outcomes, even with relatively small datasets, thanks to advancements in transfer learning.
### What we deliver
Detection, classification, and tracking of people and objects in images or video. Reading text in the wild. Custom models trained on your data, deployed where they need to run.
### How we can help
We collect or ingest training data, label it pragmatically, train models that meet the precision and recall your operation needs, and deploy to the cloud or to the edge.
- Edge or cloud deployment
- Active learning loops to keep models fresh as your domain shifts
- Tight integration with your existing data pipelines
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## Data Science
URL: https://www.elementx.ai/services/data-science
Type: Solution · Data Science
**Predictions, classifications, and recommendations in production.**
For years we have built models that turn historical data into useful predictions about the future. Advances in machine learning and computing capacity have made far more sophisticated models practical, and the application surface keeps growing.
### Where it fits
Product classification at retail and ecommerce scale. User personalisation that adapts in real time using reinforcement learning. Claims automation that processes the low-risk cases straight through and surfaces the rest for human review.
### How we can help
Fit-for-purpose models built on your data with reproducible pipelines, monitoring, and a handover plan so your team can keep them running after we leave.
---
## Enquiry Assistant
URL: https://www.elementx.ai/services/enquiry-assistant
Type: Product · Enquiry Assistant
**Supporting student recruitment at scale.**
Students get instant, accurate responses to a wide range of enquiries throughout the recruitment and enrollment process. The assistant is always on, regardless of time zone or operating hours, and is multilingual so international students get the same experience as everyone else.
### Improve student experience with real-time results
Whether students are asking about admission requirements, course schedules, campus resources, or graduation procedures, the assistant provides a consistent and reliable source of guidance. It streamlines communication, enhances the student experience, and contributes to a more efficient and supportive university environment from the start of each student's academic tenure to the end.
### Reduce contact-centre load
Routine queries are handled by the assistant, freeing your support staff to focus on the complex questions that need a human.
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## Admissions Assistant
URL: https://www.elementx.ai/services/admissions
Type: Product · Admissions
**From admissions to enrollment in record time.**
Supercharge your admissions team and decrease the time and manual processing required to assess applications, so prospective students get their results faster. The assistant addresses enquiries about admission criteria, application procedures, and document submission requirements by engaging directly with the student.
### Manage contact-centre peaks without additional staffing
Admissions is a busy season. The ElementX virtual assistant manages enquiries with a proven 90% containment rate, so your support staff can focus on the complex queries. At the same time, the assistant can ingest academic requirements and transcripts, analyse compatibility with the university, and provide immediate recommendations.
### A guided approach
The assistant guides students through the submission of essential information, reducing the number of dropped or delayed applications and improving the experience for everyone.
---
## AI Tutor
URL: https://www.elementx.ai/services/ai-tutor
Type: Product · AI Tutor
**Customizable AI tutor for every student.**
Provide personalised, on-demand support that helps students grasp complex concepts and reinforces their understanding of course material. Tailored to individual learning styles and pace, the AI tutor adapts its approach, offering additional explanations, practice problems, and real-time feedback. The result is a deeper understanding of the subject matter and students who feel in control of their learning journey.
### Learning anywhere, on any schedule
With 24/7 availability, the tutor accommodates diverse schedules, providing consistent assistance whenever students need it. An augmented learning environment that complements traditional instruction.
### Customised learning outcomes
Lecturers can tailor the AI tutor to align with specific course objectives, adjusting the content and delivery to reinforce key concepts. Analytics and performance metrics provide insight into individual and class-wide strengths and weaknesses, helping instructors refine their teaching strategies.
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## Enrollment Assistant
URL: https://www.elementx.ai/services/enrollment-assistant
Type: Product · Enrollment Assistant
**Automate program selection for each student.**
Assisting students with course selection and enrollment is highly manual and requires extensive knowledge of course availability, compatibility, and degree requirements. The ElementX enrollment assistant streamlines the process by digesting your university's knowledge base and serving course requirements, prerequisites, and more, with the student's specific requirements in mind.
### Personalised guidance, tailored to each student
A virtual assistant that guides students through the program-selection process and offers instant, accurate responses about admission requirements, program details, and potential career paths. Contextually relevant information tailored to individual preferences enhances decision-making and simplifies the journey from enrollment to program selection.
### Accurate results improve student success
Inaccurate information leads to misunderstandings and misinformed decisions. Fine-tuned responses ensure students receive reliable, up-to-date details about admission criteria, program requirements, and related processes.
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## Virtual Assistant
URL: https://www.elementx.ai/services/virtual-assistant
Type: Product · Virtual Assistant
**24/7 personalised support for tomorrow's graduates.**
Don't let your future students wait for a response. The ElementX Virtual Assistant delivers instant answers across the student's entire journey, freeing up student-support team time by an average of 60%.
### Delivering world-class student experience
Scaling support does not have to mean sacrificing quality. The assistant integrates with the systems your team already runs and is tuned to your domain, so the answer your students get is your answer, in your voice.
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## Product Attribute Labelling
URL: https://www.elementx.ai/services/product-attribute-labelling
Type: Product · Attribute Labelling
**Revolutionise your product listings with automated attribute extraction.**
Product data your customers need is often scattered, inconsistently formatted, and sometimes entirely missing. We automatically extract, parse, and match product data to existing categories on your website, without the need for manual curation.
### Automated extraction
When your supplier does not share product data in the format you require, your team is left with missing information and a huge manual task. We automatically sort and categorise products into the correct categories, saving hours of manual labour and improving consistency. The system is adaptive, so changing formats are no longer a barrier.
### List at the speed of light
Loading products with complete information faster means the data your customers need to make purchasing decisions is right at their fingertips. Help your customers compare products without leaving your site.
---
# Customer stories
## Mott MacDonald: Employee Assistant for a Global Consultancy
URL: https://www.elementx.ai/customer-stories/every-mott-macdonald-answer
Published: 10 November 2025
Industry: Engineering & Infrastructure
Location: Worldwide
From Proof of Concept to 20,000 Users in Six Months
**Client**: Mott MacDonald, global engineering consultancy
**Challenge:** Connecting teams with dispersed expertise and knowledge across complex enterprise systems
**Solution:** Enterprise-aware AI agent with organisational intelligence and permission-native architecture
**Results:** full deployment in 6 months, 3x engagement increase, thousands of daily messages
[Mott MacDonald](https://www.mottmac.com/) is a global engineering, management, and development consultancy and one of the world's largest employee-owned companies. They deliver complex infrastructure projects that shape our daily lives - from buildings, water facilities and major transportation networks to sustainable energy systems and urban development.
Operating across such diverse sectors requires deep technical expertise, strict security protocols, and seamless collaboration between specialists worldwide. With teams spanning civil and structural engineers, transport planners, environmental consultants, and urban designers, Mott MacDonald's success depends on connecting the right people with the right information at precisely the right moment.
### The Situation: Navigating Complex Disparate Knowledge Bases
Mott MacDonald has some of the world's leading engineering expertise and is committed to continuously enhancing operational excellence across the organisation. As part of this, the organisation identified a key opportunity to improve access to critical enterprise information which was scattered across multiple enterprise systems - SharePoint libraries, project databases, staff directories, technical repositories, and regulatory documents.
Finding the right information often meant navigating complex internal systems or sending lengthy email chains to track down experts. A seemingly simple question like "What are the fire safety requirements for this commercial building project?" would take careful consideration to answer properly, requiring teams to search through multiple document libraries and then locate the right specialist to validate the approach.
For a consultancy where time directly impacts client delivery and project outcomes, this process was limiting efficiency. However, any solution needed to respect existing permission structures and safeguard sensitive infrastructure such as defence project data. Open AI solutions posed unacceptable security risks, while traditional enterprise AI tools lacked the ability to understand Mott MacDonald’s organisational context. Recognising this, senior leadership prioritised the need for a [Responsible AI](https://www.mottmac.com/en/insights/responsible-ai-is-key-to-infrastructure-delivery/) solution. One that could streamline knowledge access whilst maintaining security and context. This would empower teams to spend less time searching and more time applying their expertise to solve complex engineering challenges.
### The Solution: An AI That Understands How Organisations Actually Work
Rather than implementing another search tool, ElementX worked with Mott MacDonald to develop EMMA (Every Mott MacDonald Answer) - an AI assistant that understands not just what information exists, but how Mott MacDonald's organisation actually works.
#### **Organisational Intelligence**
Enterprise knowledge isn't just stored in documents - it lives within the relationships between projects, people, and processes. The assistant was designed to understand Mott MacDonald's internal terminology, team structures, and workflows, creating an AI that could navigate their organisational landscape just like an experienced employee would.
When someone asks EMMA for help finding expertise in fire safety requirements for commercial buildings, the answer goes beyond a basic summary. It:
- Pulls relevant standards and guidelines from technical libraries,
- Acknowledges this query will likely require the user to contact a specialist at Mott MacDonald with the appropriate expertise,
- Navigates to the correct directory in order to identify suitable fire engineering specialists and,
- Provides a list, including their relevant experience and current contact details.
#### **Technical Implementation**
The system integrates seamlessly with Mott MacDonald's existing Microsoft 365 environment:
- **Security Integration:** Respects all current permissions and security protocols.
- **Dynamic Access Control:** Every response is scoped to what each user is authorised to access in real time.
- **Multi-System Integration:** Connects SharePoint, staff directories, project databases, and CV libraries in real-time.
#### **Successful Change Management for Deployment at Scale**
Deploying AI across 20,000 global users is no small task, and critically, it requires that stakeholders be informed along each step of the journey in order to build their confidence. ElementX supported this process through a series of phases, carefully designed to support buy-in.
- **Phase 1:** Stakeholder alignment across platform, security, and compliance teams
- **Phase 2:** Sprint-based development with continuous feedback and testing in a limited, controlled environment
- **Phase 3:** Comprehensive capability transfer, transforming Mott MacDonald from AI users to AI innovators with the skills to continue building on their success.
### The Results: Lifting Productivity for 20,000 Employees
The impact exceeded everyone's expectations. EMMA went from proof of concept to full production deployment across Mott MacDonald's global workforce in under six months - a timeline most organisations consider impossible for enterprise AI projects.
#### **Adoption and Engagement Metrics**
The real success lies in how people are actually using the system:
- **40% staff adoption rate in 2 months** (~8,100 unique users across the global workforce)
- **600-700 regular daily active users** engaging with the platform
- **Thousands of daily messages** handled by the system
- **3-4 messages per session** average (up from just 1 in the initial version)
- **4x increase in engagement** from V1 to current version
The depth of engagement tells the real story. It indicates genuine behavioural change where people aren't just trying the system once, they're using it for complex problem-solving and collaborative work.
### **Client Feedback**
> _"EMMA is transforming how we access knowledge and information at Mott MacDonald. By providing faster and easier access to insights, EMMA enables our team to fully appreciate the value of data. It facilitates more efficient sharing of best practices, making Mott MacDonald a more connected and collaborative workplace."_
> _— Nasrine Tomasi, Head of AI, Mott MacDonald_
### A New Model for Enterprise AI Adoption
Mott MacDonald’s employee assistant demonstrates that successful enterprise AI isn't about replacing human expertise - it's about amplifying it. By understanding how organisations actually work and respecting existing structures while enabling new capabilities, AI can become a genuine productivity multiplier rather than just another tool to learn.
For Mott MacDonald, EMMA represents the foundation of their AI-enabled future. The success metrics show a workforce that has embraced AI as part of their daily practice, with engagement levels that continue to grow as teams discover new ways to leverage organisational intelligence.
_To learn more about implementing AI solutions that understand your organisation's unique context and requirements, contact the ElementX team at [enquiries@elementx.ai](mailto:enquiries@elementx.ai)_
---
## The Warehouse: AI gift recommendation engine
URL: https://www.elementx.ai/customer-stories/gift-recommendation-engine
Published: 10 November 2025
Industry: Retail
A customised AI gift recommendation engine that increased Christmas conversion for The Warehouse.
### **How a Customised AI Gift Recommendation Engine Increased Conversion over the Christmas Period for The Warehouse**
A household name for kiwi shoppers, The Warehouse Group is the largest retail group in New Zealand. The Warehouse has over 90 retail locations across New Zealand, and as part of an ongoing dedication to customer excellence, they are leading the charge to provide customers with an online shopping experience on par with their in-store experience
This dedication has encouraged The Warehouse Group’s digital team to explore the elements of customer experience that are more difficult to replicate online - things like browsing the aisles until the perfect Christmas gift that a customer has been searching for presents itself.
### **The Vision: An Effortless Gifting Experience**
The team at The Warehouse Group quickly identified an idea that could help create a version of this experience on their website. A recommendation engine - a tool that could be used to present gift ideas for different personas (for example, “gifts under $100 for a husband who loves the outdoors”) could be used to sort through the thousands of products listed on the Warehouse website so that shoppers could quickly hone in on a suitable gift. The team hoped that making this process as effortless as possible would yield greater conversion rates, and they were right!
### **The Problem: Thousands of Product Listings Require Categorisation**
The recommendation engine itself turned out to be the more straightforward portion of the equation. With thousands of products listed in The Warehouse Group’s various systems - many in different formats or with different feature listings - the idea of manually categorising and standardising listings was a herculean task, especially with the looming Christmas deadline approaching. The main hurdle that the ElementX team faced in implementing the gift finder was this categorisation.
### **The Solution: Natural Language Processing with GPT-3**
The ElementX team identified that a Natural Language Processing solution would be the most efficient way to read and categorise product descriptions, and after some testing and iteration, the team determined that a GPT-3 model would be best suited to the wide variations in language and structure present in the product descriptions. The team used this to classify products with a high degree of accuracy, focusing on specific product types that would be useful to include in the gift finder. GPT-3 is pre-trained, which means our team didn’t have to spend a lot of time in the setup phase with training data for the AI to understand how to categorise products. This, in turn, led to a faster result for The Warehouse Group’s team and meant that the Gift Finder was ready in time for Christmas!
### **Key Considerations for Implementation**
- Automate the process of categorising products to be included in the gift finder to remove the bottleneck of doing this manually.
- Build a recommendation engine that presents the newly-categorised products to customers searching for a specific type of gift-receiver.
- Create an impeccable user experience by designing for anticipated spikes in user activity during peak traffic periods.
- Include an analytical dashboard that shows key metrics such as conversion rates, site visitors, and overall performance, so these could be tracked and quantified.
### **The Solution: Driving conversion for online shoppers at The Warehouse**
The ElementX Team worked closely with The Warehouse Group’s digital team to identify the success criteria for this project, and after running a successful proof of concept of the gift finder for Mother’s day, the team set their sights on Christmas gifts. After using GPT-3 to categorise the products to be included, the team built the customer-facing gift-finder page on a robust, scalable foundation, using Google Cloud so that the result could accommodate large spikes in user traffic as was expected of the Christmas holidays. The ElementX team also built an analytics dashboard to analyse and report on customer’s activity with the finder, allowing The Warehouse Group’s digital team to quantify success and track customer behaviours within the gift finder.
### **The Results: Improved Conversion, Basket Size, and Session Duration**
Over the three months leading up to Christmas, The Warehouse Gift Finder saw over ten thousand new users to the Warehouse website, and visitors to Gift Finder pages stayed on the Warehouse website nearly three times longer than visitors to the rest of the site. The Gift Finder was the highest online source of revenue generation for The Warehouse Group during the Black Friday and Cyber Monday periods, with an average value of Gift Finder orders 28% higher than the sitewide average. It contributed to the joy of hundreds of Christmas mornings nationwide - and we couldn’t be more proud to have been a part of this project!
### **Meet the ElementX Team involved in this project**
Ming Cheuk, CTO: Ming designed the architecture of the solution, working closely with The Warehouse Group’s digital team to ensure that every eventuality was considered and that our build would be successful.
Dmitrii Goriunov, Senior Full Stack Engineer: Dmitrii built the backend of the gift finder, ensuring its stability and ability to handle large volumes of visitors. He was also responsible for connecting and training GPT-3 to categorise The Warehouse Group’s products for the gift finder so that customers could search the full range of products.
Aorangi Smith-Iri, Full Stack Engineer: Aorangi built the frontend interface of the Gift Finder, ensuring a smooth, effortless user experience for the Warehouse online shoppers.
Daniel Jimenez, Technical Product Manager: Daniel provided project management to keep the Gift Finder build process on-budget and on-schedule. He also provided quality control for the duration of the project.
Find the Gift Finder [here](https://www.thewarehouse.co.nz/gift-finder.html)
---
## Southern Cross Health Insurance
URL: https://www.elementx.ai/customer-stories/southern-cross-health-insurance
Published: 10 November 2025
Industry: Insurance
Location: Auckland
## **AI 360 Workshop Series Drives Progress at Southern Cross Insurance**
### **The Client**
**Southern Cross Health Insurance (Southern Cross)** has been supporting New Zealanders on their health journeys since 1961. Today, Southern Cross provides cover for one in five New Zealanders every year. As a not-for-profit friendly society, Southern Cross operates solely for the benefit of members, rather than shareholders or overseas owners.
Southern Cross has been a long-time partner and customer of ElementX. Throughout our multi-year partnership, ElementX has helped Southern Cross improve its contact centre, implemented conversational AI, performed user experience research to improve product understanding, and worked alongside its internal research team to improve members ability to lodge claims using Machine Learning.
### **The Situation**
The world of AI is rapidly developing. It seems every other day there is a new breakthrough, new process, new learnings from those who have tried to implement AI in their organisation, and new problems that have been solved using AI. Keeping up to date with the latest developments can be time consuming and confusing, particularly when much of the information available may be either buried in jargon, or not important to the business. However, failing to keep up may mean that the business misses out on valuable improvements that could benefit the organisation and its members dramatically.
### **The Solution**
ElementX worked with Southern Cross to create the “AI 360” programme. This programme delivered to key Southern Cross people; tailored consultations, workshops, and presentations focused on data and AI.
Rather than just provide a general overview of all things AI, the content and recommendations specifically focused on updating the Southern Cross team on key content that was relevant to their individual needs and goals as a company. Our long-term relationship with the Southern Cross team allowed us to accurately understand the needs of the business and deliver information that was of high value to them, so that they could skip past the time spent identifying and analysing potential solutions and applications of AI.
### **The Result**
Through regular presentations and consultation, teams and individuals working on AI and data were kept up to date on the latest advancements without having to spend time researching. As a result of this information, the team was able to stay informed and inspired to find new projects and solutions to the company's key focus areas.
By presenting the information at an “all-hands” meeting, staff from across Southern Cross were informed and inspired on how they can adopt the latest AI to improve their efficiency. Some solutions that were inspired by these workshops included the creation of an MLOps strategy to prepare for supporting machine learning in production, as well as identifying opportunities to enhance self-service for the IT helpdesk with a virtual assistant. These projects were later built and launched because of the spark initiated by these workshops.
The value of the workshops has resulted in an ongoing agreement with workshops delivered at a regular cadence for more than a year.
### **Stakeholders**
Key stakeholders in the AI 360 project include:
- Chief Digital Officer
- Head of Platforms and Innovation
- Data and ML Lead
- Architecture Team
- Digital Platforms
### **About the Southern Cross group**
The Southern Cross group is a collection of independent, health-oriented businesses in New Zealand, operates on not-for-profit principles and is New Zealand’s largest non-public healthcare organisation. The group’s businesses include health insurance, private hospitals, healthcare, travel insurance, pet insurance, life insurance and wellness. The group, not itself a legal entity, is made up of two organisations - the Southern Cross Medical Care Society and the Southern Cross Health Trust.
---
## Arcus Lending
URL: https://www.elementx.ai/customer-stories/worlds-first-mortgage-lending-digital-human-assistant
Published: 10 November 2025
Industry: Finance
Founded in 2008 by award-winning author and mortgage expert Shashank Shekhar, Arcus Lending is a US-based mortgage lending company that aims to provide the best, customer-tailored mortgages for clients across 19 states. It has become a leading mortgage firm, helping thousands of families secure better financing for their homes. In 2017, it was named on the Inc 500 List of Fastest Growing Companies. The company strongly values the importance of excellent customer service, advice and education surrounding home buying decisions; values which have played a key role in the growth of the company.
## The Challenge
Arcus Lending holds quality customer service at the core of what they do. As a fast growing company, Arcus was dealing with an increasing number of customer queries. They needed to find a way to **fast-track processing simple queries to serve more customers**, allowing their team of representatives to **focus on more complex questions and issues**.
Additionally, customers may have aversions to speaking directly to Customer Service representatives for fear of judgement when revealing private information or for other social/emotional reasons. Arcus needed a solution to address these challenges to **best serve their customers** and make them as comfortable as possible.
## The Solution
Arcus Lending wanted to ensure that their customer service is the **best in the mortgage lending industry**. As a result, they were determined to become the first firm in the mortgage industry to feature a digital human assistant.
With a digital human, the customer feels more comfortable asking any question without the fear of being judged, and can do so anytime 24/7.
Digital humans must contain the best Natural Language Processing capabilities to ensure clients are understood and serviced correctly without adding frustration. Our team at ElementX utilised UneeQ’s state of the art digital human platform alongside Google’s industry leading Natural Language Processing platform, Dialogflow, to provide a quality digital human experience on the Arcus Lending website.
## The Outcome
\- Arcus Lending’s digital human assistant, Rachel, was successfully integrated into the company’s website - a mortgage industry first!
\- Customers have 24/7 access to a mortgage agent, enabling them to learn about Arcus Lending’s mortgage plans and offerings instantly.
\- Thanks to Google’s Dialogflow, the chatbot effectively interprets the questions asked by potential clients, requests their email address to send them more information and increase conversion rates, and thanks to the “chit chat” module provides a chatty and quality customer service experience to customers.
\- The Digital Human is built in a way which can be easily expanded with additional features in the future as the platform grows.
> "'We set an ambitious goal with Rachel. We wanted to launch the mortgage industry’s first digital human, and we were working on an aggressive timeline of about 5-6 weeks. ElementX helped us meet this goal and they didn’t just deliver the project on time - they delivered efficiently and to the highest standard of quality. The team was communicative, friendly, and great to collaborate with!' - Shashank Shekhar, CEO & Founder, Arcus Lending" — Arcus Lending
---
## University of Waikato: Empowering Support Centre Staff with an Agile Virtual Assistant
URL: https://www.elementx.ai/customer-stories/university-of-waikato
Published: 22 October 2024
Industry: Education
Location: Hamilton, New Zealand
Empowering Support Centre Staff with an Agile Virtual Assistant
At ElementX, we embarked on an exciting collaboration with the University of Waikato to tackle the challenges faced by their busy Student Centre. The University of Waikato has campuses in Hamilton, Tauranga and China with a student population of over 13,000.
The university was keen to undertake a proof of concept to understand how Virtual Assistant technology would support the handling of the high volume of enquiries, both in person and via phone or email. It became clear that a localised fix wouldn't suffice, as other divisions within the university fielded their own questions through various channels.
The team sought innovative solutions to create a more agile and responsive support structure; one that boosted efficiency and enhanced the overall support experience for students. The partnership centered around the implementation of a powerful Virtual Assistant, leveraging advancements in new Generative AI technology.
Over the three months of the project we worked closely with the university to ensure a successful outcome. The deployment went smoothly: team members received comprehensive training to seamlessly collaborate with the Virtual Assistant, and a robust feedback mechanism was established to continuously enhance the Virtual Assistant's performance, ensuring its adaptability to the evolving needs of the university. These early stages of our partnership signaled a promising future, where technology and human expertise combined to elevate the support experience at the University of Waikato.
The Virtual Assistant we implemented is a conversational agent capable of drawing information from identified locations to construct answers to a myriad of student queries. This cutting-edge technology is equipped with Natural Language Processing, allowing it to integrate into the support structure and support the work of Student Centre staff. Not only is this Virtual Assistant effective in disseminating information, but it also possesses the ability to learn and evolve, thus improving the quality of the responses over time.
### **Considerations and Steps for a Successful Implementation**
##### **1\. Building a Knowledge Base:**
The first step in implementing the Virtual Assistant was establishing a robust knowledge base. This foundational element acts as the source from where our Virtual Assistant retrieves relevant information to provide accurate responses to user queries. To further provide users with accurate and up-to-date information, it's essential to crawl and integrate the relevant websites into the database. In the case of the University of Waikato, this involved crawling not only their own website but also external websites necessary for comprehensive support.
##### **2\. Deployment and Cloud Accessibility**
Deploying the Virtual Assistant solution in ElementX's secure cloud environment and making it readily available to the University of Waikato team was crucial for rapid adoption. By leveraging cloud deployment, they could benefit from scalability to adapt to growing demands, reliability for uninterrupted service, and accessibility; allowing support teams to access the Virtual Assistant from any location.
##### **3\. Comprehensive Training and Onboarding**
To maximise the effectiveness of an AI-powered Virtual Assistant, proper training and onboarding are paramount. In the case of the University of Waikato, ElementX worked closely with the Student Centre team to ensure they were equipped with the necessary knowledge and skills to effectively utilise the Virtual Assistant. This comprehensive training empowered the team to make the most of the technology, optimising support processes and improving the overall user experience.
##### **4\. Continuous Feedback and Training Data Analysis**
Collecting user feedback and training data is an ongoing process that plays a vital role in refining the Virtual Assistant's performance. By monitoring feedback, whether through the use of a thumbs-up/down button or other metrics, organisations can gather insights into user satisfaction and areas for improvement. This data-driven approach allows for continuous training and fine-tuning of the system, ensuring that the Virtual Assistant remains relevant and valuable to users.
> **"The ElementX team were great to work with and highly responsive. Feedback given was actioned immediately and the team could see progress immediately."**
> _Tricia Finn, Director of Student Systems and Administration_
### **Impressive Results**
****
**Improved Sustainability**
****Some Student Centres experience frequent turnover due to their student-held positions, necessitating the constant recruitment and training of new staff members, which can be costly and take up to 12 months to reach full operational capacity. The Virtual Assistant has the capacity to provide a cost-effective solution to preserve and expand institutional knowledge.
**Consistent Answers**
****The Virtual Assistant plays a vital role in ensuring consistency in responses. This consistency is due to its robust institutional knowledge base, and not only boosts the efficiency of the support system but also enhances the overall user experience by providing reliable and uniform information to all enquiries.
**Speed and Agility**
****At an organisational level, the Virtual Assistant demonstrated exceptional speed and agility. It swiftly collects and processes information from both internal and external websites, delivering prompt and accurate responses to student queries. This efficiency enhanced the Student Centre’s ability to handle many enquiries effectively.
**Powerful Examples**
The Virtual Assistant displayed its versatility by effectively addressing a wide range of both simple and complex queries from students. There is a case of the Virtual Assistant comprehending a graduation-related query, providing multiple answers based on website information alone. Another compelling example involves the Virtual Assistant answering a question about the University of Waikato’s Computer Science program and converting the response into an email that can be directly sent to the student.
### **Valuable Feedback for Further Enhancements**
Without feedback there would be no improvement and the Virtual Assistant garnered valuable insights from users. While the tool’s satisfaction rating set a strong foundation, we identified a need for stronger integration to provide more detailed, specific answers, which can be achieved through further iteration and development. Overall, the University of Waikato team expressed satisfaction with the productivity gains achieved and recognised the potential for further integration and features, including multilingual capabilities, additional domain knowledge and Tier 0 support.
---
## Waipapa Taumata Rau, University of Auckland
URL: https://www.elementx.ai/customer-stories/university-of-auckland-ai-chatbot-case-study
Published: 14 December 2022
Industry: Education
Location: Auckland, New Zealand
Waipapa Taumata Rau, University of Auckland is New Zealand’s largest university, with about 40,000 students passing through one of five campuses each year. With such a high volume of students navigating the complex world of tertiary education, alongside a longstanding dedication to student experience, managing student enquiries and responding quickly with the most helpful and accurate information is a critical priority.
At the start of the COVID-19 pandemic, another priority was recognised as well: as the university quickly pivoted to comply with lockdown restrictions and courses shifted to virtual learning, the Student Contact Centre team realised that the answers to student queries were changing daily - sometimes hourly - and providing the most up-to-date information was critical.
Conversational AI lays the groundwork for a powerful solution that addresses all of these concerns. There are a number of unique ways to implement a conversational AI solution - from digital humans and voice bots to agent-assist systems and chatbots. All sounded appealing to the University, so we worked alongside them to identify key priorities and determine how to focus our efforts for maximum impact, establishing a system that would allow us to build a strong foundation moving forward.
The University partnered with ElementX to help identify, scope, and implement a solution that best fit their requirements, taking into account the complex nature of student enquiries, integrating with a variety of existing systems, and providing the best possible experience for both students and Student Contact Centre staff.
### **Identifying Key Priorities to Build a Foundation for Success**
Scoping is one of the most important elements in a successful project - as ElementX is technology-agnostic, we begin with scoping sessions to determine what the best option is for a project, making no assumptions about how it might be built. We worked with the University - including staff members who were actively involved in answering student queries - to workshop the desired student experience and identify high-value improvements. We then created proof of concepts to test whether or not students would use these new features. This is a critical step for any successful AI implementation, as a complete understanding of how it will be used ensures there are no surprises.
While the university is ultimately planning to roll out a multichannel system that can interact with students in any way they choose, this agile process narrowed the focus to a list of critical priorities, from which we decided to move forward with a chatbot accessible through the University of Auckland website.
**Watch the Chatbot in action:**
### **Key Considerations for a Successful Chatbot Integration**
The following is a list of criteria that we identified as being critical for the success of the project:
- Improve average call handling time and time to resolution KPIs.
- Integrate with existing systems to answer questions that are specific to an individual student (i.e. “Can I use a calculator in my next exam?”).
- Resolve high-volume, repetitive questions first, before scaling to larger, more complex enquiries.
- Reduce the call centre load during peak times like exams, by providing an automated way to access answers to specific queries and allowing staff to more efficiently answer more complex questions.
- Ability to update the chatbot with changes to information internally by the University team members, without a developer.
### **IBM Watson Integration**
ElementX recommended implementing IBM Watson as the platform for the University’s chatbot. Watson is a question-answering system that uses Natural Language Understanding that learns over time as it is trained on more information. In a lot of ways, natural language understanding platforms can be quite similar and therefore difficult to choose between, but we recommended Watson in this instance because once it’s been set up, the system doesn’t require a developer to update information - when updates to the system need to be made due to changing COVID requirements or university policies, a team member from the Student Contact Centre can make these changes easily.
This feature was critical for the success of the program: staff members who, prior to the program’s implementation, had been answering phone calls and emails in the contact centre. These staff members were re-trained so that they could update the Watson system with answers to frequent questions, while still answering more complex queries. Now called content authors, they’re the perfect people to do this work as they’re already familiar with the questions being asked and the way these conversations usually flow.
Our conversational AI designers work alongside these team members to ensure they’re following best practice and to provide advice when needed, but since Watson doesn’t require a developer to code answers to questions, it’s a perfect opportunity to ensure that students get the best experience, and that these team members aren’t spending their days answering the same question again and again.
ElementX put together a team of experts to integrate with university systems and ensure that questions were answered succinctly and with the most applicable responses, and also to ensure a seamless integration with existing systems.
### **Measurable Results and Plans to Expand**
The University assistant currently helps answer a variety of universal queries such as “what are the requirements to attend the University of Auckland” and student-specific queries such as “When is my next exam?” so that Student Centre team members can resolve complex student queries more quickly. We use two metrics to monitor the success of the chatbot - Coverage (the percentage of queries that the chatbot has the answers to) and Containment (the percentage of people whose questions can be answered without being passed over to a human agent). Currently, both Coverage and Containment are averaging well above 90%. It also efficiently handles spikes in activity around expected times like exams and enrolment, and has been well received by the student population.
The University chose to stage the rollout of their chatbot with a minimum viable product (MVP) approach, which we commend as this makes it easier to measure the success of the program so that determinations can be made on which features to prioritise next. The project includes usage analytics so the team can tell what questions are being asked, what volume of queries the system receives, and how many of these it is able to manage on its own. These reports are shared with stakeholders regularly and used to plan improvements.
In the future, the team plans to roll out additional capabilities, expand to more channels, and continue to add more functionality to the tool. We look forward to a continued partnership with the University of Auckland team, supporting them in providing cutting-edge student experience using AI. You can visit the University of Auckland chat assistant live here.
### **Meet the ElementX Team Involved in This Project**
**Ming Cheuk:** Chief Technology Officer: Ming led the scoping portion of this project, ensuring that all options were considered and that the critical priorities were captured and satisfied.
**Dmitrii Goriunov:** Senior Full Stack Developer: Dmitrii managed the integrations with a wide variety of existing systems for the University, making sure they worked seamlessly with the Watson chatbot.
**George Qiao:** Full Stack AI Engineer: George led the development process from concept to execution, translating Ming’s scope to reality. He worked with Dmitrii on the required integrations and also on the conversation design aspects of the chatbot alongside Kevin. His contributions on both sides are invaluable.
**Kevin Adams:** Conversational AI Designer: Kevin ensured that the chatbot was created to answer questions in language students could understand, offering feedback and assistance to the content authors.
**Daniel Jimenez:** Engineering Manager: Daniel managed scheduling and timing and was the main point of contact between the University of Auckland team and ElementX. He ensured that the project was delivered as expected within planned deadlines.
**Tessa Phillips**: Senior AI Engineer: Tessa worked on the analytics systems to help the UoA team understand how students are using the chatbot and how it is performing so that improvements can be made and new features can be prioritised.
---
## Autogrow Systems
URL: https://www.elementx.ai/customer-stories/agtech-iot
Published: 14 October 2022
Industry: Agriculture
Development partner for Autogrow, an automated growing platform using IoT, data science and AI to increase plant yield.
Established in 1994, Autogrow provides intelligent hardware, software and data solutions for customers in over 40 countries from single compartment environments through to large-scale automated greenhouses.
With their team of horticulture technology experts, engineers and software designers, Autogrow is committed to creating the most innovative solutions in sustainable crop production.
> "“ElementX have been outstanding to work with and we’d definitely recommend the team. They delivered what we asked for and on time!" -Esteban Ruperti, Principal Engineer, Autogrow" — Autogrow Systems
---
## Peoply
URL: https://www.elementx.ai/customer-stories/education-platform
Published: 14 October 2022
Industry: Education
Peoply is a New Zealand company offering online programmes for children struggling in school, due to anxiety, lack of confidence, stress or simply learning alternatively than the norm. Designed to empower children aged 8 to 12 from the comfort of their own home, each class is facilitated by a coach with training in youth development and leadership.
> "“The ElementX team are creative problem solvers - always striving to overcome hurdles, and are proactive in ensuring that potential risks are mitigated before they arise. They are detailed oriented and thorough in ensuring that software developments and assets delivered are consistently excellent, while also providing strategically sound technical recommendations for our business” - Hannah Wrathall, Product Owner, Peoply" — Peoply
---
## Cove Insurance
URL: https://www.elementx.ai/customer-stories/cove-insurance-chatbot
Published: 14 October 2022
Industry: Insurance
Founded in 2017, Cove was created by a team passionate about fixing and building upon the future of insurance . They set to reimagine the traditional model of insurance, putting much of the responsibility and management in Kiwis hands. Cove empowers their customers with user-friendly, ‘self-serve’ digital insurance which can be completed through web, mobile and social channels. They are known for their simplified quote, registration and claims processes, as well as their responsive human-to-human customer service.
## The Challenge
The typical experience of an insurance policy holder involves filling out complicated forms, waiting to talk to someone over the phone at the most inconvenient of times, expensive premiums to pay for all of the overhead of a legacy IT infrastructure and the additional human effort support it.
Cove wanted to reimagine this model to simplify the experience of insurance by taking advantage of modern IT infrastructure, software automation and channels that the modern consumer is familiar with - web, mobile, social. The vision is to completely automate the journey all the way from purchase to claims, allowing customers to buy and manage their insurance policies wherever they are, 24/7.
## The Solution
Part of re-inventing the way Kiwis buy and manage their insurance involved exploring new channels. Social media is a big part of most people's lives these days, and was an obvious previously untapped channel.
ElementX helped Cove build their first digital employee - a Chatbot on Facebook Messenger that allowed people to buy and claim through a conversational interface alone. This guided prospects through the quotation process and allowed them to buy it straight away, without having to then go on the website or call someone.
> ""The team at ElementX have been instrumental in the development of Cove’s technology. We’ve found them to be incredibly bright, hard-working and efficient at getting the development work done to a very high standard" - Andy Coon, CEO, Cove Insurance" — Cove Insurance
---
## Southern Cross Health Society
URL: https://www.elementx.ai/customer-stories/insurance-digital-human-assistant
Published: 14 October 2022
Industry: Insurance
Southern Cross Health Society is New Zealand's leading health insurance provider, with over 870,000 members. Established in 1961, Southern Cross is one of the country’s most well-known and trusted brands. Consistently ranked among the best in Australasia, the Society was awarded Canstar's Most Satisfied Customers Award in Health Insurance, from 2016 to 2019.
## The Challenge
Aimee is more than just one of the most relatable digital humans created; she has the intelligence to process and interpret what is said to her and respond accordingly. ElementX was brought on to help implement the layer of intelligence and personality she possesses.
## The Solution
Aimee is built on UneeQ’s digital human platform using recent advances in machine learning and natural language processing. She is trained to recognise the most popular questions that are asked and give an appropriate answer.
The delivered solution was more than just the NLP component - we helped create a production-ready cloud infrastructure to support the load and reliability requirements. ElementX played a vital part in the overall integration of UneeQ with Southern Cross systems.
## The Outcome
- First health insurer in NZ to have a Digital Human
- 7000 conversations in 3 months
- 95% customer satisfaction
> "“The ElementX team were an invaluable part of Aimee’s success. Their expert guidance and can-do attitude, with fast implementation of smart solutions to any problems the project team encountered, make them one of the best business partners I’ve ever worked with.” - Ella Ryborz, Product Owner, Southern Cross Health Society" — Southern Cross Health Society
---
## The Warehouse: Product attribute labelling automation
URL: https://www.elementx.ai/customer-stories/product-labelling-automation
Published: 14 October 2022
Industry: Retail
Automated product attribute labelling that enabled The Warehouse to launch a "shop by age" experience for toys without manual data classification.
'We wanted to meet the customer experience expectation of being able to shop our Toys range based on what might be the most age-appropriate options. Our blocker was the fact that we did not have the age classification product data to enable that experience. ElementX helped us solve that issue quickly and efficiently, working with our current assets, sourcing data, feeding information back to us in order to enrich our product content, and bring to life the planned experience. The opportunity to “shop by age” was then our most engaged with wayfinding unit during our Big Toy Month campaign.' - Bonnie Bradley, Head of eCommerce Optimization, The Warehouse
Since 1982, The Warehouse has grown to be New Zealand's largest general merchandise retailer. The Warehouse is a Kiwi household name, with over 90 iconic 'big red sheds' around the country. They are part of The Warehouse Group, which manages market-leading retail brands including Warehouse Stationery, Noel Leeming, Torpedo7, 1-day and most recently TheMarket.
## The Challenge
The Warehouse manages one the largest retail inventories in New Zealand. Correctly labelling products with metadata is important as it enables personalised customer experiences and improves product recommendations on The Warehouse’s eCommerce store. However, the quality of data from suppliers can vary drastically, resulting in many missing labels and metadata for the categorisation and sorting of products.
###### Key challenges presented to us:
The retailer needed to keep track of DVD classifications, particularly whether DVDs had been correctly labelled or were missing the necessary age classifications. This tedious process was done manually, which involves an individual going through thousands of images and checking each DVD. The accuracy of this metadata is especially imperative, due to the prohibitions related to the distribution of age restricted media to underaged customers.
Another challenge was categorising toy products by age appropriateness. Many parents may not know what toys are appropriate for their kids, thereby relying on recommendations from retailers. The problem is most manufacturers do not specify age data, and is too costly for a staff to do this completely manually, which often involves a lot of guesswork.
## The Solution
The data available was in an unstructured format, which is traditionally difficult for machines to process. There was an opportunity to leverage recent AI developments in order to automate the labelling process.
For the DVD classification task, we made use of [Computer Vision](https://cloud.google.com/vision) to detect product labels and automatically classify the ratings of DVD titles. This makes checking a database of thousands of DVDs a matter of minutes in the background.
To sort the toys, we used Natural Language Processing to understand the description and predict the age range based on previously labelled data. This provides a scalable way for The Warehouse to continuously sort new toys into relevant categories.
## The Outcome
\- Most engaged wayfinding unit, during Big Toy Month campaign
\- Provided customers the ability to shop for toys by age
> " " — The Warehouse
---
## vWork
URL: https://www.elementx.ai/customer-stories/ai-document-extraction
Published: 14 October 2022
Industry: Other
vWork is a job scheduling and dispatch software. vWork aims to reduce the cost of managing field jobs for mid-sized businesses with complex or high-volume workflows. The software is used by many organisations, including Toll Group, Foodstuffs and Rockgas.
## The Challenge
vWork customers process large quantities of work orders requesting or reporting on jobs which must be added to the platform. These are mainly in the form of PDF email attachments and vary significantly from client to client, and sometimes from job to job. These work orders were manually entered into the system - a time-consuming process.
vWork was interested in exploring potential solutions to automating this manual task to increase their ability to scale and add more value to the platform for the customers. ElementX was engaged to conduct exploratory research and report delivery to assess how this could be achieved.
## The Solution
We presented a report identifying and comparing potential solutions, ranging from completely custom options to fully managed 3rd party platform. These solutions had different tradeoffs for vWork’s use case, mainly coming down to quality and ease of implementation for the managed solutions, and improved customisation and price for the more custom solutions. Solutions explored ranged from SaaS to fully managed turn-key solutions from Xtracta, Docparser, AWS and Azure.
## The Outcome
A wide range of potential solutions for document extraction were investigated to find those which showed the most promise for vWork’s specific use case and considerations. The tradeoffs, pricing models, recommendations, and implementation plan were presented, which allowed vWork to proceed with the next steps in creating a proof of concept integrated with their backend.
---
## iMonitor
URL: https://www.elementx.ai/customer-stories/imonitor-helpdesk-automation
Published: 14 October 2022
Industry: Food safety
iMonitor is NZ’s only commercially available, fully integrated food safety compliance and monitoring platform. The platform features real-time temperature monitoring and a Food Safety Plan that is based on Ministry of Primary Industry (MPI) Food Control Plan regulations to enable the commercial food industry, including restaurants and supermarkets, to more easily monitor and regulate their food safety.
## The Challenge
iMonitor has a focus on great customer experience, and as a result has been very hands on with the onboarding process, requiring many phone calls and in person site visits.
iMonitor needed a scalable system to support their growing customer base and assist with the onboarding process and other system support enquiries.
## The Solution
To help iMonitor better serve their customers, ElementX integrated the helpdesk solution Intercom, which has several features that help them automate and scale their customer support capabilities. This includes a chatbot (Resolution Bot & Custom Bot), live agent assist, and also cross-channel messaging automation capabilities (Series).
## The Outcome
\- Decreased customer set-up processing time
\- Effective and efficient communication with customer, via targeted messaging
\- Without the system, iMonitor would have lost ~35 customers within the first 3 months
\- Data insights collected from the chatbot platform, help iMonitor make better informed decisions regarding their customer support
> "“There are so many different ways of doing chatbots - it’s a real minefield. ElementX came over, listened to us and helped us identify the best platform to use.” - Shakeel Ahmed, Food Scientist, iMonitor Ltd" — iMonitor
---
## iSell
URL: https://www.elementx.ai/customer-stories/retail-product-categorisation-automation
Published: 14 September 2022
Industry: Retail
Established in 1998, iSell is a global leader in IT lifecycle management. Their award winning Cloud-based software quoting platform ITQuoter helps MSPs and VARs automate quoting and procurement. They operate a web-based software service called IT Quoter, which contains a catalogue of over 5,000,000 products and allows companies to easily build and receive quotes on large quantities of IT products and software solutions.
## The Challenge
IT Quoter has a catalogue of more than 5,000,000 products, with additional products regularly added in the fast-paced world of tech. In order to add these new products to their system, they must first be tagged and categorised into the correct product categories using information provided by the manufacturer. Unfortunately, due a lack of consistent industry standards, the information provided by the manufacturer is often incorrect or of poor quality.
In order to overcome the challenge of inconsistent data quality offered by the manufacturer, iSell has a team of product specialists that have to manually categorise these products into the right categories. Due to the complex nature of this task, some products may take up to 30 minutes to categorise. In an effort to speed up the process, a machine learning model was requested to augment iSell’s expert team to increase the speed of product categorisation.
## The Solution
Given iSell’s large catalogue of products, we developed a custom Machine Learning model to classify the products automatically based on features such as product name and description.
The machine learning model was trained on over 1,000,000 products and hosted on the [Google Cloud platform](https://cloud.google.com/automl). This scalable architecture is capable of handling all types of product data. We also developed a custom data pipeline which makes use of big data and machine learning products offered by Google Cloud to manage the data-preprocessing, model training and deployment.
This unique approach frees up iSell’s expert team to focus on the most complex products.
## The Outcome
\- The AI model has classified over 116,000 unlabelled products into over 1250 categories with comparable accuracy to human labelling.
\- The AI model is able to categorise millions of new products within minutes, saving hours of manual time and cost.
\- The AI model can classify new products on-demand or as a scheduled batch job, and can constantly be updated with new product data.
\- The result of this AI model has helped iSell save hours of manual categorisation and allows their team to focus on the most difficult products to categorise.
\- As the AI model can continuously learn and improve with new products fed through the model, the accuracy of the product categorisation increases over time.
---
## UVLens® Sun Safety App
URL: https://www.elementx.ai/customer-stories/uvlens-sun-safety
Published: 13 September 2022
Industry: Consumer
UVLens is the number 1 rated sun safety app on Android and iOS. UVLens shows the danger level of ultraviolet (UV) rays throughout the day and provides personalised recommendations based on the user’s skin type. - 500k+ worldwide users
\- Featured by Apple App Store
\- Editors Choice on Google Play Store
---
# Blog
## To one-shot or not? Building production systems with coding agents
URL: https://www.elementx.ai/blog/building-production-systems-with-coding-agents
Published: 3 September 2026
**One-shotting an application with a coding agent is great for prototypes and can be a disaster for production. A five-step hybrid keeps the merit and drops the risk.**
You may have heard of one-shotting an entire application with a coding agent: you hand it the full brief, walk away, and come back to a working system generated in a single pass. It does work, and there is real merit in it. The agent has the complete picture from the start, so every component is built knowing about every other, the structure is coherent, and nothing gets bolted on later that the original design didn't anticipate. For a prototype or a demo, something you want in front of stakeholders by Thursday, it's hard to beat.
For anything heading to production it can be a disaster. A single enormous change is hard to test as you go, hard to review, and hard to unpick when the agent made a quietly wrong assumption early on that everything afterwards depends on. The tests, if there are any, were written by the same process that wrote the bugs, and the first time a human properly understands the system is when it fails in front of a user.
The approach that has worked well for us keeps the merit and drops the risk. It's a hybrid: capture the full requirements up front and let the agent consider all of it at once, the way a one-shot would, but only ask it to one-shot the plan and the foundations. The features then get built incrementally on a skeleton that already anticipates them. Most of the iteration moves earlier: you argue over the architecture, the features, and the build order while they are still words in a document, when changing your mind costs nothing, rather than after the agent has poured code around a decision nobody quite made.
If that sounds to you like a quiet return to waterfall, you're not the first to say so, and I've answered that at the end. The five steps come first.
## 1. Write the decisions down before anyone writes code
Every technical decision, spec, and constraint goes into the repo as documentation, and everyone who has a stake in the system agrees to it before a line of code exists. That means product, architecture, security and compliance, and the developers who will live with the result, all signing off on the same set of documents.
Nobody should sit down to write these documents from scratch. Have the conversations you would have had anyway, summarise them into a document everyone can agree to, and deposit that in the repo. Meetings are good at surfacing disagreement; documents are good at recording what was resolved.
Talking isn't the only way to gather requirements, either. When the group can't agree on what a feature should do, a one-shot prototype earns its keep. Have the agent generate a rough version in an afternoon, put it in front of the people who will use it, and watch what they do with it. You learn more from ten minutes of someone clicking around than from an hour of them describing what they think they want. The rule is that the prototype is disposable: it exists to sharpen the document, nothing gets built on top of it, and that's what lets you keep it quick and dirty.
Expect this step to take several rounds. Someone will notice the data model can't support a feature two pages later, or that two teams have assumed different owners for the same integration. Every one of those arguments is cheap to resolve in a document and expensive to resolve in code the agent has already generated around the wrong answer.
An agent works from whatever context it can see. If the architectural intent lives in someone's head, or in a Teams thread from March, the agent will invent its own, and it will be internally consistent and confidently wrong.
## 2. Generate the implementation plan from the documentation, and agree that too
With the documentation in the repo, ask the agent to produce an implementation plan from it: what gets built, in what order, and how the pieces depend on each other. Then put that plan through the same agreement process. The build approach deserves as much scrutiny as the requirements did.
A useful side effect is that the plan is already most of your backlog. We've generated the tickets straight from it into the team's backlog tool, which saves a planning session and keeps the tickets tied to the documented intent rather than to whoever was typing fastest.
## 3. Scaffold the whole repo in one pass, without building every feature
This is the part that earns the "one shot" label. With the full spec and plan in view, have the agent scaffold the entire repository and every component the system will eventually need: libraries, dependencies, database schemas, service boundaries, configuration. The goal is a structure that can carry every feature in the spec without a major refactor later, even though most of those features don't exist yet.
A capable coding agent will recommend this anyway; it doesn't want to build everything in one session either, since it can't test as it goes. What it can do well is set up the shape of the thing when it can see the whole picture.
Three things belong in the scaffold that teams often defer:
- **Test structure, including frontend tests.** When an agent can run tests and see them fail, it checks its own work and fixes the failure before you ever see it, instead of handing back code that looks finished and isn't.
- **CI/CD from day one.** Linting, tests, versioning, and deployment pipelines all set up before the first feature. Every later change then arrives with a verdict attached.
- **Observability from day one.** Logging, tracing, and error reporting wired in at the foundation, so you're never retrofitting instrumentation onto a system that's already misbehaving.
## 4. Deploy the empty scaffold before you build anything on it
Get the scaffold into a test environment and confirm every part is talking to every other part: the frontend reaches the API, the API reaches the database, the pipelines run, the logs arrive. Deploying an empty system feels like a strange thing to celebrate, but it flushes out the integration and environment problems while they are cheap, rather than in week six when they're tangled up with real features.
Where you can, give the coding agent access to the logs in that test environment. An agent that can deploy a change, read the resulting error, and try again closes the loop on its own. One that has to wait for a human to paste in a stack trace is working at human speed.
None of this is new. It's what a careful team did before coding agents existed. The change is that the time cost has collapsed, so the excuse for skipping it has gone.
## 5. Build the features, one at a time, as usual
With the foundations deployed and verified, the rest is ordinary feature work. The agent builds against a structure that already anticipates each feature, tests it against a harness that already exists, and deploys it through a pipeline that already runs.
## Expect bigger pull requests, and review them differently
One side effect shows up quickly once the features start landing: pull requests get a lot bigger. An agent can implement an entire feature in one go, touching the database, the API, the frontend, and the tests together, where a person would have delivered it in three or four smaller changes. There's an upside to that: the history stays clean, since each merge is a complete feature rather than a fragment. The downside is that a change that size is hard to undo by hand if something goes wrong.
It's also more than any reviewer can read line by line, which is why the test structure and pipelines from the scaffolding step matter so much. They become the first reviewer, and they don't get tired on the four-hundredth line. My own review of a large pull request has changed as a result. Rather than reading it top to bottom, I point an agent at it and interrogate it about the things I'm suspicious of: how does this handle a failed write halfway through, where does the permission check happen. The agent has read every line; I've read the parts that carry risk.
## Are coding agents pushing us back to waterfall?
On paper this looks suspiciously like the thing the industry spent twenty years getting away from: agree the requirements, write the architecture down, get sign-off before anyone builds. A project manager from 2004 would recognise the plan. It isn't waterfall, though. Waterfall's problem was never that it planned; it was that the plan was expensive to produce, expensive to change, and the first real feedback arrived months later when the system met its users. Agile shortened that loop so you could learn early and cheaply, and nothing here gives that up.
The rhythm is the same: plan, build, verify, learn, repeat. What changes is the shape of each pass and what you expect to come out of it. The first iteration used to be a thin vertical slice through the stack and now it can be the entire deployed foundation; an iteration that used to deliver a single story can deliver a whole feature with its tests and pipeline attached. The design phase gets iterations of its own as well, on documents rather than code; that is where the early agreement gets built.
One caveat. This approach assumes the requirements are largely knowable up front, which is true of most enterprise builds where the domain, the integrations, and the constraints are already understood. On a product still in discovery, where nobody yet knows what users want, the spec agreed in week one will be wrong in ways no architecture can absorb. That's where the one-shot prototype comes back into its own: generate it, test the concept with stakeholders, throw it away, and repeat until the picture is clear enough to write down. Discovery becomes a series of disposable one-shots feeding the document, and the build starts once it holds.
## Closing thoughts
The answer to the title, then, is: one-shot the prototypes and the foundations, never the production application. The plan and the skeleton get created in one go, with every factor considered at once, and the system gets built layer by layer from there.
What has shifted most is where the humans spend their effort. Nearly everything in the five steps is about setting up the conditions for the agent to succeed: the documented decisions it works from, the structure it builds into, the tests that tell it when it's wrong, the pipelines and logs that close the loop. The job has become building the factory rather than handmaking the product, and the teams getting the most out of coding agents are the ones who noticed that early and put their best people on the factory.
---
## Getting the best value from your AI token spend
URL: https://www.elementx.ai/blog/getting-the-best-value-from-your-ai-token-spend
Published: 20 August 2026
**AI token costs are climbing and spending caps are tempting. Five levers cut your cost per completed task before you start rationing.**
import { Image } from "astro:assets";
import usageReport from "../../assets/blog/token-spend-july-2026-usage-report.png";
For the last couple of years, organisations have been encouraging their people to use AI: find the productivity gains, get rid of the drudge work, experiment. It worked. People have discovered what agents can do, and in some places it's working almost too well. Token costs are ballooning, finance teams are asking questions, and some organisations are reversing course with hard spending caps.
Sometimes a cap is the right call; it brings spend under control without anyone noticing a difference. Other times it's a real handbrake on work that was paying for itself. Before reaching for the cap, it's worth checking whether you're paying more per token than you need to. In our experience, five levers make the biggest difference.
## Use subscription plans wherever you're eligible
If your organisation qualifies for a subscription plan like Claude Team or ChatGPT Business, use it. The economics are hard to argue with: for the same volume of tokens, a subscription typically works out something like 6 to 10 times cheaper than metered API usage, and for heavy users the gap can be far wider. Looking at my own usage last month, the tokens I consumed would have cost around US$1,800 at API rates. My subscription cost US$100.
For your serious users, you'll want Premium seats. They're available now on [Claude Team](https://support.claude.com/en/articles/9266767-what-is-the-team-plan) and [rolling out on ChatGPT Business](https://openai.com/index/premium-seats-chatgpt-business/), and at around US$100 to $150 a month they're far better value than a $20 standard seat plus overage billed at API rates. The useful part is that you can mix seat types within one plan: a handful of Premium seats for your heaviest users, standard seats for everyone else, and you adjust as usage patterns become clear. That makes it a low-barrier model to adopt.
One caveat: these plans have seat limits. Claude Team, for example, tops out at 150 seats before you're moved onto Enterprise, where usage beyond your allocation is typically billed at standard API rates. If you're a large enterprise above those thresholds, you won't be able to take full advantage of the subsidy built into the subscription tiers, and the efficiency levers below matter even more.
## Match the model to the task, not the price per token
Another tempting lever is dropping to a smaller model with a lower price per token. The trouble is that your bill is driven by cost per completed task, and the two can point in opposite directions. A smaller model often takes more turns to get there, retries more failed approaches, and sometimes fails the task outright; a failed run costs you every token it burned, plus the rerun, plus someone's time sorting it out. This is well documented: [Artificial Analysis](https://artificialanalysis.ai/agents/coding-agents) benchmarks agents on cost per completed task for exactly this reason, and the Allen Institute for AI's [AstaBench study](https://arxiv.org/abs/2510.21652) found that agents built on models priced 3 to 25 times cheaper per token ended up roughly twice as expensive per task, taking more steps and getting stuck in loops, while also performing worse. Match the model to the task, then measure what a finished task costs you, rather than what the pricing page says.
## Put what you know into skills and context files
Day to day, the biggest driver of token spend isn't the tasks your agents complete; it's everything they figure out along the way. An agent without good instructions will rediscover your conventions from scratch every session: probing your systems, hitting an error, reading the error, trying again. Each of those loops burns tokens, and they compound across every person and every task.
The fix is to encapsulate what you know into skills and context files: how your systems work, what the conventions are, which approaches to use and which to avoid. The more you write down once, the less your agent has to work out on the spot, every time. Of everything on this list, this is the change we see move the needle most.
## If a task can be scripted, get the agent to write the script
When an agent operates APIs directly, it reasons through every call: read the response, decide the next step, handle whatever comes back. For a one-off task that's fine. For a task you run weekly, it means paying full reasoning cost on something that stopped needing reasoning after the first run.
Instead, have the agent write a script the first time, then run the script from then on. The thinking happens once; the repetition is nearly free. It's a small habit change that turns your most repetitive workflows into your cheapest ones.
## Automating a browser? Prefer DOM navigation over screenshots
If your automation needs a browser, how the agent "sees" the page matters a lot. Screenshot-based approaches, common in browser extensions, send images into the model's context and rely on vision to work out what to click, which is expensive and imprecise. Tools like Playwright instead hand the agent a structured text snapshot of the page: [a few hundred tokens per page rather than thousands](https://playwright.dev/mcp/introduction), with every element carrying a stable reference the agent can act on deterministically. Same task, a fraction of the tokens, and fewer failed attempts to pay for on top.
## Caps treat the symptom; efficiency treats the cause
A spending cap tells your people to use AI less. The levers above let them use it just as much, for a fraction of the cost: the right plan, the right model for the task, well-built skills and context, scripts for the repeatable work, and efficient browser automation. If your token bill is climbing, start there before you start rationing.
---
## Notes and takeaways from the Claude Meetup Aotearoa
URL: https://www.elementx.ai/blog/claude-meetup-aotearoa-july-2026
Published: 3 July 2026
**Notes from the Auckland Claude meetup: AI adoption at airline scale, governance as an accelerator, agents as teammates, and who the future gets built for. With commentary from the ElementX crew.**
import TeamNote from "../../components/TeamNote.astro";
If you couldn't make the July Claude meetup in Auckland, or you were there and want the takeaways in one place, these notes are for you. Four talks and a panel covered the full arc of where enterprise AI is landing right now: rolling AI out across an entire airline, turning a two-week legal bottleneck into a two-minute self-serve workflow, agents starting to work as colleagues rather than tools, and a historian's view of who this technology will serve. Simon Conroy kept the evening on the rails as MC.
We've kept the notes faithful to what each speaker said, and added our own commentary throughout; look for the "Our take" blocks.
## Mike Parsons: rolling AI out to an entire airline
[Mike](https://www.linkedin.com/in/mikeairnz/) leads enterprise AI adoption within Air New Zealand's digital organisation, where he has taken AI tooling from early proofs of concept to more than 3,000 licensed users, all under the governance demands of government-owned, nationally critical infrastructure. Outside of work he's a published science-fiction novelist (six of them), a former Canadian junior chess champion, and currently building a chess-career simulation game called _Ultimate Grandmaster_.
- **Capability beats integrations.** His staff chose OpenAI Codex over Copilot, despite Copilot's deep Microsoft 365 integrations. Counterintuitive, but raw model capability won out over integration breadth.
- **AI belongs to everyone.** Rather than rationing licences by department ("are we the guys in the '90s deciding who gets email?"), they rolled it out to all knowledge workers. Learning loops should be fast _and_ distributed across the organisation, not concentrated in one team. And be transparent about what you try and what you learn.
- **Governance is the enabler, not the blocker.** Air New Zealand is government-owned, nationally critical infrastructure and, in Mike's words, "the safest airline in the world", so governance rests on four legs: privacy, legal, cybersecurity and data science. That last one covers algorithmic bias, which matters acutely in multicultural New Zealand, especially in areas like recruitment. Build safe guardrails, then say "go for it".
- **A permissive, celebratory culture.** Never shame AI use; celebrate "that used to take twelve hours and took you two". Weekly "what did _you_ do with AI this week?" sessions draw around 1,000 followers, and they're deliberately fronted by non-technical people from finance or maintenance so AI isn't seen as owned by digital.
- **Real adoption versus passive resistance.** Usage sits around 85%, but resisters don't refuse; they log one trivial query a week ("what time was the All Blacks game?") so the metrics look fine while output stays low. The metric that matters is how fast you move someone from "this costs me time" to "this saves me time".
- **The fastest way to convert a sceptic is a story from their own world.** Aircraft parts go by different names across suppliers, and the global supply chain is in rough shape, so Air New Zealand's supply chain team would routinely lose hours chasing a single part. One of them tried AI instead and found the part in five minutes; when teammates asked why they looked so pleased with themselves, the disbelief ("it always takes hours and hours") converted the whole team overnight, because the example spoke their language. Mike says moving someone from "this will cost me time" to "this will save me time" used to take him half an hour of persuasion; with the right story it now takes under five minutes. And once people are in credit on time, encourage them to reinvest it in learning more; every workflow you solve keeps paying you back.
- **Reframe risk for risk-averse organisations.** Aviation already assumes humans make mistakes and builds compensating controls; identifying risk is supposed to be these teams' superpower. Ask "where is it appropriate, and with what controls?" instead of saying no. His personal version: a burner laptop with a $500-limit card for trying dodgy AI services. Contain the blast radius.
- **The three-layer cake.** AI for individuals gets near-total freedom: duplication is fine, the cost is low and the learning is high. Big rocks get significant effort and stronger governance. Agents sit at "cautious curiosity": sandboxing, permissioning, and the open question of agent identity. An agent inheriting a person's permissions and going rogue "can't happen".
- **Tokenomics: four control levers.** Per-person token limits (easiest, but blunt: the best users are defined by mindset, not role). Workload routing (attractive in theory, hard and invisible in practice, though probably where things are heading). Building a harness (his strong recommendation: "same quality at 50% of the tokens", or his medium-reasoning harness that approximates extra-high). And model choice (policy alone fails; it's an education problem, and people leave everything on the highest settings).
The framing that stuck with me was how Mike manages the risk that comes with
agents, because it's so much more useful than "don't use it". Aviation
assumes humans make mistakes, and the whole system is designed around that
point so that when someone does, nothing catastrophic happens. Apply the same
principle to agents: assume they'll make mistakes, design the compensating
controls around them, and you have a credible path to adopting them safely in
business-critical functions.
The supply chain worker anecdote was such a good illustration of this point.
Sourcing the aircraft part is a task so niche that nobody outside supply
chain would even realise it could take hours of work, let alone be something
AI could touch. That's what makes it so perfect: it wasn't a generic win
someone had sold him on, it was his own problem, the specific thing that ate
up his day, solved in front of him. And because it was his, he got excited,
and he went and told everyone. That natural enthusiasm of discovery is a
much better conversion tool than a carefully designed rollout plan.
## Natalie Kim: NDA review, from two weeks to two minutes
[Natalie](https://www.linkedin.com/in/nekim1/) is the founder of Inflection Group, an AI strategy and governance advisory, and a strategic advisor to [Anthropic](https://www.anthropic.com/) who helped build Claude for Legal. A Harvard Law JD, she spent eighteen years in the US building a career across tech and legal in the Bay Area and Seattle, most recently as general counsel at a venture-backed cleantech company, where she also led company-wide AI transformation alongside the CEO. She's now returned home to New Zealand after what she calls "the world's longest OE".
> "AI transformation cannot stop at doing the existing work faster; it needs to reimagine the work."
- **Everyone feels behind, even the Bay Area.** Her worked example: taking NDA review turnaround from two weeks to two minutes.
- **The problem: a templatised document stuck behind a human bottleneck.** NDAs are about as standard as legal agreements get, yet everyone insists on their own editorial tweaks ("lawyers are very attached to their own specific words"), so each one still gets reviewed manually by a lawyer, who usually has twelve fires to fight before reaching your NDA. Fires don't wait in line. At her company, where she was solo counsel, that meant two-week turnarounds for what is mostly boilerplate. Her fix came in three versions.
- **V1, the generic prompt, looks impressive and is "completely useless".** It knows nothing about your company, context or risk posture, and ten runs give ten different answers. People who stop here wrongly conclude AI isn't ready. That's _using_ AI, not _building with_ AI.
- **V2, a customised skill,** encoding sixteen parameters of how she thinks about NDA risk, cut her personal review from twenty minutes to two. But turnaround stayed at two weeks, because only she could run it. Speeding up the bottleneck isn't transformation.
- **V3: governance is the accelerator.** People hesitate to delegate to AI because they don't trust it; governance is how trust gets built (even if she had to rebrand her committee "AI Innovation and Oversight" to keep the board awake). Her skill-design pillars: **identity** (what you are, and what you're never allowed to pretend to be), **hard stops** (explicitly scope what it must _never_ do, so chained agents don't trample each other), **sign-off thresholds** (what sails through versus what needs a human), and **graceful degradation** (when it's out of its depth, hand off with context so the human doesn't start from scratch).
- **The result:** sales self-serves, negotiating and signing their own NDAs, with output written for a salesperson audience and legal pulled in only when it's needed. The pattern replicated across legal, marketing and finance.
- **You don't need to be technical.** "If you can use a Word document, you can use a markdown file." What you do need: the ins and outs of your company and its culture, plus the subject-matter experts closest to the workflow. It's unglamorous process-improvement work; the alternative "looks like it works" while quietly accruing governance debt.
- **Make space for the slowdown before the leap.** Her skill took four hours to build but two weeks of monitored piloting. Leaders must give teams room to change how they work on top of their existing workload, and individual contributors shouldn't make their acceleration "somebody else's problem tomorrow".
What I really liked here is that Natalie institutionalised specialist
knowledge. Her legal expertise is encoded once and scaled to the whole
company, so staff can do what they need to do much quicker without taking on
new, unnecessary risk. And the design is close to failsafe: hard stops,
sign-off thresholds and graceful degradation mean the skill knows what it's
allowed to do, what can sail through, and when to hand back to a human with
context. That's expert judgment built into the workflow, not bolted on
afterwards.
Natalie and Mike arrived at the same governance conclusion from opposite
ends of the room. Natalie put it as "governance is the accelerator": you
delegate to AI once you trust it, and governance is the foundation that
trust is built on. Mike's aviation perspective was that in a world where the
assumption is that a human will make a mistake, you design your system to
include the redundancies required to catch it before it matters. Same move,
applied to AI: assume it'll get things wrong, build the controls that
contain the damage, and then set it free within that safe boundary.
## Adam Holt and Jake McInteer: agents joining the team
[Adam](https://www.linkedin.com/in/adamjohnholt/) is New Zealand's Claude Ambassador, a serial builder across several businesses, and the driving force behind the local Claude community and the [CoLab](https://thecolab.ai/) group; he hasn't written a line of code by hand in eight months, and recently returned from the developer conference in Tokyo even more bullish. [Jake](https://www.linkedin.com/in/mcinteerj/) joined Anthropic in Sydney roughly seven weeks before the meetup, after a stint at MongoDB where he ran the developer meetups that first put Adam on stage. The two have spent months experimenting with multi-agent collaboration, including their WhatsApp agents "Kev" (a shark) and "Keith" (a goose).
> "Don't build for the model you've got today; build for the model that's going to be there in six months."
- **Capability is compounding faster than org charts can adapt.** And the progress chart is logarithmic, which makes it scarier than it looks. There's no sign of it slowing down: Fable 5 and Mythos 5 landed while the slides were being written.
- **Don't take a point-in-time view of AI** (Jake). When AI fails at something today, stash the failure; it's the perfect test case for the next model release. Look at the direction of the line, not the current dot.
- **The Bun rewrite story.** [Bun](https://bun.sh/) creator Jarred Sumner rebuilt what he estimated as a full team-year of work in eleven days, solo, using new workflow features. Jake is lukewarm on the "SaaS apocalypse": the cost of _producing_ software is trending toward zero, but the cost of _maintaining_ it isn't. Don't rebuild everything; do rebuild the third-party software that's a binding constraint on your business.
- **Agents as colleagues.** Their two WhatsApp agents collaborated like two people, spinning up databases and building a web app in a Sunday afternoon: a preview of agents as entities within organisations. The corollary: treat agents like junior hires. Give them their own identity and permissions, and **don't hand them the company credit card**.
- **Goals and loops.** Long-running agents (13 to 24+ hours) work when there's a clear objective and a way to self-test, like a browser or a verifiable outcome. Long runtime doesn't guarantee good output: models want to please you, so build verification in rather than trusting the answer.
- **Tokenomics as capex, not opex.** Discovering a new AI workflow carries an upfront, deliberately wasteful cost; you then hill-climb on efficiency while model prices fall for a given level of intelligence.
- **Claude Tag is the headline.** Claude as a multiplayer teammate in Slack, with its own account, per-channel permissions, and memory that respects channel boundaries. It now opens 65% of Anthropic's pull requests, and context accumulates naturally in channel history, which dissolves much of the context-maintenance and hallucination problem of hand-built harnesses. Teams support is coming.
- **Build dexterity, not just tool skills.** Claude Code went from 0% to 100% of how Anthropic writes code, and its share is _already_ declining as newer tools arrive. Enablement on any single tool is half the equation; the durable skill is comfort with constant change.
- **Do more of your work in the open.** Day-to-day work that lives in DMs is invisible: to your colleagues, and to any agent you'd like to help out. Move it into open Slack or Teams channels and the context accumulates where an agent can absorb it and start contributing proactively, and where the knowledge compounds for the humans too. Jake likened the shift to Bezos's early-2000s mandate that every Amazon team expose its work through clean interfaces: an unglamorous discipline at the time, and the one that made AWS possible.
Designing for the future is how you get ahead: build for the model that's
six months away, not the one in front of you. The thing I'm most keen to
explore is Claude Tag as a new paradigm of AI interaction. Tools like Claude
Code give one person an intelligence boost; an agent that participates in
your team's channels, with its own identity and memory, levels that up to
organisation-wide intelligence.
## Damon Salesa: who does the future get built for
Professor [Damon Salesa](https://www.linkedin.com/in/damon-salesa-54567a10a/) is Vice-Chancellor of [Auckland University of Technology](https://www.aut.ac.nz/) (28,000 students and 4,000 staff) and a distinguished scholar of social and technological change and of the Pacific. He leads one of the institutions most directly exposed to AI's disruption of education, and brought a historian's lens to the question of who technological revolutions really serve.
> "Technology is neither good nor bad; nor is it neutral."
- **Kranzberg's first law.** Technology always interacts with its social, political and economic context: the printing press spread both the Reformation and the Inquisition; the internet connected the world and surveilled it. The same technology liberates in one context and imprisons in another.
- **Artifacts have politics.** Design encodes who will use a technology and on what terms, like who gets the power station next door and who gets the high-voltage line overhead. A system built without your participation will often work against you, not through malice but through indifference, which is just as consequential.
- **The enduring questions** are never just what a technology can do, but _who it will do it for, on what terms, who decides, and who benefits_.
- **AI is historically unusual.** It's the first major technological shift without public-research origins; it came from the commercial private sector. That's part of why Anthropic's constitutional AI and safety commitments drew him in: an inspiring intellectual project, building a constitution for a technology we haven't yet seen.
- **AUT started with principles, not technology.** Affirming _te tāngata_, the primacy of human-to-human relationships (dismissed as too obvious to write down two years ago; prescient now that people form deep relationships with AIs), and tikanga-aligned adoption. The commitment: AI should improve who they are, not transform their values.
- **The risks are already proximate.** The SaaS apocalypse is coming for universities (a small team can now replace a half-million-dollar subscription), while third-party risk is enormous: AUT was among 8,000 institutions hit by the Instructure hack. Internally, some staff struggle to log into email while others run local LLMs under the desk: "a beautiful microcosm of New Zealand".
- **Two futures.** One has already happened: a world with AI and robotics that we must adapt to and align with who we want to be. The other is the future we make together: a rare opportunity to fix existing inequities, rather than letting the revolution reshuffle the deck into new haves and have-nots.
- **New Zealand must contribute, not just consume.** A nation of five million won't train frontier models, but it can't settle for being a consumer of this technology. Its contribution can be unique, distinctive and enduring.
A few good chats after the talks landed on this: what's the role of humans
once AI and robotics handle the basic needs? What does that economy look
like, and where does human value sit? Damon's framing is the honest answer:
that world is already coming, but its shape isn't fixed, and it depends on
how we design, adopt and work with the technology today. If I had to guess
where the value settles, it's the principle he wrote into AUT's approach
before it seemed worth writing down: te tāngata, the human-to-human
relationships that matter most.
## Panel Q&A highlights
### Does AI efficiency kill originality?
In verifiable domains (code passing tests, mathematics), originality isn't the point. In creative domains convergence is a real risk, but you can deliberately prompt for out-of-distribution ideas ("I want crazy ideas") and skills can bend the model's defaults (Mike, who notes AI still "fails miserably" at writing his novels). Efficiency and creativity are orthogonal: models are trained to hill-climb verifiable tasks at minimum tokens, which looks nothing like idea generation (Jake). Fable 5 reportedly arrived with about 80% of its system prompt deleted as models got smarter; and question whether "the way we've always done it" is really originality at all, à la AlphaGo's move 37 (Adam). Natalie uses AI pre-idea (topic generation) and post-idea (pressure-testing arguments) for her legal AI Substack.
### How do you vet shared AI skills for malware?
Jake reads his small working set (five to ten skills) line by line, and argues even an organisation-scale curated registry only needs 50 to 100 skills; human review is feasible and appropriately conservative, though AI-assisted scanning can be designed too. Natalie pointed to a _Skills-QA_ skill in the Claude for Legal plugin built for exactly this purpose.
### How do you get internal users to buy in to AI?
Give people relatable winners and references (Mike). Local champions beat external enablement teams; the nervous convert when a teammate succeeds, not when a programme tells them to (Jake). Pitch AI as a career-growth engine, automating the drudgery so people grow on judgment work, never as a headcount cut (Natalie). Pair decades-deep domain experts with technical people and build in the open; the veterans who never coded are now doing incredible things (Adam). And never show up with a requirements document when you could show up with a prototype (Mike).
### How do you use AI with sensitive client data?
It's mostly about demonstrable governance: a framework for what you do and don't do with AI that your client is comfortable with (Jake). Law firms now face clients demanding AI use _and_ clients forbidding it. Meet them where they are, with multi-model flexibility or architecture that provably separates one client's data from another's (Natalie).
### How do the speakers use AI in their personal lives?
Adam: wrangling his many businesses into one organised whole. Natalie: World Cup win-probability artifacts with the players rendered as Pokémon cards for her son. Jake: Claude does his groceries at Woolworths and filed his tax return via the IRD website, and he recently gave Claude Code overnight read access to his email and Drive to build a personal knowledge graph. Mike: building _Ultimate Grandmaster_, a chess career-simulation game. Watch for it on Steam.
## Closing announcements
Adam announced the first **New Zealand Claude Impact Lab**, targeted for 8 August in Wellington with Auckland to follow: 50 to 70 volunteer engineers, product managers and builders solving real problems for government and charities, free of charge. If you know a charity or agency with a problem worth solving, get in touch with Adam.
Congratulations to [**Chris Watson**](https://www.linkedin.com/in/chris2watson/), New Zealand's newest Claude Ambassador.
And Adam's reality check for the room: his barber has heard of ChatGPT but not Claude. We're still early.
---
## The Hidden Cost of Waiting on AI
URL: https://www.elementx.ai/blog/the-hidden-cost-of-waiting-on-ai
Published: 18 March 2026
Updated: 30 April 2026
**What's the risk of doing nothing?**
At a time when it seems like there couldn't be more uncertainty added to our environment, organisations (or their boards) are being asked to make decisions about investing in artificial intelligence projects without the sort of certainty they would normally prefer for significant capital allocation decision-making.
#### What about Vibe Coding?
2026 has fast shaped up to be the 'Year of Agent-Assisted Coding' among other things (commonly referred to as [Vibe Coding](https://www.forbes.com/sites/bernardmarr/2026/02/10/why-vibe-coding-is-about-to-change-work-in-every-industry/)). This is a meaningful shift with significant implications for labour markets, established software companies and consumers of software technologies. There are many articles and posts speculating on the implications of this. I work as a board chair, so I am less interested in being perfectly right about these implications and where they lead; and much more interested in understanding how boards can still make great decisions in these uncertain periods.
When it comes to adopting and implementing these new agentic tools, the downside risks are mostly obvious. Reckless innovation can lead to 'agentic off the rails' carrying reputational and even operational risks for an organisation. Locking into AI applications or onto technology stacks might lead to dead-end technology choices in the current race for model and inference dominance. A clear leap forward and significant advantage gained could even be lost almost overnight, as major vendors release new tools and render your newly built application practically obsolete.
#### How do we decide?
These downside risks of investing in agentic AI are all real and need to be carefully considered. They need to be weighed against expected and measurable gains, outcomes, and improvements for the organisation.
But what about the risk of doing nothing?
At a time of such uncertainty and volatility, often the safest choice appears to be to wait on the sidelines until the dust settles in the expectation that the best path will be revealed by the pioneers who have gone before (not to mention the bodies of those that didn't make it).
#### How safe is it on the sidelines?
Whilst this will sometimes without a doubt be the best choice for an organisation, it is important that the risk of doing nothing is measured and assessed in order to reach that conclusion. For clarity, the concept of doing nothing should be specified. In this context, I think doing nothing means putting any planned agentic or AI initiatives on hold, or stalling them at inception whilst asking for more information to support a proposal. For some organisations, it might mean locking down or locking out the use of generative AI tools.
A widely cited [MIT paper](https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/) late last year suggested that a high proportion of organisations that have invested in AI failed to see a meaningful return. The methodology and underlying data has since been challenged, but the implications drawn created a lot of debate. Yet at the same time, there are many examples of organisations with foresight and enough access to AI capability, making choices to significantly reduce their headcounts and cost bases, relying significantly on these tools to make the difference. Regardless of the proportions, this means there are organisations expecting significant gains through these investments.
In this context, the choice to do nothing is not actually a zero-cost path. Time spent doing nothing will mean time spent experiencing declining relative productivity compared to those competitors who have invested and executed successfully. Your organisation may have an operational 'moat' created over time by finding the best way to serve customers. If a competitor uses agents to successfully automate complex workflows, they may soon achieve a cost structure that you cannot match.
#### Time on the sidelines might be time out of the game.
Time spent doing nothing is not just about shifts in relative productivity. For this kind of decision making, time dimensions need to be considered. It takes time to get to a successful outcome with AI tools; especially when it comes to your workflows and complex operational processes. Finding and organising your data supply, tuning and training your models, developing your organisation's unique context. These requirements all take time.
#### A future where access to AI tools and processing becomes important to your employees.
If you believe your people and organisational culture is your significant advantage, then consider that the best talent in the market will likely gravitate to AI-forward businesses. Where they can learn skills and develop their careers to suit the direction of travel we can all see. Otherwise they will lean into and employ the tools at home on their own time. Perhaps also, they will create shadow AI projects in your workplace, which of course introduce risks of their own.
So whether you are starting a general board discussion about your organisation's position with respect to the adoption and use of AI tools or whether you are considering a specific business case for a project, make sure you have teased out the main risks of doing nothing for your unique situation and that these risks are included in the discussion and the decision making process.
###### In addition to his position as Executive Chairman of ElementX, Richard McLean has over 20 years of experience helping New Zealand businesses tackle growth challenges and bring new products to market.
###### Subscribe below to be the first to know when new posts are published, or [follow Richard on LinkedIn.](https://www.linkedin.com/in/richardmcleannz/)
---
## From Chaos to Clarity: Navigating Data Duplication, Abundant Data, and Query Optimisation
URL: https://www.elementx.ai/blog/from-chaos-to-clarity-navigating-data-duplication-abundant-data-and-query-optimisation
Published: 4 February 2024
Updated: 4 February 2024
**A practical guide to making Retrieval-Augmented Generation models perform on real enterprise data, covering data cleaning, deduplication, and query strategies like vector search, semantic reranking, and HyDE.**
The Retrieval-Augmented Generation (RAG) model is emerging as a powerful new AI tool; however, harnessing its full potential requires navigating the complexities of extensive and messy datasets. From website crawling to query optimisation, this blog post will provide some clarity to help you unlock the true power of this model.
#### **The Complexity of Retrieval**
Searching for information can be a challenging task, especially when dealing with large datasets, duplicate content, and unstructured information. Unlike typical machine learning tasks that involve categorising or predicting outcomes, search focuses on identifying the most relevant documents from a myriad of options with overlapping information.
#### **Data Cleaning for Improved Performance**
Enhancing RAG performance relies on meticulous data cleaning. While RAG may work well with simple and clean data sources out of the box, using websites or complex datasets requires preprocessing. Crawling data from websites often leads to extraneous information, which can introduce noise in the retrieval results. To address this, it is crucial to take a systematic approach that includes only the relevant information.
#### **Addressing Data Duplication**
Duplicate data is a major hindrance to RAG's performance as it reduces the uniqueness of information available for generating answers. A careful crawling strategy that includes only unique information is essential. Additionally, a post-crawl deduplication step becomes crucial, especially when dealing with scenarios like university websites where course outlines from different years may be duplicated.
#### **
Navigating Through Abundant Data**
When it comes to dealing with vast amounts of data - even after ensuring it's clean and accurate - enterprises, especially Tertiary Institutions, often face a challenge in searching through it all. Let's take an example: someone asks, ‘Do you have any scholarships for first-year students in Creative Arts?’. In a typical search, it would retrieve pages with any of those search terms, including creative arts faculty pages, first-year guides, and general scholarships.
To tackle this issue, we need to go beyond a basic keyword search and enhance the retrieval strategy. One approach is to combine vector search, semantic reranking, and adopt a multi-stage search process, just like a human would. Rephrasing queries is a common practice that boosts retrieval performance. A lightweight yet effective solution is instructing the language model (LLM) to rephrase the query into an efficient search query. Another method, called [HyDE](https://arxiv.org/pdf/2212.10496.pdf), constructs a hypothetical answer/document using an LLM, which is then utilised in the search. However, it's essential to exercise caution with keyword-based search engines, as increasing the number of words can lead to unexpected and sometimes irrelevant results.
With these refined approaches, we can conquer the data challenges faced by enterprises and Tertiary Institutions, making information retrieval more effective and efficient. By harnessing the power of technology, we can discover meaningful insights and find relevant answers amidst the vast sea of data.
#### **Final Thoughts**
Mastering the potential of the Retrieval-Augmented Generation model involves addressing the intricacies of data, from cleaning and deduplication to effective filtering and query optimisation. By implementing these strategies, you can unlock the true power of RAG and turn it into a valuable asset for information retrieval in diverse scenarios. So go ahead and optimise your RAG model to get the most out of it!
---
## The Importance of Explainability in AI
URL: https://www.elementx.ai/blog/the-importance-of-explainability-in-ai
Published: 25 January 2024
Updated: 30 January 2024
**With generative AI now routine, businesses need to weigh up when a transparent 'glass box' model is the better choice over a powerful but opaque one, especially in finance, healthcare, legal, and education where decisions carry real weight.**
In just the past year, Artificial Intelligence (AI) has experienced a revolutionary shift with the rise of ChatGPT. This breakthrough has not only propelled AI technology forward, but also raised general awareness about its capabilities, advancing domains like chatbots, image generation, and image recognition. It's easy to feel overwhelmed by the daily flood of news and the pressure to implement such AI in your own organisation - even for those with some degree of understanding - and navigating this landscape can be daunting, which is why it’s more important than ever to understand the concept of ‘explainability’ in AI. Let's break it down:
#### **The Importance of Explainability in AI**
In AI, explainability refers to the ability to describe a model's decision-making process in a way that humans can understand. It is pivotal to tailor this explanation to the intended audience, whether it's a customer seeking clarity on a loan application denial or a doctor interpreting a diagnostic AI tool.
#### **The Critical Need for Explainability**
Explainability becomes especially vital when AI decisions significantly impact individuals and communities. This need spans across various sectors, including finance, legal, healthcare, and education. Understanding the rationale behind an AI's decision is not just a matter of curiosity, but of ethical and practical importance.
#### **Classifying AI Models: From Glass Box to Black Box**
AI models can generally be classified into two types: glass box models, which are highly explainable, and black box models, which, although well-understood in their construction, offer limited insight into their decision-making processes. Generative AI and deep learning models usually fall into the latter category, posing challenges in terms of explainability.
#### **Addressing the Dilemma in Critical Industries**
Imagine a scenario where AI decisions are pivotal, like determining the best treatment plan for a patient, making complex financial predictions, or providing sound legal advice. In these critical industries, the inability to fully understand how generative AI models arrive at their conclusions can present major hurdles. This lack of transparency raises significant questions and can limit the trust and acceptance of AI technologies.
Let's draw a parallel with the world of healthcare. Just like how medicines like Panadol are proven effective through rigorous trials, despite not having a comprehensive understanding of their underlying mechanisms, AI models can produce accurate results without us fully deciphering their internal processes. However, in critical industries, where the stakes are high and human lives and livelihoods are at stake, we need to strive for a deeper level of insight into AI decision-making.
#### **Adopting Generative AI with Caution and Responsibility**
So, how can we harness the power of generative AI in areas where explainability is crucial? The key lies in making well-informed decisions when selecting the right model for the job. It's important to remember that not every situation calls for generative AI; sometimes, a more transparent, glass box model may be a better fit. However, in cases where generative AI is the chosen route, understanding the training data and implementing techniques for improved explainability becomes paramount. By taking these proactive measures, we pave the way for responsible and effective deployment of generative AI.
#### **The Path Forward: Testing, Human Involvement, and Transparency**
Just like rigorous processes in the medical field help us comprehend new treatments, thorough testing and trials play a pivotal role in understanding generative AI models. This approach allows us to assess their effectiveness and identify potential risks. Additionally, by incorporating human oversight, we ensure continuous monitoring and evaluation of AI's decisions. Human involvement not only adds an extra layer of accountability but also provides valuable insights that machines alone may overlook.
And let's not forget about good AI governance and transparency! Openly discussing how AI models work and acknowledging their limitations not only builds trust with users but also fosters an environment for constructive feedback and improvement. Together, these strategies create a solid path forward, enabling us to unlock the full potential of generative AI while keeping responsible practices at the forefront.
#### **Key Takeaways: The Importance of Explainability in AI**
**1\. Concept of 'Explainability':**
Explainability in AI refers to the ability to articulate a model's decision-making process in a way understandable to humans.
**2\. Critical Need Across Sectors:**
Explainability is crucial when AI decisions significantly impact individuals and communities, spanning sectors like finance, legal, healthcare, and education.
**3\. Glass Box vs. Black Box Models:**
AI models can be classified as glass box (highly explainable) or black box (limited insight). Generative AI and deep learning often fall into the black box category, posing challenges.
**4\. Dilemma in Critical Industries:**
In critical industries, where AI decisions hold substantial weight, the lack of transparency in generative AI models poses challenges in terms of trust and acceptance.
**5\. Adopting Generative AI Responsibly:**
The key lies in making informed decisions when choosing AI models. Not every situation requires generative AI; sometimes, transparent models may be more suitable. Understanding training data is crucial for responsible deployment.
**6\. Testing, Human Involvement, and Transparency:**
Rigorous testing, trials, and human oversight are essential for comprehending generative AI models. Human involvement adds accountability and provides insights machines may overlook. Openly discussing AI models' workings and limitations fosters trust and constructive feedback.
Reference & Further Reading: [https://aiforum.org.nz/knowledgehub/explainable-ai-building-trust-through-understanding/](https://aiforum.org.nz/knowledgehub/explainable-ai-building-trust-through-understanding/)
> **Ming Cheuk**, ElementX's CTO and Executive Council Member of the AI Forum, is a visionary leader with a background in Mechatronics and a PhD in Bioengineering. He's authored this insightful post highlighting Explainable AI's crucial role in AI integration, addressing challenges, and advocating for responsible adoption. Learn more about Ming and his contributions to the field of AI on [ElementX's team page](https://www.elementx.ai/team/ming-cheuk).
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## Enhancing Education with RAG: How Universities Can Benefit
URL: https://www.elementx.ai/blog/enhancing-education-with-rag
Published: 21 January 2024
Updated: 21 January 2024
**How universities can use Retrieval-Augmented Generation to keep AI responses grounded in their own course materials, improving search, personalising the learning experience, and giving students access to multimedia resources through a single interface.**
Retrieval-Augmented Generation (RAG), an architecture that augments the capabilities of Large Language Models (LLMs) like ChatGPT, combines the power of information retrieval systems with generative AI. By integrating RAG into their infrastructure, universities (and other enterprise businesses) can take control of the grounding data used by LLMs and provide an enhanced learning experience for their students.
One of the key advantages of implementing RAG is the ability to control generative AI output, making it focus on company-related content from enterprise documents, images, audio, and video. This means universities can ensure that the AI-driven responses generated by LLMs are based on accurate and relevant information from their own content repositories. Whether it's text-based resources, images, or even multimedia content, RAG enables universities to deliver precise and tailored results to students' queries.
Here are some ways universities can benefit from RAG:
**Improved Search Capabilities:** With RAG, universities can provide students with a more sophisticated search experience. The information retrieval aspect of RAG allows for indexing strategies that load and refresh at scale, ensuring the students have access to the most up-to-date and relevant information. The query capabilities and relevance tuning capabilities enable universities to return short-form results that meet the token length requirements of LLM inputs.
**Enhanced Learning Experience:** By integrating RAG with AI, universities can deliver personalised and context-aware responses to students' questions. The natural language understanding and reasoning capabilities of LLMs enable the generation of responses that are augmented by information from the retriever. This allows for a more interactive and engaging learning experience, where students can have back-and-forth conversations or receive fully composed answers in real-time.
**Comprehensive Content Search:** RAG's ability to index and retrieve various types of content, including text, images, audio, and video, makes it an ideal solution for universities with diverse resources. Whether it's searching through academic papers, analysing images, or accessing multimedia content, RAG enables students to explore a wide range of resources through a single integrated search platform.
**Faster Access to Relevant Information:** Time is of the essence, especially when students are searching for information to aid their studies. RAG's fast and efficient search capabilities, coupled with fast response times, ensure that students can quickly retrieve the information they need. This reduces the time spent on searching for resources and allows students to focus more on learning and critical thinking.
However, assessing RAG's suitability for diverse educational applications is crucial, considering its strengths and limitations at an enterprise level. Robust data governance practices will also play a vital role in ensuring privacy and security when integrating RAG in education, fostering a safe environment for student engagement and building trust in the technology. ElementX offers a [comprehensive free resource](https://www.elementx.ai/whitepapers/what-is-retrieval-augmented-generation-rag) delving into RAG's potential in education, providing insights, pros and cons, and considerations for adopting RAG in universities. Or, to discuss the application of RAG in your business, get in touch [here](https://www.elementx.ai/contact).
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## Introducing Sophie
URL: https://www.elementx.ai/blog/introducing-sophie
Published: 7 November 2023
Updated: 9 November 2023
**How ElementX, in partnership with UneeQ, brought a digital human named Sophie to the Aotearoa AI Summit panel as an unscripted AI participant, pulling insights from a large language model in real time.**
During the Aotearoa AI Summit, we made world history with Sophie, our digital human collaboratively developed with UneeQ. Our vision for Sophie was groundbreaking: she was designed to introduce unscripted insights into panel discussions, marking a significant departure from the scripted conversations typically associated with digital humans in this use-case. Her unscripted contributions provided us regular humans with the opportunity to interact with AI in a completely natural manner - not only did this novel approach allow for a seamless interaction, but it also brought the invaluable insights of a broadly trained LLM into the conversation. This breakthrough in AI-human interaction set a new standard, combining the best of both worlds to create an engaging and informative experience for all involved.
We'll walk you through the step-by-step process of bringing Sophie to life and the technologies that make it all possible.
#### 1\. Audio Integration: Gearing Up for Immersive Conversations
The process begins with audio integration. Sophie is connected to the AV system, allowing her to receive audio input directly from the microphones the panelists used and play audio back into the room speakers. This integration ensures that they can listen and respond just like a real person would.
#### 2\. Real-time Transcription: Decoding the Language of Sound
One of the primary challenges in creating a digital human is accurately transcribing the real-time audio input. Imagine multiple speakers talking simultaneously in a single audio stream – deciphering this linguistic puzzle is no easy feat. Clever algorithms work tirelessly to separate the voices and create coherent transcriptions.
#### 3\. EPIC Integration: Guided by Human Expertise
No digital human creation process is complete without human involvement. The transcribed audio data is sent to EPIC (ElementX Panel Intelligence Controller), which acts as an interface for human-in-the-loop review. Here, the operator meticulously reviews the transcribed content in real-time, ensuring its accuracy and relevance before it is sent to Sophie for a thoughtful and contextually appropriate response.
#### 4\. Large Language Models: The Power of Artificial Intelligence
Modern large language models are the backbone of this process. These AI models which also power ChatGPT, enable the digital human to understand and interpret the transcribed content, forming the basis for their responses. The model has been tuned specifically for the panelist use-case to ensure appropriate and insightful responses.
#### 5\. Speech Synthesis: Breathing Life into Words
Once Sophie has processed the transcribed content and generated a suitable response, the next step is to vocalize that answer back to the audience. This is where speech synthesis comes into play. This phase is crucial for the project as it brings together all previous steps, making Sophie truly interactive and human-like.
#### 6\. Live Demo: Witnessing the Future in Real-Time
Once the digital human interface is up and running smoothly, it's time to showcase its incredible capabilities. A panel discussion ‘Large Language Models in Aotearoa’ was the perfect venue to share the integration of AI in human-computer interactions. The panel she presented in brought together experts from various sectors, academic research, education, and public consultations. During the panel, Sophie interjected her unscripted thoughts, adding a fresh perspective to the dialogue and igniting fascinating discussions that challenged conventional thinking.
Sophie's introduction into the world of AI has already begun to change the way we interact with LLMs and AI systems. Her unscripted interjections during the panel brought new insights to the discussion and challenged conventional thinking. This revolutionary approach to human-computer interaction demonstrates the value that AI and LLMs can add in synthesising information and contributing to more effective decision-making. As we continue to evolve our understanding of AI, embracing new technologies like Sophie will pave the way for more natural and productive interactions between humans and machines.
This incredible fusion of technology and humanity opens up limitless possibilities, and while Sophie's role as a panelist is certainly impressive, it's just the beginning of what Digital Humans can do. With their empathetic faces, they can provide training, serve as information sources in complex knowledge bases, and help users of all backgrounds navigate intricate online tasks. At ElementX, we've partnered closely with UneeQ Digital Humans to deliver a wide range of innovative solutions. Whether you're looking to invite Sophie the Panelist to your next conference or embark on your very own world-first digital human project, our team is here to assist. Let's revolutionise the way we interact with technology together!
---
## Happy 10th Birthday, ElementX!
URL: https://www.elementx.ai/blog/happy-10th-birthday-elementx
Published: 18 September 2023
Updated: 18 September 2023
**Founders Daniel Xu, Ming Cheuk, and Richard McLean reflect on a decade of building ElementX (formerly Spark 64), the AI breakthroughs they have lived through, and the lessons they pass on to organisations starting their own AI journey.**
Happy 10th birthday, ElementX! We’re thrilled to celebrate this milestone (with cake!) and reflect on the lessons we've learned, the exciting challenges we've tackled, and the fantastic accomplishments we've achieved. To help us put the last decade into perspective, we sat down with our three founders to discuss what they've learned about AI, practical advice for companies looking to start, and all the memories in between - from silly Mario Party competitions, to blowing up on Twitch, this last decade has had it all.
#### **Reflecting on the Journey**: Looking back over the past decade, how have you seen the landscape of AI technology change, and what are the most significant milestones or achievements in the AI domain that stand out to you?
**Daniel Xu:**
AI has come such a long way over the past 10 years. It’s gone from somewhat gimmicky to now delivering real business value, especially in the field of Virtual Assistants. I remember in the early days, there was a lot of hype and vapourware going around. I recall back in 2016, we were using an ‘AI image recognition’ service and later found that it was actually just a bunch of people in the backend manually tagging the images. It did fool a lot of people though and got us onto WIRED.
**Ming Cheuk**:
The past 10 years has corresponded with some of the biggest developments in AI, despite the fundamental principles they’re built on existing before that (1957). We saw image recognition taking a leap in 2012 with AlexNet, the basis of modern LLMs in 2017 with the Transformer, and emerging signs of generative with GPT-1 shortly following in 2018. The GPT we know today has really only been a recent development in the past year, and has leapfrogged the capability and usefulness of AI into solving some problems previously unsolvable by machines.
**Richard McLean**:
Ten years seems a long time to look back on AI, given the constant acceleration of its capabilities and use. Clearly one of the significant milestones has been the release of LLM’s and useful interfaces to allow non-experts to make use of and experiment with the technology. I think this has kick-started a lot of curiosity and thinking around AI. One of the achievements in this regard is the democratization of access to the technologies.
I am also proud of our team and how they have managed to stay abreast of the frequent and varied advances in AI technologies. To best serve customers we have needed to maintain this knowledge - even though it changes daily.
#### **Lessons Learned:** Building and scaling an AI consulting company involves navigating various AI-related challenges and learning experiences. Can you share one key lesson or piece of AI-specific advice you've gained during ElementX's journey?
**Daniel Xu:**
There’s a lot to consider around AI - not just the technical. A lot of people have concerns about bias, safety, ethics, etc. So one thing we’ve learned is you’ve got to take people on a journey. One advice we give to organisations considering AI - especially if they’re new to this - is to not be too ambitious with your initial step. Don’t pick the hardest problem to solve, but instead, identify a low hanging fruit, get a win and use that to help drive adoption inside an organisation.
**Ming Cheuk**:
Don’t dismiss a technology just because it’s in its infancy. Several times in the past we’ve come across AI accomplishments from research that sounded great on paper but disappointed when tested out on real data. This includes virtual assistants that couldn’t even respond to “hi”, early image recognition that couldn’t tell a cat from a dog. However, as those technologies became better, more people realised the value (and potential dangers). Recognising signs of the beginning of something big is important from both a competitive advantage perspective, but also from an ethics/safety perspective in managing the adoption/use before they become an unfixable problem.
**Richard McLean**:
One lesson is how organisations need to get things in the right order - get their ducks in a row, so to speak. Not so long ago we were often dealing with mature organisations that had decided at the executive level to launch into a specific AI project on the advice of their teams and with board level support. The challenge (and learning) was that they weren't ready, nor was their data. Not an insurmountable challenge by any means, but it highlighted the importance of setting expectations around AI and managing those.
#### **Favorite Memories:** Every journey is filled with memorable moments, big and small. What are some of your favourite memories or experiences from the past 10 years as co-founders of this company? Whether it's a project, a team achievement, or a personal anecdote, we'd love to hear about what has made this journey special for you.
**Daniel Xu:**
For me business is all about people. One of our values at ElementX is People First. There’s been so many amazing people we’ve worked with, our team, customers and partners. A lot of blood, sweat, and tears. One of the things I’m most proud of is the way we’ve trained and developed our people along the way. We have a number of staff that started out as grads/interns and have gone into much more senior roles. Its just fantastic to see them grow into their careers.
**Ming Cheuk**:
Our hackathons were something special. We’d get the team together for a few days at a bach somewhere out of town, experiment with and build some really cool tech we wouldn’t normally do day-to-day. Some memorable things that came out of it include launching a digital human streamer on Twitch with early Generative AI (Blender from Facebook) and getting huge stream engagement, and 1,000 followers in the course of a few days (and money-paying subscribers too!). One hackathon idea/project even led to a client engagement which is now deployed in production. Aside from the intense building, there was always enough time to enjoy a team cooked meal, play some board games, and share skills and stories outside of work.
**Richard McLean**:
So many, they all weave into a series of great memories. We started out with very little apart from some ambition and an intrepid attitude. The main highlights all involve experiences shared with my co-founders or others in the team. Many of those were about achieving goals, both small and large. Planning a sales meeting, closing a deal, and reviewing the process together for example. Another would be hitting revenue milestones as we have grown. One that brings back a smile is having all our laptop boxes stored - with serial numbers etc. We were burgled by a burglar who must have been a bit obsessive compulsive. He spent so long matching all the laptops to the right boxes (all while being watched on video) the police had time to come down and grab him in the act. A fond memory.
From the early days of AI hype and experimentation to the rise of GPT and every-day AI interactions, we have witnessed the transformative power of AI firsthand. But beyond that, what’s made this journey truly special are the memories we have created together as a team. From the thrill of hackathons and innovative projects to the growth and development of our talented staff, every moment has shaped our company and its culture. And as we look forward to the next decade, we can't wait to see what the future holds for ElementX and the ever-evolving world of AI.
---
## Assisted Search: The Natural Evolution of Search
URL: https://www.elementx.ai/blog/assisted-search-the-natural-evolution-of-search
Published: 15 May 2023
Updated: 21 May 2023
**A walkthrough of the four levels of search (keyword, semantic, contextual, and generative) and how enterprises can move beyond simple keyword matching to give customers and staff faster access to the right answer.**
In today's digital age, enterprises are faced with the challenge of providing an efficient and effective means for their customers and employees to search for relevant information within an ever growing set of content on their website or internal knowledge systems. In customer-facing industries like telecommunications, customers expect quick and accurate answers to their questions about their plan or service. Similarly, large enterprises often have vast internal knowledge bases that can be difficult to navigate, making it challenging for employees to find the information they need. In this article, we will explore the 4 levels of search, which offer advanced and accurate ways to quickly and easily find the right information.
#### **Level 1: Keyword Search**
This is the most basic level of search and what we're all familiar with. It involves typing in a few keywords into a search engine and hoping that the results will be relevant to our query. While this level is still effective for simple queries, it falls short when we're looking for more complex information or have trouble coming up with the right keywords. If, for example, we're looking for information about the best restaurants in Auckland, we might type in ‘best restaurants in Auckland’ and hope for the best.
#### **Level 2: Semantic Search**
Semantic search takes keyword search up a level by considering the meaning behind the keywords, rather than just the keywords themselves. This means that we don't need to use the exact keywords to find what we're looking for. Instead, we can use synonyms and related words and still get relevant results. If we're looking for information about the best eateries in Auckland, we might type in ‘top dining spots in Auckland’ and still get relevant results.
#### **Level 3: Contextual Search**
Contextual search goes beyond just matching the meaning of the keywords to also take into account the specific user and their intent behind the search. This means that the search engine can provide more accurate and personalized results based on the user's context - such as if we're searching for information about a particular car model, the search engine might take into account our location, our past search history, and other contextual factors to provide us with more relevant results.
#### **Level 4: Generative Search**
Generative search takes search to the next level by not just returning relevant results but also generating an answer based on those results. This means that the search engine can directly answer the user's query, rather than just providing a list of potential results. For example, if we're searching for the recipe for a particular dish, the search engine might generate a summary of the recipe, including the ingredients and steps needed to make it.
At ElementX, we believe that search should be more than just a simple keyword search: that's why we offer a better search solution for enterprises that leverages the power of semantic, contextual, and generative search to better serve information to their staff and customers. By understanding the user's intent and context, we can provide more accurate and personalised results, making it easier for users to find what they're looking for. [Contact us](https://www.elementx.ai/contact) to learn more about how we can help improve your enterprise search solution.
---
## Unleashing the Power of Search: Exploring the 4 Levels of Advanced Search for Enterprises
URL: https://www.elementx.ai/blog/unleashing-the-power-of-search-exploring-the-4-levels-of-advanced-search-for-enterprises
Published: 3 May 2023
Updated: 30 May 2023
**Why AI-assisted search is the next step beyond traditional search engines for enterprises, with examples from healthcare and legal, plus the guardrails and disclaimers needed to manage the risk of inaccurate AI-generated answers.**
Search engines are not new. In fact, they've been around for decades. With technological advances, simple keyword search tools have evolved into intricate algorithms that incorporate machine learning to understand natural language and context to offer more precise and relevant search outcomes. Despite significant advancements, though, searching remains a time consuming task - whether it's utilising a tool like Google or navigating an internal inventory for a store; and searching becomes even more difficult when dealing with intricate knowledge bases. Enter assisted search.
AI assisted search is the natural evolution of search, taking it one step further by eliminating the need for manual page visits to find information. Instead, it uses AI to sift through mountains of data to present the most relevant and accurate answer. A great use case for this would be in customer service, where quick access to information can help customers find what they need without having to call a support center - a win for those customers who prefer to avoid human interaction.
Even if they do need to call the support center for a more complex issue such as technical support, the AI Assisted Search can help support staff quickly find the relevant internal documentation to help the customer, providing better customer service and the ability to serve more customers.
Likewise, AI search can help other staff who are frequently confronted with the intimidating task of navigating through disorganized and voluminous internal company documents to locate required information as part of their role (knowledge workers). For instance, medical staff can leverage assisted search to gain quick and easy access to the latest treatment guidelines or procedures, which can result in better patient outcomes; [Diabetes & Metabolic Syndrome: Clinical Research & Reviews, Volume 14, Issue 4](https://www.sciencedirect.com/science/article/pii/S1871402120300771), suggesting that AI is ‘used for proper screening, analyzing, prediction and tracking of current patients and likely future patients’ during the Covid-19 pandemic. Given the constantly evolving nature of medical research and best practices, assisted search ensures that medical professionals stay informed and up-to-date, thereby enabling them to deliver high-quality patient care.
Similarly, legal firms can benefit from assisted search technology by facilitating the retrieval of relevant case law. Lawyers can save considerable time and effort by employing assisted search to identify the most pertinent legal precedents, which can aid them in making more informed decisions on behalf of their clients. This allows legal professionals to dedicate more time to building compelling arguments and developing winning strategies, thereby enhancing their overall effectiveness and success.
While assisted search technology can offer numerous benefits, it's crucial to recognise that there are potential risks associated with its use. The most significant concern is the possibility of AI-generated answers being inaccurate, leading to incorrect decision-making or actions. This is particularly concerning for our healthcare workers and lawyers alike, who deal with situations that can have significant implications for human lives.
To mitigate this risk, a large language model (LLM) that has been trained on high-quality, up-to-date data and well tested and tuned should be used. An example of this is OpenAI’s GPT 3.5 model, which had over 200 million users in the first 2 months, which gave OpenAI plenty of opportunities to refine the output. At the time of writing, no other model has had this level of user testing and fine-tuning from human feedback. Furthermore, if the AI generates an incorrect answer, there should be mechanisms in place to help reduce the likelihood of it shown to the user; NVIDIA has recently released an open source toolkit called [NeMo Guardrails](https://github.com/NVIDIA/NeMo-Guardrails) to control the output of LLMs to minimize hallucinations and factual inaccuracies.
Another way to mitigate the risks is by clearly informing the user that the answer is AI-generated and a disclaimer that it may be inaccurate. Furthermore, links to the original source of information can be provided as footnotes, which provide a mechanism for users to quickly fact check the answer (which is what Bing uses). This helps ensure that users make informed decisions based on reliable information and minimise the potential of harm from inaccuracies.
Despite these risks, assisted search is a promising technology that will only get better over time as AI improves. With the ability to quickly and accurately search through large amounts of data, assisted search can save valuable time and effort in customer service, support, and knowledge work. As with any new technology, it's important to be aware of the risks, but the benefits of assisted search make it a technology worth considering.
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## Revolutionising Tier 0 and Tier 1 Support with AI Virtual Assistants
URL: https://www.elementx.ai/blog/revolutionising-tier-0-and-tier-1-support-with-ai-virtual-assistants
Published: 19 April 2023
Updated: 24 April 2023
**How AI virtual assistants can power Tier 0 (self-service) and Tier 1 (basic) customer support, covering FAQs, knowledge bases, virtual assistants, and smart search.**
As businesses increasingly rely on technology to support their operations, the world of customer service is also evolving. One important trend in this space is the emergence of Tier 0 and Tier 1 support levels that prioritise self-service and basic support respectively.
In recent years, AI virtual assistants have emerged as powerful tools that can help enhance the quality and efficiency of these crucial support tiers. Tier 0 support, for example, enables customers to access tools and resources that can help them resolve their issues independently, without the need for human intervention, with Tier 1 support focusing on addressing basic issues and inquiries that are relatively simple and can be resolved quickly. By investing in self-service support, businesses can not only benefit their customers, but also reduce the strain on their support teams, allowing them to focus on more complex inquiries.
Here are some basic Tier 0 support implementations for your business to consider:
1. FAQs
By using topic summarisation and analysis on customer support tickets, transcripts, and emails, businesses can identify and create a comprehensive list of frequently asked questions tailored to their specific customer base.
2. Knowledge Base
Generative AI can be utilised to draft knowledge base articles, accelerating the writing process and ensuring coverage of long-tail questions. This provides customers with easy access to detailed information on various topics, even if they’re hard to navigate to on your website.
3. Virtual Assistant
Integrating a virtual assistant with the FAQs and knowledge base you already have enables the AI to answer customer inquiries more effectively. It can also collect customer details for a richer set of capabilities, such as generating quotes, submitting insurance claims, or [applying for mortgages](https://www.elementx.ai/customer-story/worlds-first-mortgage-lending-digital-human-assistant).
4. Smart Search
Implementing a smart search feature allows customers to easily search through the website, documents, and tables. By presenting summarised results or specific sections, customers can find the exact information they’re looking for more efficiently.
As the demand for fast, efficient, and personalised customer service continues to rise, businesses are increasingly turning to AI virtual assistants to meet these expectations. By utilising these powerful tools, companies can not only enhance their support offerings but also establish deeper connections with their customers. To explore the different application options available for Tier 0 and Tier 1 support levels with AI, we invite you to dive into our comprehensive and free white paper. Don't get left behind in the ever-changing landscape of customer service – stay ahead of the curve and elevate your support offerings.
---
## Zero/Few/One-Shot Learning
URL: https://www.elementx.ai/blog/zero-few-one-shot-learning
Published: 12 March 2023
Updated: 25 January 2024
**An introduction to zero-shot, one-shot, and few-shot learning techniques that let machine learning models work with very little training data, where each approach shines, and where it falls short of traditional deep learning.**
Machine learning has become increasingly popular over the years, with many companies investing heavily in deep learning models to solve their more complex problems. However, collecting and labelling data can be both time-consuming and expensive, making it difficult for many companies to get started. It's a delicate balance that can leave us feeling a little like Goldilocks – too little data and it won't work, too much data and it'll overfit. It's enough to make any company think twice before diving into the world of machine learning.
This dilemma has led to the rise of alternative approaches such as zero/few/one-shot learning, which allow machines to learn from a limited amount of data or even just one example. How do they work, and are they as effective as traditional deep learning models?
#### Zero-shot
Zero-shot learning is a type of machine learning that allows for a system to train without having any training examples from the target class. This is useful because it allows us to apply our models and techniques in new contexts where there are no labelled datasets available.
In order to achieve this, we need to build a mapping between the high level information and the input image in order to determine the class label. This can be done using a new type of neural network called a transformer, which are quickly becoming state of the art in many applications.
This approach has been shown to work well in natural language processing tasks where there are no labelled examples available - such as when trying to transcribe an audio recording into text or translate between languages where there are no parallel corpora available.
#### One-shot
One-shot learning is a technique that allows researchers to train a machine learning algorithm on a small amount of data, then have it perform well on a much larger dataset.
The technique uses the idea that if you train on one example from each class, the algorithm will be able to generalise to new examples from that class. This is surprising because we would expect the training set to overfit to the single example and not generalise well at all. But in fact, it turns out that this approach works surprisingly well. The algorithm is trained to compare examples and find a match on a large related dataset.
One-shot learning is being used today in many facial recognition tasks. This allows these products to work reliably while only needing to store one reference image.
#### Few-shot
Few-shot learning is a type of machine learning that can be used for solving problems with few examples.
The idea behind few-shot learning is simple: it uses a concept called the support set to find the most likely match for an input from a small set of examples. A few examples are needed because the system needs to learn from the examples.
In traditional machine learning, if you're trying to train a system to recognize dogs in photos, you need to give it enough information so that it can distinguish between different types of dogs (e.g., Labrador Retriever vs. Poodle). But how many photos should you give it? If you don't give it enough, then it will overfit and not be able to make reliable predictions.
The idea behind a support set is that we look at all possible matches for our input and then select the one that has the most support among our examples. For example, if we have an image of a dog that might be either a Labrador Retriever or Poodle (or any other type of dog), we will take all possible matches for each kind of dog and rank them by how similar they are to our original image (using some distance metric). We then choose one of these matches as the 'true' label for our input image.
####
#### What are the upsides/downsides?
In the past, it was often difficult for businesses to justify the cost of collecting and labeling data for use in machine learning. But with recent advances in zero/one/few-shot learning, the rise of deep learning has been incredible. Models have gotten more accurate and are able to do things that were previously impossible without massive amounts of data.
Deep learning models are expensive to train and can take a long time to be ready for production use. Collecting and labelling data is also expensive, which makes it hard for companies to get started with machine learning.
In the past, a lot of companies would just stick with old-fashioned decision trees or other simpler models because they don’t have the data or computational resources to train a deep learning model. Now, thanks to new research on transformer architectures for deep neural networks, zero-shot learning becomes feasible for many applications: we can train one model that can do many things! This means that we don't need separate models for every application; instead, we can use one model across multiple domains and products.
However, this comes at a cost: zero/one/few-shot models have lower accuracy than regular deep learning models on tasks with large all encompassing datasets. This is still an active area of research in many domains so there are less pre-trained tools out there for all applications/domains.
#### Where can you use these approaches?
These approaches are often used in proof of concept projects and demos because it allows businesses to get a feel for how well their algorithms will work before they invest in collecting more data or hiring more employees. It's also used in areas where data collection/labelling is expensive or invasive (eg: medical), areas with a large amount of weakly labelled data (eg: audio, images, video), and any sort of facial recognition task. If you're working on any sort of visual recognition task, zero-shot learning will allow you to train a model without having access to any labeled images for training purposes.
Deep learning has allowed developers to find solutions to problems that were previously unsolved. Zero/one/few-shot learning has allowed computers to begin making sense of the world around them and is the next big step in machine learning. We look forward to seeing how these developments will continue to push artificial intelligence into new territories.
---
## Our 2023 summer internships
URL: https://www.elementx.ai/blog/our-2023-summer-internships
Published: 2 March 2023
**A look at the projects our 2023 summer interns Fuki and Raining worked on at ElementX, from attribute extraction to generative 3D models.**
At ElementX, we believe that it's important to provide opportunities for young talent to learn, grow, and make a meaningful impact in this field. Our summer internships offer an immersive, hands-on experience for our interns, allowing them to work on real-world projects and gain valuable industry experience. We've designed the program so that students can build skills in their areas of interest while also getting their hands dirty with some of the latest AI advancements and cutting-edge technologies, as well as receive mentorship and feedback from experienced professionals, and make significant contributions to the company.
#### **Our Interns**
Meet Fuki, an engineering student specialising in software engineering. When he's not coding, Fuki enjoys staying active by bouldering and hitting the gym. He also has a passion for music, being a big fan of the guitar and hopes to pick it up again in the future. Fuki is trying to master the art of latte-making with his Breville coffee machine, but he admits that the results are sometimes less than perfect. If Fuki could be any animal, he'd choose to be a duck for their versatility - they can fly, walk, and swim! Fuki also appreciates their blue feathers and their ability to have fun in life, even in a simple duck pond.
Raining is a computer systems engineering student with a passion for travelling and scuba diving. When she's not in the water, she can often be found tinkering with robots or exploring the latest developments in machine learning. In the future, Raining hopes to pursue a career as a researcher or engineer in robotics or machine learning, or possibly even combine her technical skills with her desire to make a positive impact by working as a missionary. If Raining could be any animal, she would choose to be a dolphin, admiring their intelligence, playfulness, and their ability to thrive in the water.
This summer, our interns had the opportunity to work on a wide variety of projects, ranging from natural language processing to generative models. Each project offered a unique challenge and allowed our interns to gain hands-on experience and develop new and existing skills.
### **Attribute Extraction for Retail**
The interns first worked on an attribute extraction tool for retail sites. This powerful solution enables retailers to extract key attributes from free-text product descriptions using question and answering models and natural language processing, making products easily searchable on their website and allowing customers to filter by relevant specifications. This makes products easily searchable on their website, allowing customers to filter products by relevant specifications such as RAM, memory, and screen size. Read more on the Attribute Extraction project here.
### **Chatbot Conversational Testing Tool**
Fuki and Raining also worked on the Chatbot Conversational Testing Tool project. This solution is designed to evaluate the quality and effectiveness of chatbots by simulating human language and interactions. It streamlines the testing process and stands out from other chatbot testing methods because it is capable of mimicking human language. The Chatbot Conversational Testing Tool is capable of answering questions posed by a person in plain English, while also using the same kind of grammar, tone, and word choice that humans would use when responding to the same question. It also has advanced AI capabilities that allow it to interact with real-world situations like humans do: asking follow-up questions and providing context for answers.
### **3D Models**
In addition to the Chatbot Conversational Testing Tool project, the interns also worked on a project investigating generative AI using the latest and greatest tools such as DALL-E and Stable Diffusion. The goal of this project was to set up a 3D generative model that can turn text into a 3D project and generate new 3D assets for games. They were setting up models from research papers and experimenting with 3D modeling techniques. This project aimed to push the boundaries of what is possible with AI in 3D modeling and asset generation for the gaming industry.
### **Their experience**
Through their work with us, Fuki and Raining have been honing a variety of skills, including how to receive and process feedback and how to give feedback to others through code reviews. Raining said that she loved ‘being exposed to a different working culture where I know I’m being cared for’, and ‘has high management expectations for my next role’. She loved that ‘ElementX emphasizes a healthy work environment and encourages taking breaks, as the workday is forecasted for six hours instead of eight. This allows for a good balance between work and rest, without anyone micromanaging us’.
Fuki said that ‘the balance between research and implementation is also a major plus, as we’re able to see both aspects in our work’. He also said that he was ‘excited to be working on different types of projects and using the most current technologies’, as well as that ‘the small scale of the company allows us to get to know our colleagues and work together in a close-knit environment’.
The skills and knowledge gained during their time at ElementX have been instrumental in helping our interns further their careers in their desired fields. Fuki has gone on to become a software engineer at Amazon Japan, while Raining is pursuing a PhD in Robotic Engineering, focusing on the development of underwater robots.
We truly enjoyed working with Fuki and Raining on these innovative projects - their fresh perspectives, dedication, and hard work has kept us on our toes and pushed us to explore new possibilities. That's why we’re always looking for new talent to join us and take part in our exciting projects. If you’re a university student interested in interning with us in the future, we invite you to send us your information using our [contact form](https://www.elementx.ai/contact). We can't wait to hear from you!
---
## Attribution Extraction for Retail Products
URL: https://www.elementx.ai/blog/attribution-extraction-for-retail
Published: 2 March 2023
Updated: 25 January 2024
**How natural language processing makes it practical to pull structured product attributes (RAM, screen size, features) out of free-text supplier descriptions, helping retailers tag thousands of products and improving the on-site shopping experience.**
Online shopping has changed the game for retailers and consumers alike. Why would customers go on a wild goose chase for their desired products nowadays when they can just search them up and have them delivered to their door?
When customers visit your website already knowing what they’re looking to buy, they're less interested in navigating through the different categories of products and more focused on finding specific information about a particular product; whether a coffee machine has the kind of self timer they need, or whether a baby’s car seat will fit in their car. If a customer has difficulty finding something, they're likely to leave your website and go elsewhere—and that's money out of your pocket. That’s why the ability to quickly and accurately extract key attributes from product descriptions is critical. It can take a long time for staff to tag all your products’ listings with detailed specifications, covering every possible keyword, which means it takes longer to get products in front of shoppers. This becomes an even larger problem if you have a large inventory and limited resources—more so if descriptions are formatted differently across different manufacturers.
This is where attribute extraction tools come into play. These tools allow retailers to extract important information from suppliers to share on their websites, making it easy for customers to find the information they need to make a purchasing decision. By doing so, retailers can help guide customers to the right products, ultimately leading to higher sales and customer satisfaction. It’s also possible to tag large volumes of data much more efficiently than could ever be done manually. (Of course, it's important to note that even with automation, it's still necessary to have people check the results. While machines can do the bulk of the work, they aren't perfect and human oversight is necessary to ensure accuracy.)
The evolution of our attribute extraction tool has come a long way - initially, we used a tool to extract information about home printers using Regex to match text strings directly. This worked well, but as printer manufacturers started to introduce new features and synonyms for keywords related to double-sided printing, colour printing, and scanning features, it became harder and harder to match them exactly.
While it's impractical to manually list every possible synonym, our latest approach has simplified the process. Instead of custom-developing the Regex attribute each time a change is made, an API is used to identify the attribute based on its name using natural language. This new approach has made it easier to set up and can be applied to any attribute.
However, our newest version of our attribute extraction tool isn't just limited to printers. By using question and answering models and natural language processing (NLP), retailers can extract key attributes from free-text product descriptions and make them searchable on their website. This allows customers to easily shop for computers using relevant specifications, such as RAM, memory, and screen size, and can be expanded to other products like home appliances, baby car seats, and more.
Another challenge for retailers is that product descriptions are formatted differently across different brands and products, making it difficult for customers to find the information they want. Attribute extraction can help retailers make shopping easier for customers by helping them find the products they're looking for, even when product descriptions are formatted differently.
For example, a customer might be looking for a laptop with a specific amount of RAM and storage. Our attribute extraction tool can help them filter laptops by those specifications, saving time and ensuring the customer doesn’t need to leave your site to find information about the laptop elsewhere. Not only that, we can also use this tool to suggest similar laptops that meet those specifications, providing more buying options for the customer.
As online retail continues to transform the way we do business, it's essential to stay ahead of the game. Ensuring that customers can easily find what they're looking for on your website is critical, and attribute extraction tools are a game-changer in achieving this. If you’re interested in learning more about how attribute extraction could work on your website, get in touch with one of our experts [here](https://www.elementx.ai/contact).
---
## The Future of Retail: How AI is Changing the Way We Shop
URL: https://www.elementx.ai/blog/the-future-of-retail-how-ai-is-changing-the-way-we-shop
Published: 13 February 2023
Updated: 25 January 2024
**How AI is reshaping retail: personalised recommendations, conversational shopping assistants, smarter product search, and inventory management, with examples including The Warehouse Group's gift recommendation engine.**
The way we shop has changed dramatically over the past decade, with technology playing a more prominent role than ever before. According to a 2021 report by [Hootsuite](https://www.hootsuite.com/?utm_campaign=all-social_transformation_program-digital_2021-glo-none-&utm_source=white_paper&utm_medium=owned_content&utm_content=Digital_2021_Global_Report) and [We Are Social](https://wearesocial.com/uk/), the average internet user spends approximately 6 hours and 54 minutes online each day. This includes time spent on a variety of activities such as browsing websites, using social media, watching videos, and online shopping. We've gone from merely buying books and CDs online to purchasing everything from groceries to furniture through our phones. Now, AI is poised to change everything again, from how consumers find products and services to how retailers manage inventory, build relationships with customers, and streamline operations. It's a game-changer that will change the way you shop—and it's already happening!
With the help of AI-powered chatbots or digital humans, retailers are able to offer more personalised shopping experiences to their customers. These chatbots can assist with product recommendations, answer questions about inventory and pricing, and even handle customer service inquiries. It’s more than a customer service solution though - AI can analyse customer data and make recommendations based on a customer's past purchases, browsing history, demographics, location, and other factors.
A great example of this is the gift recommendation engine we built for The Warehouse Group, who wanted to provide customers with a seamless online shopping experience. To categorise the thousands of products listed, we used a Natural Language Processing solution with GPT-3, then built the customer-facing gift finder page on a scalable foundation, using Google Cloud to handle spikes in user traffic. The results showed improved conversion rates, basket size, and session duration. The Warehouse Gift Finder was a success during the Christmas period and was the highest online source of revenue generation. You can read more about the Gift Finder in our case study [here](https://www.elementx.ai/customer-story/gift-recommendation-engine).
As interest in incorporating ChatGPT into existing chatbots increases, customers will be able to interact with ChatGPT in a way that resembles a conversation with a knowledgeable salesperson. The conversational nature of ChatGPT results in a more personalised and human approach to recommending gifts and products by gaining further insight into customer interactions. Retaining this conversational context allows the assistants to pick up on the more nuanced details in their questions and requests to provide more relevant responses.
AI is also being used to improve product search and recommendation systems. These systems are designed to help customers find what they're looking for, when they're ready to buy it. This can be extremely helpful for online retailers who have a large number of products, but don't want their customers to have to sift through all of them manually. An AI-powered product search system can use customer data to recommend products they may need based on their browsing or purchase history as well as insights and trends drawn from similar demographics.
This not only improves the customer experience, but also saves retailers time and resources. A customer can use a conversational AI assistant to ask questions about a product, place an order, or track a shipment, without the need for a human customer service representative, freeing them up to handle more complex issues, such as resolving disputes or handling sensitive customer information. Additionally, the use of AI technology in customer service can also reduce errors, provide faster response times, and offer customers a convenient and accessible way to get the information they need, day or night.
The use of AI technology in retail is not limited to just customer service. The technology is also being used to optimise inventory management, reducing the chances of stock shortages and overstocking while also helping retailers make data-driven decisions about which products to stock and promote. For example, sometimes seasonal trends throw off an inventory plan—such as an influx in demand for winter coats if winter arrives earlier than usual, leading to a shift in the typical cycle of demand for winter coats. Retailers may experience an unexpected surge in sales as customers begin purchasing winter coats earlier in the season than they used to. Similarly, there may be changes to the usual demand for summer vacation products due to factors like travel restrictions or changes in consumer behaviour. AI technology helps solve these issues by using machine learning algorithms that can process large amounts of data at once (and even use historical data from past seasons). This allows retailers to make informed decisions and predict upcoming demand to determine which products need restocking based on past trends without having to manually check every single product's sales history first—which would be quite time intensive!
In the future, we can expect to see even more developments and applications of AI in retail. AI may be used to automate more aspects of the retail process, from inventory management to supply chain optimisation. It may also be used to improve the in-store shopping experience by integrating AI into an in-store kiosk, providing personalised recommendations and assistance to customers as they shop. It’s important for retailers and customers alike to stay informed about these developments. By understanding the role of AI in retail, we can better prepare for the future and take advantage of the opportunities that AI offers.
---
## 9 Awesome Applications of Computer Vision
URL: https://www.elementx.ai/blog/9-awesome-applications-of-computer-vision
Published: 7 February 2023
Updated: 25 January 2024
**Nine industries where computer vision is delivering measurable ROI today: retail, manufacturing, healthcare, transportation, agriculture, banking, energy, food and beverage, and environmental monitoring.**
In recent years, computer vision has made great strides and has been applied across a wide range of industries to drive efficiency and revenue. But what are the most practical and important uses of computer vision today? From the meat industry to object recognition, automated form reading to data entry, we've put computer vision to work for our clients in ways that have helped them solve complex problems and improve their performance. We understand the unique challenges and requirements of these industries and use this knowledge to craft custom-tailored solutions that deliver results for our clients. To ensure the successful implementation of these solutions, we collaborate with a range of experts in the field of computer vision. One company that we work closely with is [SnapIT](https://www.snapit.group/), a team of specialist software developers and engineers, both in electronics and mechanics, create and produce live cameras, tracking systems, and satellite communication systems.
Based on our experience, we’ve compiled a list of key use cases for computer vision that are delivering a high return on investment (ROI) for businesses just like yours. From quality control in manufacturing to self-driving cars and medical image analysis, these use cases will give you a glimpse of what's possible with computer vision.
> 1\. Retail
One way that retailers can take advantage of their growing popularity is by using computer vision to provide a better customer experience. For example, clothing retailers can use computer vision to enable customers to virtually try on clothes, reducing the need for physical fitting rooms and increasing sales.
> 2\. Manufacturing
Computer vision can be used in manufacturing to improve quality control and reduce defects. The use of computer vision systems on assembly lines can identify defects in products, allowing real-time correction and eliminating manual inspections.
The use of computer vision systems on assembly lines can identify defects in products, allowing real-time correction and eliminating manual inspections. For example, if you're assembling a part that requires 100 screws, but you only put in 90 screws, the computer will instantly recognize this and alert you so that you can correct it before moving on to the next step. This saves time and money by reducing human error and improving workflow efficiency.
This technology has been used for years in other industries like aerospace and automotive manufacturing (and even construction), but only recently has it become available for use in consumer electronics like phones and tablets.
> 3\. Healthcare
The ability to detect diseases early and treat them quickly is critical for improving patient outcomes and reducing costs for healthcare providers. Computer vision algorithms can be used to identify signs of conditions such as diabetes or cancer before they become symptomatic. This can help doctors catch disease earlier and treat it more effectively, which would reduce hospital stays and help medical staff improve patient outcomes.
> 4\. Transportation
Computer vision can be used in transportation to improve safety and reduce accidents. In self-driving cars, computer vision systems can help identify obstacles and navigate roads safely. These systems are able to detect things like lane lines, traffic lights, pedestrians, and other vehicles. They can even identify objects that are not on the road, such as trees or signs. It does this with sensors installed on the exterior of the vehicle—like cameras and lidar—as well as sensors inside the car—like radar detectors and sonar. The computer vision system sends this information to a processor which uses machine learning algorithms to make decisions based on what it sees around it.
> 5\. Agriculture
In the agriculture industry, improving crop yields is one of the key ways to increase profit, and computer vision can help. For example, computer vision systems can be used to monitor crops and identify areas that need watering or fertilization, enabling farmers to optimize their use of resources. These systems are especially useful for large-scale farmers who have many acres of land to manage. They can also help with weed management by identifying areas where weeds have sprouted so they can be treated before they spread throughout the fields.
> 6\. Banking
In banking, computer vision can improve security and reduce fraud. Computer vision algorithms can be trained to automatically detect fake IDs or credit cards, enabling banks to prevent fraudulent transactions. They can also be used to ensure that account holders are authentic by comparing their face against a database of verified customers.
However, in order to train these algorithms, banks will need access to large amounts of data on real-world examples of both genuine documents and fake ones. This kind of data is very difficult for banks to collect themselves because it requires them to purchase large numbers of fake IDs or credit cards and attempt a variety of frauds against their clients. In order to make this type of training possible without breaking the law or endangering their customers' privacy, banks will need access to a huge database of such images that they can use for training purposes.
> 7\. Energy
Computer vision can be used in the energy industry to improve safety and reduce downtime. A computer vision system can be used to monitor oil and gas pipelines, enabling operators to identify and repair leaks before they cause damage.
This type of technology can also be used to detect anomalies in electrical equipment, such as problems with wiring or cracks in insulation. This enables operators to schedule maintenance before an issue becomes serious enough that it would require replacing parts or shutting down equipment for repairs.
> 8\. Food and Beverage
The food and beverage industry can benefit from computer vision to improve quality control and reduce waste. For example, computer vision systems can be used to automatically detect spoilage in fruits and vegetables, allowing producers to reduce waste and increase profits.
Computer vision solutions allow companies to capture images of food products and analyse them using machine learning algorithms. This process can be used to identify items that are too small or too large, or have an incorrect shape or color (for example, an orange that is too pale).
In addition, it is possible to use computer vision systems for more complex tasks such as identifying defects on the surface of a product (for example, a crack in an egg shell). The system can also identify whether a product has been damaged by handling during packaging or transportation; inspect parts for defects before they are shipped out; check if products are correctly packaged before being transported; or even visually inspect products after they have been assembled at the end of their manufacturing line.
> 9\. Environmental Monitoring
Computer vision is a powerful tool for conservationists, enabling them to monitor the environment and track changes over time. Using computer vision algorithms, conservationists can track the health of ecosystems and take actions to protect them by automatically detecting changes in plant or animal populations.
Computer vision has the potential to drive significant ROI in a variety of industries by improving efficiency, reducing costs, and increasing revenue. As the technology continues to advance, we can expect to see even more exciting and innovative use cases for computer vision in the future. If you’re looking to integrate computer vision into your business, schedule a chat with our team of experts [here](https://www.elementx.ai/contact).
---
## Chatbot Implementation: Our CTO's Blueprint for Success
URL: https://www.elementx.ai/blog/chatbot-implementation-our-ctos-blueprint-for-success
Published: 30 January 2023
Updated: 25 January 2024
**CTO Ming Cheuk's six-step checklist for planning a chatbot project, from defining the underlying problem and choosing a platform to training the model, monitoring metrics, and maintaining the bot once it is live.**
In today's fast-paced business environment, where time is money and customers are king, organisations need to stay ahead of the game to keep up. One way to do this is by embracing the power of conversational AI assistants, or chatbots. Chatbots can handle a wide range of tedious tasks, like answering FAQs and scheduling appointments, leaving your human customer service reps free to tackle the heavy-hitters. They are also available 24/7 and in multiple languages, so they can service more customers faster.
However, before launching a chatbot project, it’s important to have a clear plan in place. That's why a project checklist is essential for ensuring a smooth implementation and a successful deployment of your conversational AI assistant. In this blog post, our CTO, Ming Cheuk, provides a comprehensive checklist that will help you to plan and implement your chatbot project with confidence.
### **Step 1: Define the problem or opportunity that the AI assistant will address**
This step is like the foundation of a house: without it the whole project could come crumbling down. It's crucial to make sure that the assistant is designed to meet the specific needs of the business or users. To define the problem or opportunity, it's important to dive into business data and conduct customer research to gain a thorough understanding of the current situation and identify areas for improvement. This can include analysing customer feedback, sales data, and other relevant information to determine the specific pain points or opportunities that the AI assistant can address.
Ming considers this the most crucial step in the process. For example, when it comes to implementing a chatbot for customer service, it’s important to first understand the needs of your customers and your current customer service setup. Are customers waiting too long to get answers? Is it due to a lack of staff or a lack of information? This will determine what kind of chatbot is needed and how it should be built.
The key to utilising technology to its fullest potential is to figure out the problem and why you're implementing a chatbot in the first place. By clearly defining the problem or opportunity, the AI assistant can be tailored to meet the specific needs of the business or users and deliver real value.
Additionally, maintaining a chatbot can be a significant undertaking, and it may be more work to keep it up-to-date than to have a human handle certain tasks.
### **Step 2: Identify the desired capabilities and features of the AI assistant**
Once the problem or opportunity has been defined, the next step is to identify the desired capabilities and features of the AI assistant. It’s important to ensure that the assistant will be able to deliver the desired functionality and meet the users’ needs.
Some examples of desired capabilities and features include natural language processing, personalised responses, and the ability to integrate with other systems. It’s important to consider the business needs and customer expectations when identifying the desired capabilities and features of the AI assistant. Business needs include things like how much you can afford to spend on the project, what kind of functionality you want your chatbot to have, or what kind of customer support you want it to provide. Ming adds that you should also consider whether or not you want your chatbot to be able to answer a lot of different types of questions or just one type, if it's going to be used for marketing purposes or customer support, and how much time you expect it will take for people who aren't familiar with computers or technology in general to learn how to use it.
In terms of a customer service setup, it's also important to understand which parts of your customer service process a chatbot can help with and which parts still require human interaction. For example, a chatbot can assist with tasks like changing a password, but more complex issues may still need to be handled by a human.
As you implement a chatbot, it's important to make sure that if a customer needs to speak to a human, they can do so as soon as possible. Otherwise, they may become frustrated and feel invalidated. Additionally, make sure your staff is just as informed and in the loop as your chatbot, so they can effectively assist customers and troubleshoot any issues that may arise. Overall, by understanding your customer's needs and utilising technology and human support in the right way, you can create a chatbot that improves the customer experience and streamlines your customer service process.
This step is tricky because it's hard to predict how people will react to new technology. You'll want to talk to real people about what they want from a digital assistant — and that's not easy. It involves talking to customers, conducting surveys, and getting feedback from users.
Additionally, it is important to consider any technical constraints or limitations that may impact the design and development of the assistant. By identifying the desired capabilities and features, the AI assistant can be designed to meet the specific needs of the business or users and deliver real value.
### **Step 3: Research AI solutions and vendors**
The next step is to research AI solutions and vendors to find the best fit for the business. Ming recommends comparing different solutions and vendors based on their capabilities, pricing, and customer reviews. What may work well for one business may not be the best fit for another.
First, look at their capabilities. What kind of features does the solution offer? Is it capable of handling all your data or just some of it? Does it offer any other useful features that could make it more valuable to your organization? If you're looking for an AI solution that can help with customer service, you'll want to look for solutions that specialize in natural language processing and understanding
Next, compare pricing. How much does each solution cost per month? Are there any other fees or costs associated with using that particular product or service? If so, how much are they? Can you afford them?
Finally, check out reviews from previous customers. Not every company will have reviews online—some may even insist on confidentiality agreements—but if you can find some reviews from people who have used the technology before, they'll give you an idea about what kind of experience other people have had with this type of product or service before deciding whether or not it's right for your needs as well!
Ming suggests that when choosing a vendor for your chatbot, it's important to first consider your current situation and what you want a chatbot for. It's also important to think about how you envision your team maintaining and keeping the content fresh. Will you set up an internal team or hand it off to a vendor? This will determine if you need a self-managed platform or a managed solution. Some companies may prefer to build a chatbot internally while others may want to buy a pre-built solution.
Different platforms offer different levels of complexity, some are more business-friendly while others are more developer-friendly but offer more powerful features. It's important to evaluate the different platforms based on your company's needs and expertise. It's also important to consider your budget and resources when making your decision. Some platforms may be cheaper to buy/license but require more time, effort and resources to implement and maintain. Finding the right balance between the bells and whistles you crave and the budget you have on hand is critical.
### **Step 4: Create a plan and budget for implementing the AI assistant**
Now it's time to get down to business and plan out how to make your virtual assistant a reality. Just like any other big project, you'll need to have a solid plan and budget in place. It's essential to identify the resources and support required, such as data, hardware, and personnel. Without a plan and budget in place, it can be difficult to effectively implement and maintain the AI assistant, leading to wasted resources and potential delays.
Creating a plan and budget for implementing the AI assistant involves setting clear goals, timelines, and budget constraints. Think of it like a game plan for a football team: you’ll need to know what you're aiming for and how to score before you hit the field. Determine how the AI assistant will be used, how it will impact your business operations, and what resources will be required to achieve those goals. And just like a football game, you’ll need to keep track of the time, set a timeline for implementation and budget constraints - this will help ensure that the implementation stays on track and within budget. And lastly, always have a backup plan: include contingencies in your plan and budget for unexpected challenges or changes that may arise during the implementation process.
### **Step 5: Implement and test the AI assistant**
Now it’s time to create and test your AI assistant! This is where all the training wheels come off and the fun begins. You'll spend some time fine-tuning your AI assistant to make sure it understands and responds to user inputs like a pro. Ming says to think of it like teaching a new employee: you train them and test them before putting them in front of your customers. To train the AI, a dataset of sample utterances (phrases you expect the customer may say to the bot) is used to teach the model how to respond to different inputs. Once the AI has been trained, it can be fine-tuned by adjusting the utterances and responses and testing it with a smaller dataset to ensure that it is working correctly.
But the fun doesn't stop there! It's important to keep an eye on your AI assistant's performance, just like a boss checks in on their employee's progress. This includes tracking metrics such as time to resolution, accuracy, and user satisfaction. By monitoring these metrics, you can identify areas where the AI needs to step up their game and make adjustments accordingly.
Additionally, regular testing and updating the AI with new data can help to keep it up to date and improve its performance over time. Many companies have implemented chatbots, but not all of them do it well. As artificial intelligence rapidly evolves, this is likely to change. In a year or two, chatbots may be able to handle more complex questions with more specific responses, ultimately providing a better customer experience. It's important to stay informed and adapt as needed to ensure that your chatbot is meeting the needs of your customers and providing value to your business.
### **Step 6: Continuously improve and expand the use of the AI assistant**
Just like maintaining a website, a chatbot also needs to be updated with new information, and either your internal or external team should be responsible for this. This includes incorporating new data and adjusting its capabilities to ensure that it is meeting the changing needs of the business and users. As technology and customer needs continue to evolve, it's important to update the AI assistant to ensure that it stays relevant and effective. Not only that, it's important to monitor the performance of the AI assistant and make adjustments as needed to improve its accuracy and functionality.
A great example of this is the bespoke chatbot we built for the University of Auckland. They have a dedicated team that looks after the chatbot, ensuring that it is meeting the needs of their customers and providing value to the university. This is important to note as one of the misconceptions around chatbot is that once it is set up, it will automatically learn about the company and be able to handle all customer interactions on its own. This is not the case (at least not yet) - it needs to be trained and managed by staff.
By having a dedicated team that looks after the chatbot, the University of Auckland has been able to effectively implement and maintain a chatbot that improves the customer experience and streamlines their customer service process.
The potential for the AI assistant to help the business grow and evolve in the future is huge. Not only can it help to improve customer satisfaction and reduce costs, but it can also open up new opportunities for growth and expansion. With the ability to analyse large amounts of data and make decisions quickly and accurately, the AI assistant can help businesses to identify new trends and opportunities, and make better-informed decisions. Additionally, the AI assistant can help to automate repetitive tasks, freeing up employees to focus on more strategic and high-value activities.
Having a checklist in place before you get started provides a clear and actionable plan that outlines the steps required to build a successful chatbot. It helps to ensure that the AI assistant is tailored to meet the specific needs of the business and users, and that it is implemented in a way that maximises its potential to deliver real value.
With all that said, the AI assistant is not just a tool, but an enabler that can help the business to stay competitive and adapt to the ever-changing business environment. However, it is important to remember that the planning process is just as vital as the implementation in order to ensure a successful outcome. If you're interested in learning more about how an AI assistant can benefit your business, don't hesitate to connect with Ming or any of our other AI experts for more information and help getting started on your AI journey.
---
## AI Governance Series: To Build or to Buy?
URL: https://www.elementx.ai/blog/ai-governance-series-to-build-or-to-buy
Published: 23 January 2023
Updated: 25 January 2024
**How boards should approach the build-or-buy question for AI, covering trade-offs around speed, control, talent, maintenance, and capex versus opex, plus the often non-binary middle ground of building on top of pre-trained models.**
So you and your fellow directors are moving towards sign-off on an exciting project that will use Artificial Intelligence to solve some exciting challenges. Given that it is relatively unlikely that your board will have deep technical skills in this new and fast moving technology - one of the aspects of a decision that you should consider carefully is the tricky question of whether to build or buy. (Tailor made v roll-your-own in the old jargon). Here we consider some good questions to ask and some context against which you might weigh the answers.
In a general sense, (like many decisions before the board) your internal proposer or sponsor for the project should provide a reasoned analysis of the pros and cons of each available choice. Also very useful here would be a simple analysis provided by a friendly (read neutral and trusted) expert who can point out the key considerations.
**Beware the subtle differences**
As mentioned already in this series, an AI project is not necessarily the same as your previous and more familiar technology projects. A good example of this through a build vs. buy lens is the impact of the speed at which the AI frontier is moving. If the main reason being offered to take on the build internally is the fact that there isn’t currently a solution available in the market, then either look into the most analogous products and ask the vendors or ask your team some questions about how soon one might be available. Or both - reassure yourself they have looked widely.
Consider [Generative AI](https://techcrunch.com/2022/10/28/generative-ai/), which is hot right now, attracting investment and showing real leaps in performance of large models. As I write this, Open AI has released [chatGPT](https://openai.com/blog/chatgpt/) and a sibling technology called InstructGPT. These are both examples of large model generative AI and can in many cases create a very useful piece of prose, article or other copy from a simple instruction or prompt. (No - I am still old school and am writing this myself). I point it out simply because what seemed more or less impossible - or more to the point not viable - only a couple of years ago is now coming quickly to general availability. So if the main plank of an argument presented to you and your fellow directors to build internally rests on the fact that there isn’t a viable option to buy now, at least ask for some evidence or analysis to show that the competitive advantage or moat the organisation intends to create won't be rendered obsolete around the time the project completes.
**Trade offs to consider**
Like many investment decisions, it cannot always be about hard data and numerical analysis. Sometimes it is just a matter of balancing various trade-offs. Here are a few you will encounter when considering whether to build your AI yourself or buy (rent) it. By examining each lightly, I hope this will inspire some questions you can ask while considering any proposal.
**Fast v Slow.** This is about the speed of delivery of your project. Is time to value critical? In most cases, designing and building your own models will take longer than if you are able to access and use an open source or licensed model for your purposes. Consider the [important stages](https://www.elementx.ai/post/avoiding-roadblocks-to-adoption) in a project and how long it might take to navigate them successfully. Using available tech probably won’t eliminate any of these steps, but some of them might happen much faster.
**Control v Constraints**. Deploying Artificial Intelligence for a specific outcome can seem to be a very unique exercise, especially at the sharper edge of technology progression. This can mean the design of a unique set of features. In an ideal world, it would be easy to create custom applications for your organisation and also easy to continue to tweak and add to them as you learn and as customers demand more. If the unique requirements are the main drivers for a build approach, then be sure to test this with some questions that qualify the assumptions. If there is an existing product available that is close to your requirements but lacks the features you seek, might the vendors be open to adding what you need or might they already have it on their road map for development? Footnote : If you are promised features on a road map in a sales process - get it in writing!
**Resource Heavy v Light**. This is a ‘how many engineers’ type of consideration. There will be a mix of important skills in a successful AI project - not least of which may not be engineers but people with strong domain knowledge and know-how inside your organisation. Of course a decision to build is likely to include external suppliers of talent and tools. Separately, the credentials and track records of external suppliers must be surfaced and checked. But the main question here is about whether your project will have access to the talent needed for a build process, or would a buy option be lighter in its requirements?
**Maintenance - Core v Context**. Here you are asking about the skills needed to maintain a solution once completed and for as long as it is in use. While one of the key propositions of Artificial Intelligence is that the machine can learn, there is still an important set of maintenance requirements during the life of an AI project. Models need tuning for improvements in performance. They have ongoing training needs and will need additional design and production if your organization seeks to turn it to new uses in future (the best kind of value extraction from the asset). As touched on elsewhere, there will be needs arising to integrate your models into existing technology and workflows. So your first question might be - what are the talent requirements for maintenance over the first few years of the project’s life and has that been factored in?
As a side note, we have found more than one instance where an AI project has been stood up within an organisation - but the main protagonist has moved on to a new organisation leaving behind no one who has a clue how to manage or change the solution - resulting in someone deciding to throw the switch on it.
**Opex v Capex**. When GPT3 was released by OpenAI. An [article](https://venturebeat.com/ai/ai-machine-learning-openai-gpt-3-size-isnt-everything/) at the time provided an estimate of the cost to train the model - at around $12m USD. That is a lot of compute resources and a significant capital investment in the product. But companies able to make use of the resulting model can do so at a micro cost level by comparison as an expense. The usage fee is based on [tokens](https://chengh.medium.com/understand-the-pricing-of-gpt3-e646b2d63320), which can be complex at first. This kind of comparison may not be as simple for your project but is worth considering if your proposed project is an internal build and will incur significant capital expense.
**The cost of waiting**. If your organisation waits for the technology to be available off the shelf for the solution you seek, then what are the opportunity costs? Will a competitor or disruptor enjoy significant market advantage while you wait? Waiting for the latest model of smartphone might mean you get the most features when you do buy, but if you put it off too long you have a period where you cannot access the utility available in the model you choose to pass on.
This will be a more subjective judgement for your decision process - but the answers should lie within the information you have regarding the anticipated benefits (financial or otherwise) of the AI project you are considering and the time frame over which they will be available.
It is also worth mentioning that many ‘traditiona’l IT platforms are building AI into their products. For example; Service Now, Intercom and Hubspot, all have virtual assistant platforms baked in now. It is probably easier to use their one - which is tightly integrated to their system already, than to try to build a bespoke integration - unless you have very special requirements.
**Non-binary**
For an AI project, the build vs. buy part of the decision may not be a simple binary one. The general concept of platforms and pre-trained models being developed and provided by larger vendors with the financial and human capital to do so means that often the ‘build’ component of a project specific to your organisation can be a final layer, built on top of these existing models and providing the unique use you need.
This is often the compromise reached out of necessity, due to the high cost and long lead times to build models from scratch. Good design can mean an underlying platform can be swapped out for lower cost of better performing alternatives later. But a final layer of this type is still a build, so the questions and critical thinking needed during the decision process remains.
##### In addition to his position as Executive Chairman of ElementX, Richard McLean has over 20 years of experience helping New Zealand businesses tackle growth challenges and bring new products to market.
Richard's AI Governance series can be found here:
1. [An introduction: Does Artificial Intelligence deserve a place on the board agenda?](https://www.elementx.ai/post/including-ai-on-the-board-agenda)
2. [Consideration of key stakeholders in the board decision making process](https://www.elementx.ai/post/ai-governance-considering-stakeholders)
3. [Preparing for and avoiding roadblocks to adoption](https://www.elementx.ai/post/avoiding-roadblocks-to-adoption)
4. [Keeping regulation and compliance concerns front of mind during AI project planning](https://www.elementx.ai/post/ai-governance-series-keeping-regulation-and-compliance-concerns-front-of-mind-during-ai-project-planning)
##### Subscribe below to be the first to know when new posts are published, or [follow Richard on LinkedIn.](https://www.linkedin.com/in/richardmcleannz/)
---
## Looking Ahead to 2023: AI Developments our Founders are Watching
URL: https://www.elementx.ai/blog/looking-ahead-to-2023
Published: 18 December 2022
Updated: 25 January 2024
**Founders Ming Cheuk, Daniel Xu, and Richard McLean on the AI developments they were watching for the year ahead: foundation models, generative AI in production, AI in defence, and the democratisation of access for smaller organisations.**
###### In 2022, we had a lot of fun working with our clients, learning about their businesses, and bringing them innovative ideas that will help them make their operations more efficient. As we wrap up another year and look ahead to the future, the team at ElementX has been reflecting on the state of AI and what it means for industry advancements in New Zealand and beyond. With so many exciting developments and advancements happening in the field, it can be difficult to keep track of it all. That's why we decided to sit down with our three founding members and ask them: what AI developments will they be watching the most closely in 2023? Here's what they had to say.
**Ming Cheuk**:
_I’ll be looking at generative models and how the public reacts to them. I remember back in 2019 when we encountered the first GPT models. It was impressive in what it could generate but not very useful. Fast forward today - generative language models like InstructGPT (GPT-3.5) and its sibling ChatGPT are finding their way into all sorts of applications due to their capability of generating reasonably sounding and fairly accurate outputs from the user’s instruction. The decision for OpenAI to include the public in ChatGPT with the easy-to-use interface has suddenly made a lot of people who aren’t in the AI space realise how far things have come since the first chatbots of 2017 (when we first started in chatbots)._
_They’re also reaching a point where they’re good enough for people to be concerned, and for good reasons. The outputs are becoming so convincing that it’s really hard to differentiate whether it was generated by AI or a human. This can create problems for sites like Stack Overflow (who recently banned ChatGPT answers) because it can create_ [_legitimate sounding answers that may not necessarily be correct_](https://www.deeplearning.ai/the-batch/issue-174/)_. And due to the speed at which it can be generated, their community review team can’t keep up._
_Regulation may also come in to manage the proliferation of AI generated content, due to the potential harm it can bring._
**Daniel Xu:**
_Something really cool is that over the last few years the use of AI has become more accessible to nontechnical people through the development of no-code, drag-and-drop, and prompt-based engineering tools. This means that anyone, regardless of their technical background, can create solutions using AI. In the next few years, it’s likely that we will see even more tools and platforms emerging that allow nontechnical people to build with AI without having to study for four years. This is really cool because it opens up the potential for more people to take advantage of the benefits of AI and create innovative solutions._
**Richard McLean**:
_I’ll be watching several aspects closely. For one, I am personally concerned about how it is finding its way into the defence sphere and how current conflicts around the globe are seeing the introduction and testing of AI based weaponry. So I will be trying to keep an eye on the publicly available information about this._
_Closer to home, my interests are more commercial. I am particularly interested in new commercial deployments of different types of Artificial Intelligence here in New Zealand. There are so many possible use cases, but limited capital - human and financial available to deploy._
__
###### With the rapid pace of change in the AI industry, it's important for businesses to stay on top of the latest developments and consider how they can take advantage of new opportunities. One recent example of this is OpenAI's release of ChatGPT, which has opened up new possibilities for businesses to incorporate AI into their operations. In thinking about the year ahead, our three founding members have identified a number of opportunities in AI that businesses should consider in 2023.
**Ming Cheuk**:
_There is a lot of power in foundation models. These are large AI models trained on a giant amount of data (often internet scale) which have generalized enough knowledge to be used for various downstream tasks. GPT-3 is a perfect example - the same model can be used for traditional NLP tasks such as sentiment analysis, text classification, intent matching, etc and often better than traditional models due to the vast quantity of knowledge it has ingested during training time. Stable Diffusion (images), Whisper (speech to text) are also examples of foundation models. I think that businesses will begin to leverage these models in situations where they would never have enough data to train a quality model, particularly in the contact center where there is lots of power in parsing customer queries but the data available to train a model is of poor quality (skewed, transcription errors, etc). It will allow them to realize the value of AI much quicker than before_**_._** _There is also much less capital expenditure required to leverage a foundation model._
**Daniel Xu:**
_One potential use for chat-based AI systems like GPT-3 is as an intermediary step between fully automated customer service and human responses. While many businesses may be hesitant to completely replace their customer service teams with AI, chat-based systems could be a useful tool for keeping customers engaged and entertained while they wait for a human representative to become available. Businesses could also use chat-based AI as a way to brainstorm and generate new ideas, asking it questions and using its responses to spark creativity and come up with new solutions._
**Richard McLean**:
_It seems to me that some of these new large model releases are opening the door to small business innovators to experiment with the technology. It has previously been noted that there is a risk that significant commercial benefits from AI will accrue mostly to larger organisations who can afford to design and build complicated models. Just last week I saw an example where a small business owner had experimented with ChatGPT to build out several AI generated tools for marketing. This was a great example of a move towards democratization of access to the benefits. I hope to see more of the same opening up in 2023._
###### One of the things we were most interested in asking Richard, Dan, and Ming about was what they were looking forward to most in 2023. As pioneers in the tech industry, they have a unique perspective on the potential developments and advancements that are on the horizon. We asked them what they're most excited for in the coming year - both within the AI industry and beyond.
**Ming Cheuk**:
_I’m particularly excited about the proliferation of AI in products and services. People have been talking about the democratization of AI, and I think foundation models, particularly open source versions, put the power in startups and smaller organizations' hands. What was too costly and time consuming to develop, and probably even infeasible in terms of accuracy 2 years ago should hopefully be unlocked by foundation models. Seeing a cleaning business leverage ChatGPT to speed up their operations is just the start of it. There is also an increasing number of companies and organizations looking to own the generative space - the generative AI arms race has begun and is going to continue in 2023._
__
**Daniel Xu:**
_Getting our own office space again. One of the best parts of the last year has been the opportunity to do cool events together and to just hang out and have fun as a team, so I’m looking forward to being able to do that more often in 2023._
**Richard McLean**:
_My favourite would be an end to Covid restrictions, but there will likely always be a need to protect the vulnerable section of our community - so that might be more of a wish._
_On the AI frontier I am looking forward to better customer experiences delivered at scale. As a customer of many large service organisations, (including Government services) I personally shy away from some tasks simply because I know the experience is going to be an unhappy one. (like having to listen for ages to hold music so loud it is horribly distorted) so I hope to see within 2023 some of those experiences made faster and more enjoyable._
_Also on the AI front I look forward to seeing more boards and execs engaging more deeply in understanding the technology and how it might impact their business models. Our offshore competitors are investing and NZ has to move fast to ensure not being left behind._
######
###### We can't wait to see what 2023 brings in terms of new and exciting AI developments. Our team is constantly reviewing and implementing the latest and greatest developments in our field, and we want to make sure you don't miss a beat. If you're as passionate about staying up-to-date as we are, be sure to subscribe to our newsletter. We'll keep you informed on all the latest trends and advancements, and together we can stay at the cutting edge of innovation.
---
## AI Governance Series: Keeping regulation and compliance concerns front of mind during AI project planning
URL: https://www.elementx.ai/blog/ai-governance-series-keeping-regulation-and-compliance-concerns-front-of-mind-during-ai-project-planning
Published: 12 December 2022
Updated: 30 April 2026
**As AI continues to become more widespread in our daily lives, it is important to consider the regulatory and compliance requirements associated with such projects.**
As I write this, media in New Zealand this week have been talking about the use of facial recognition technology in big retail. This follows closely on the heels of a very similar situation across the Tasman involving consumer protection groups and the retail sector holding very different opinions over what is an acceptable use of artificial intelligence. In the Australian case a [complaint was filed with the regulator](https://www.reuters.com/technology/australian-retail-giants-targeted-facial-recognition-tech-complaint-2022-06-27/) which led to one of the three retailers voluntarily pausing their use of the technology while there is an ongoing investigation by the regulator.
This week's local story shows a similar set of concerns being raised, but confined to a [discussion in the media](https://www.nzherald.co.nz/nz/foodstuffs-use-of-facial-recognition-technology-raises-significant-privacy-and-ethical-concerns-consumer-nz/4XVG4T2GF5CD7HNC3DBQEYKXFI/) with no mention of investigation by regulators. It is easy to see the motivation on both sides of this particular debate, especially with retailers currently seeming to face more and more theft and personal security issues on a day to day basis.
This is a challenging balancing act for any organisation looking to use these advanced AI technologies to solve real world challenges. The focus of today's discussion though is on Compliance and its close twin - Regulation. Elsewhere in this series we discuss Ethics and [Stakeholders](https://www.elementx.ai/post/ai-governance-considering-stakeholders) in AI through a Governance lens.
In the case highlighted in Australia - the complaint to the Office of the Australian Information Commissioner (OAIC) is reported as based on concerns about 'unreasonably intrusive use' of technology and _potential_ breaches of privacy laws. It seems the OAIC has yet to reach a determination on the specific complaint raised, but has issued guidance on compliance with privacy law as well as recommending retailers consider customer and community expectations as well as the impact on their privacy.
Another hot topic causing interest and debate at the moment is that of AI Generated content and the [potential issues of copyright](https://www.siliconrepublic.com/machines/ai-generated-images-legal-risks-copyright). Whilst generative AI models assert care has been taken to avoid copyright infringement, their terms of use also usually include a disclaimer of liability. It seems more likely that someone using an AI generated image that raises copyright issues will be the party answering any action taken as a result.
This certainly means taking a little care to ensure originality or right-to-use before including AI generated content in anything likely to be construed as commercial use.
Whilst legal compliance and more optional obligations (like considering community expectations) are often mentioned together in this context, what does become obvious is that regulation is lagging well behind the development and deployment of AI technology in most markets.
**For directors, it is then helpful to consider compliance in two parts;**
1. What are the legal obligations of the organisation intending to deploy an AI solution now?
2. What are they likely to be within the next few years, as the anticipated successful return on a project is delivered?
The first requires a little research and understanding, the second more of a 'crystal ball' approach.
At present in New Zealand the most obvious risks of non-compliance or infraction stem from privacy issues. Many useful AI applications are built using data sets that include personal information or data that can be used to determine identity - albeit strictly for the purposes towards which the AI project is designed. A board has obligations to ensure that privacy laws have been adhered to during the design and build of the project. They also have obligations to ensure that any of this type of data collected is kept secure from unauthorised access or use. These are the obvious obligations or 'low hanging fruit', but there will be more depending on the type of AI and use case being considered.
A little less obvious, but by no means obscure is the issue of discrimination. In New Zealand, compliance is largely governed by the Human Rights Act. Under this act it is illegal to discriminate against people on several grounds, including things like Gender identity, Race, Age and Ethnicity. Consider the process of employment. The early stage of a recruitment process is a rich area for automation using AI. Training a model to read large quantities of CV's and reducing them to a shortlist for consideration by a human is a very compelling investment (Especially to those who have had to read 50 or 100 CV's before having the model).
But imagine bias being subtly but surely 'baked in' to the model as it is designed and then trained. The result might be a sleek recruitment process, but emerging patterns (or even preserved patterns) of discrimination at the early filtering stage of the process. If the model is automatically discriminating, then the organisation is in breach of the legislation - now just doing it faster and at a larger scale. On top of this, from the board's perspective there will be a lack of diversity in the organisation - growing undetected at first.
**The crystal ball**
For any director concerned enough with potential future compliance issues it would probably help to look into the legislation which is a work in progress in the EU jurisdiction. Currently named [The AI Act](https://artificialintelligenceact.eu/), this is a body of legislative regulation which is intended to become law in the EU. I recommend that any directors considering significant investment into AI look into it on one simple premise - it is probably a reliable indicator of any legislative framework coming into force in New Zealand. When GDPR was signalled and then introduced in the EU, operating businesses there out of New Zealand forced me to understand the new act and how best to comply. It certainly had teeth. But the compliance was a worthwhile investment that made it easier to meet NZ privacy laws as they were updated later.
This new act, (a work in progress) contemplates fines as one means of enforcement and they are potentially significant in the tens of millions of Euros, sometimes based on a percentage of world-wide annual turnover. The act will apply 'extraterritorially' to any application, product or service that reaches the EU market. It will be risk-based and would explicitly ban specific uses of AI - for example general purpose social credit scoring or exploiting vulnerable community groups. It will contemplate high-risk uses like employment process, law enforcement and border control. The Act document is over 100 pages and a dense read. There is a helpful summary [published here](https://datainnovation.org/2021/05/the-artificial-intelligence-act-a-quick-explainer/) which offers a quick explainer and offers advice on how to consider the requirements.
**So where to start?**
For lovers of detail and deep dives, the same web site that publishes the current version of the EU Act has various tools including an assessment tool called [Cap AI](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4064091) developed by University of Oxford researchers and designed to help organisations understand how they might assess compliance with the developing Act. The tool follows an ethics-based auditing approach to AI Governance and has a simple way of illustrating an approach that links compliance, technical diligence and ethics together.
It is a reference from a separate study that shows how finessing ethical issues is easier once a solid legal compliance basis is set and then a technically robust use of AI is designed.
For those who prefer a simple approach, asking questions of a proposed project or just during a discussion about AI in an organisation towards compliance is a great start. Privacy requirements provide an obvious place to begin, then further requirements like non-discrimination can follow. If an issue arises and is unclear, most legal advisory firms will have a partner who specialises now. There are specialist advisory firms like [Simply Privacy](https://simplyprivacy.co.nz/) or [Kindrick Partners](https://kindrik.co.nz/) available. Or you could suggest a workshop session using AI specialists for the board to better understand these implications.
The compliance bar seems a little low here in NZ as is probably the case with many other markets as discussion and formation of public policy lags behind the development of the technology. But it does seem sure that this bar will rise as this changes. For some, being ahead of this change through understanding the requirements will be a wise choice.
##### In addition to his position as Executive Chairman of ElementX, Richard McLean has over 20 years of experience helping New Zealand businesses tackle growth challenges and bring new products to market.
Richard's AI Governance series can be found here:
1. [An introduction: Does Artificial Intelligence deserve a place on the board agenda?](https://www.elementx.ai/post/including-ai-on-the-board-agenda)
2. [Consideration of key stakeholders in the board decision making process](https://www.elementx.ai/post/ai-governance-considering-stakeholders)
3. [Preparing for and avoiding roadblocks to adoption](https://www.elementx.ai/post/avoiding-roadblocks-to-adoption)
##### Subscribe below to be the first to know when new posts are published, or [follow Richard on LinkedIn.](https://www.linkedin.com/in/richardmcleannz/)
---
## AI Governance Series: Preparing for and Avoiding Roadblocks to Adoption
URL: https://www.elementx.ai/blog/avoiding-roadblocks-to-adoption
Published: 27 November 2022
Updated: 30 April 2026
**Part three of the AI Governance series. Three roadblocks that quietly kill AI projects: data preparedness, organisational culture, and change management, plus the questions a board should ask before greenlighting a project.**
_This image is not a photo: it was generated using DALL E 2. DALL E 2 creates realistic images and art from a natural language description, and it's just one of many examples of disruptive AI that is becoming more and more mainstream._
_This is part three in our series on AI Governance. Visit part_ [_one_](https://www.elementx.ai/post/including-ai-on-the-board-agenda) _or part_ [_two_](https://www.elementx.ai/post/ai-governance-considering-stakeholders)_._
Artificial Intelligence is more of a frontier than a technology. That is to say, the term covers a wide range of applications and underlying types of machine learning models, other software models and hardware integrations that provide sources of data. Like any frontier of old, there will always be a few 'pioneers' who fall victim to the many hazards associated with new or uncharted territory.
With such rapid advances in research and development, this frontier is [expanding rapidly](https://www.elementx.ai/post/including-ai-on-the-board-agenda). Whilst it is certain that developments in AI will continue to advance and improve the delivery of goods and services in marketplaces everywhere – what is much less certain is the success of individual AI projects, even if the technology is already proven.
For any organization, it can make good sense to consider a 'proof of concept' styled approach to any Artificial Intelligence project. This is a well proven approach to testing the value of investing significant time and money into the full production and integration of a solution. The theoretical progression is something like;
Of course, this is a simplified view of what can be a complex series of decisions. For a Governance team though, it is helpful as a simple way in which to consider several key concepts, namely;
1. That 'into production' is a general term reflecting the phase after a POC and where the model is built out and integrated to the organisation's workflow and existing technologies.
2. That advances in AI development - especially where providers are large and well-funded – are allowing faster and easier POC development. This by giving access to AI platforms and reducing the need for R&D into building the models themselves. Essentially this means often a POC can be a layer on top of an existing model that had already been trained on huge datasets. Examples include GPT3 from OpenAI, which has pretrained models that mostly work 'out-of-the-box' and [Google's AutoML](https://cloud.google.com/automl) where customers 'bring their own data'. More locally, Arcanum have released a [platform](https://www.arcanum.ai/ml-accelerate-platform) to allow businesses to accelerate their ML projects.
3. That just because a particular application of Artificial Intelligence shows promises of delivering new competitive advantage or differentiation to the organisation, it doesn't mean that advantage will materialize once a project is completed. There are a few potential roadblocks for boards to be aware of and ask about early on.
4. That a concept may be technically feasible and proven to work from a pure technology point of view, but not be practical to implement for an organization.
The point of this article is not to warn boards off AI projects, rather quite the opposite. The point is to be aware of and consider the potential roadblocks, internal and external as early as possible and thus significantly increase the chance of a successful POC and perhaps more importantly, the chances of the project making it into production. Current statistics reveal [nearly half don't make it](https://venturebeat.com/ai/new-gartner-survey-only-half-of-ai-models-make-it-into-production/), so it is worth the time spent early to de-risk where possible.
### **Preparedness**
Preparedness is definitely a thing when it comes to AI projects. Often, they ingest large amounts of data (especially in the training phase) and they require access to specific types of data. If the data needed is not readily available and accessible, then this can become an early roadblock that slows or stops a project, which happens more often than might be obvious. To generalize terribly, large organisations are more likely to have achieved a stage of digital maturity and therefore are likely to have more useful data for a project, but if that data is widely distributed in many formats and therefore difficult to access and use – then there is going to be a significant requirement to prepare it. Understanding the downstream requirements before commencing a POC is healthy practice, as is establishing sound data governance (a [separate subject](https://www.techtarget.com/searchdatamanagement/definition/data-governance) in its own right).
Questions worth asking on this front are:
- Where does the data (training data or data feeds for the project itself) needed for the POC reside and do we have easy access to it?
- If the project progresses to production, which types of data will it need and again, is it accessible?
### **Culture**
Whilst the benefits of a successful venture into AI may be obvious to sponsors and a board, the impact on people and culture may not. Considering [stakeholders](https://www.elementx.ai/post/ai-governance-considering-stakeholders) and the organisation's purpose is an important step, but so too is being sure to understand internal views and beliefs held towards the technology.
If there are personally held concerns about the introduction of AI technologies or even wider beliefs that AI is not a good thing, then this will significantly impede the success of your project and can be a significant barrier to adoption when it comes to being in production. The complete opposite can be an impediment too. Unrealistic expectations of AI - such as 99% accuracy for an out-of-the-box model applied to a new process can be a poor start for any project. An internal survey or informal team discussions can help a board understand where attitudes lie.
### **Change management**
In a similar vein, the concept of change management as it applies to embedding new technology is important. For directors, understanding the likely changes in workflow or systems used for people and how they will be assisted through these changes matters. These existing technology systems and structures create inherent boundaries and constraints, which will need to be overcome.
Good questions to ask around this include;
- Once this anticipated approach to AI reaches the 'production' stage, which of our existing technology systems will it need to integrate to?
- How will the project change the way our people use these systems and how they work?
- Has any work been done to understand these facets of the project?
If the board is looking at a proposal simply on the merits of the technology and don't have access to at least some investigation and understanding of these potential roadblocks to success then it is very likely worth asking for this to be done before committing.
##### In addition to his position as Executive Chairman of ElementX, Richard McLean has over 20 years of experience helping New Zealand businesses tackle growth challenges and bring new products to market.
Richard's AI Governance series can be found here:
1. [An introduction: Does Artificial Intelligence deserve a place on the board agenda?](https://www.elementx.ai/post/including-ai-on-the-board-agenda)
2. [Consideration of key stakeholders in the board decision making process](https://www.elementx.ai/post/ai-governance-considering-stakeholders)
3. [Preparing for and avoiding roadblocks to adoption](https://www.elementx.ai/post/avoiding-roadblocks-to-adoption)
##### Subscribe below to be the first to know when new posts are published, or [follow Richard on LinkedIn.](https://www.linkedin.com/in/richardmcleannz/)
---
## 12 Use Cases for Virtual Assistants that are Helping Businesses Drive Customer Satisfaction Around the World
URL: https://www.elementx.ai/blog/12-use-cases-for-virtual-assistants
Published: 21 November 2022
Updated: 25 January 2024
**A walkthrough of twelve real-world chatbot deployments, from Domino's pizza ordering to the University of Auckland's student service bot, showing how virtual assistants are reshaping customer support across industries.**
Chatbots are the future of customer service. They're fast, they're accurate, and they can help customers with frequent queries while saving time for your customer service team to handle more complex requests. Customers expect this kind of automation everywhere they go these days; with so much information readily available at our fingertips, why should people have to sit on hold for 30 minutes just to ask about something simple like shipping times? Chatbots are able to handle these types of questions without any wait time at all.
The technology is relatively new, but it's already proven itself as a way for businesses to interact with their customers in a more efficient way than ever before. And as more businesses adopt chatbots, we'll see more and more customers expecting them everywhere—including your business!
Below, we'll look at how businesses like Dominos, University of Auckland, and Telstra have used chatbots for their own business purposes. If you're thinking about using a chatbot to engage with your customers or improve your business processes, these examples will give you some inspiration and ideas.
### 1\. Dominos
With so many ways to order food, creating an ordering system with the lowest friction gives businesses an edge over the competition. With Dominos' Facebook Messenger chatbot, customers can place an order in just a few seconds—and get updates on delivery progress as it happens.
The bot uses artificial intelligence to remember previous orders, which means you never have to type in your address, contact details, or even your order again!
### 2\. HLC
When you’re in the business of making customer support a priority, you have to be able to answer your customers—and they’ll never stop asking questions.
HLC, a leading distributor of bicycle parts, was looking for a live chat solution that would help them better categorize customer support requests, provide online support for an extensive and highly technical product line, add more customer communication channels beyond phone and email, and solve technical issues and answer product/shipping questions in real-time.
Since implementing Acquire Live Chat, HLC has achieved a nearly 100-percent live chat response rate, onboarded new hires quickly, and gained greater operational visibility into business and product issues.
### 3\. Glasses USA
The Glasses USA chatbot streamlines the process of ordering glasses online - a task that has historically been limited to in-store shopping experiences. It can help you determine what style of glasses would best fit your face, and it can also give you an estimate on how much your new frames will cost. The chatbot has recommended questions to get you started, and can help guide customers through returns, insurance claims, and more.
### 4\. ACCES Employment
Over the past few years, ACCES has been developing a new virtual employment and resource assistant to help them address the growing demand for their services. After development was complete, ACCES named the agent VERA—an acronym for Virtual Employment and Resource Attendant.
VERA addresses multiple areas of service, 24 hours a day, seven days a week, without human intervention. She can answer FAQs for jobseekers, employers and volunteers; refer users to specific ACCES programs and services; facilitate registration of users into workshops and events that match their interests; surface direct links to job-search resources such as resume templates, articles, videos and web content; automate email flows to jobseekers; support registered clients; populate Salesforce CRM with user profiles for staff to pursue as leads; increase capacity to assist jobseekers.
### 5\. Telstra
To better serve its 28,000+ employees, Telstra sought a smarter way to manage internal queries and self-service functionality. The result is Darcy, a virtual assistant that helps Telstra employees with their HR queries, such as ‘How can I check my vacation hours?’.
Darcy can walk the user through a series of questions and answers, refining the initial query and moving closer toward a satisfactory answer. If Darcy can’t answer the query directly, it can pull up relevant content from a knowledge base. Failing that, if Darcy cannot resolve a query or if it is sensitive in nature, it will hand off to an appropriate human agent within HR.
### 6\. University of Auckland
Driven by a need to respond to spikes in activity around exams and enrolment, we built a bespoke AI chatbot for the University of Auckland using [IBM](https://www.linkedin.com/company/ibm/) Watson's powerful NLP platform. The result? Faster resolution times, and greater capacity for the Student Centre team to respond to more complex issues.
By integrating the University of Auckland chatbot with student systems, the university is able to offer immediate answers tailored to individual students, as well as providing enrolment information and assisting them with administrative tasks.
### 7\. The Dufresne Group
In the wake of COVID-19, The Dufresne Group —a premier Canadian home furnishing retailer— needed to adapt. No longer could they sell furniture the old way; they needed a new platform that would allow customers to connect with them wherever they were, all while still providing exceptional customer service when they needed it most.
They realized that the best way to solve this problem would be by using a live chat platform like Acquire (a company which provides live chat, cobrowsing, and chatbots). With this technology in place, customers can shop from the comfort of their home through video tours of furniture, or have basic questions covered by their 24 hour chatbot that can capture contact information even when the team is out of office.
### 8\. DEWbot
As the world of streaming continues to grow, so does the need for brands to find new and innovative ways to engage with their audiences. For Mountain Dew, this meant creating a fully customised chatbot that could be integrated with their Twitch channel – the first-ever branded chatbot on the platform.
DEWbot is a chatbot that engages with the Twitch community in real time and collects data to provide insights previously unavailable from the platform.
The result? A 550% increase in in-stream conversation during the promotion; a 265% increase in Mtn Dew Twitch fans; 572% increase in channel engagement; 190K unique in-stream viewers; 11.6K hours of branded content watched; 1,270% increase in the Average Viewers Per Stream benchmark; 48 influencers called out with 86K estimated total views.
### 9\. Southern Cross Health Insurance
Aimee is a Digital Human we built on top of UneeQ's digital human platform. Aimee is trained to recognise the most popular questions that are asked in the insurance industry and give an appropriate answer, reducing waiting time for customers calling into the call centre. She is also able to remember what she’s told each customer so that she can respond with more personalised information next time they contact her.
The outcome? In only 3 months, Aimee had 7000 conversations with customers and achieved 95% customer satisfaction!
## 9\. Arvee
Contact centre traffic and volume soared during the early days of the pandemic at Camping World. There were more glaring gaps in agent management and response times than ever before.
The solution? Arvee: Camping World’s virtual assistant.
Following the implementation, customer engagement rates have trended upwards significantly, and the number of dropped conversations has decreased. Customers are experiencing shorter wait times and faster responses, with live agents’ ability to handle multiple simultaneous chats increasing their overall efficiency by 33%. As of March 2022, customer engagement increased by 40% and Camping World saw wait times drop down to 33 seconds.
### 10\. Cleverbot
Cleverbot is a conversational AI that allows you to converse with your computer. It uses artificial intelligence and machine learning to mimic human conversation and learns from interactions with users.
You can talk to Cleverbot about anything you want! You can ask it questions about the weather, politics, or even what it thinks of your favourite TV show.
### 12\. Virtual City Ambassador For Chicago
The COVID 19 pandemic greatly impacted the tourism industry, and Choose Chicago had to work double time to encourage visitors to come back to the city by marketing all of the amazing things it has going on right now.
What better way to encourage people to visit than by showing them what they can expect when they get here? That's why Choose Chicago turned their attention toward digital solutions like chatbots.
The Bean is an AI-chatbot that helps answer visitors’ questions, suggest new places and events, and serves as a virtual ambassador of Chicago. The Bean can understand when you ask it about Chicago neighborhoods, where to eat, attractions, breweries, events, celebrities, and much more. The Bean covers 63 topics, and has logged 139,000 users between August 2021 to September 2022.
Chatbots can be used to automate anything. Providing 24/7 support where it was previously impossible or too expensive is a big win for businesses, and while the technology is not yet perfect, the benefits are clear: companies now have a new, innovative way to interact with their customers, and chatbots promise to change customer service forever.
If you’re interested in learning more about conversational AI and how it can be used to improve your business, check out our [Conversational AI White Paper](https://www.elementx.ai/whitepapers/conversational-ai-platforms). If you have an idea for a virtual assistant that you’d like to discuss with an expert, get in touch with our team [here](https://www.elementx.ai/contact).
---
## AI Governance Series: Consideration of key stakeholders in the board decision making process
URL: https://www.elementx.ai/blog/ai-governance-considering-stakeholders
Published: 16 November 2022
Updated: 25 January 2024
**Part two of the AI Governance series. How directors should think about the impact of an AI project on customers, employees, shareholders, the wider community, and government regulators before committing to it.**
_This image is not a photo: it was generated using DALL E 2. DALL E 2 creates realistic images and art from a natural language description, and it's just one of many examples of disruptive AI that is becoming more and more mainstream._
_This is part two in our series on AI Governance. Visit part one_ [_here_](https://www.elementx.ai/post/including-ai-on-the-board-agenda)_._
Directors should take note, even though you may have an established internal format for consideration and decision making for any new investment in technology – it might not work so well when it comes to the introduction of Artificial Intelligence without some adjustments. The need for a plan and a business case are likely to be the same, but there will be some new or unique considerations.
For example, deciding to implement a Computer Vision solution is not the same as deciding to install or upgrade a CRM system. Imagine your board was being asked to invest in a Computer Vision solution for the purpose of improving customer experience and this in turn meant using cameras and video to capture customer use of a product or service (think retail foot traffic, H&S monitoring in construction, [looking at traffic in a drive-through](https://www.fingermark.tech/eyecue), or [preventing crime](https://www.auror.co/)).
In each of these scenarios, considering the various interests of stakeholders gets interesting. For example - Is there a chance of individuals being identified through the video capture – whether accidentally or by design? If the proposed solution includes any facial recognition technology, then the question becomes more acute. Suddenly, the customer’s right to privacy and therefore their interests as a stakeholder become an important part of the picture (no pun intended).
Some of these applications of AI require additional steps to protect the interests of stakeholders. In the above example, there might need to be ‘[masking](https://towardsdatascience.com/personal-identity-masking-in-images-videos-and-live-stream-28892f9db46f)’ of the image data to prevent the identification of individuals. This requirement may extend into the early stages of a project during training of the machine learning model. Sometimes this is handled by a 3rd party for labelling data and so masking would likely also be required for this stage.
Given that AI covers a broad spectrum of possible solutions and is developing quickly, then those in governance roles must also develop their approach to these technologies. Not only consideration of a wider than usual group of stakeholders, but also how those stakeholder interests are considered.
#### **Considerations for AI at the stakeholder level**
Take four stakeholder groups and a few of the types of considerations an investment in AI may require. This is only a representation of a wider set of stakeholder and issues;
#### **Customers**
How might the proposed solution impact their right to privacy? Might it capture information that could personally identify them and if so how will the information be used and stored?
If the solution is being designed to provide a significant benefit to one group of customers, does it have the potential to have a negative impact on another group of your customers? For example; using data to reward off-peak or other types of consumption with discounts, but maintaining or increasing prices to another group of customers unable to access the benefit.
#### **Employees**
Business cases for AI can often hinge on operational efficiency gains. Repetitive or similar tasks completed by humans that can be done faster and more accurately by machine learning models are a great place to start when considering how to take advantage of AI. The current labour supply challenges being experienced in many markets are in part fueling research into digital solutions to help.
But what would the introduction of AI projects to your organization mean for your employees, their career opportunities and their futures? If the project will reduce the headcount required for the same or greater outputs then the board needs to at least consider employees as stakeholders and how it might create new career requirements internally and how there might be new development opportunities for existing employees. It may not be reasonable to expect that any staff who are [displaced by technology](https://www.forbes.com/sites/forbestechcouncil/2022/06/28/as-ai-advances-will-human-workers-disappear/?sh=456874915e68) will easily retrain or find other roles in the organisation. In some cases, Unions or other employee representative groups will be stakeholders to consider and even consult.
Similarly, if it is a significant AI project then it may represent real changes to your business model and / or your business processes. A board asking questions along these lines early in the life of such a project may flush out technology adoption or other internal challenges that should be added into the costing model or business case.
#### **Shareholders**
It is clear that board accountability has extended beyond simply acting in the best interests of the company first and then in the interests of the shareholders. More recently there are models emerging that deliver ESG reporting initiatives and recognition of an organisation’s wider responsibilities. In a way, these models reflect the same concepts I am referring to here with wider groups of stakeholders to consider and be responsible to.
But shareholders remain a very important stakeholder group. So a board must consider whether any new AI project is in line with the shareholders’ expectation of the brand. How will it be received by non-customers or groups outside of the customers who will benefit?
Is the project consistent with the organisation’s ethics and shareholder expectations of those ethics? A machine learning model should be adequately explainable (understanding how it reaches conclusions and outputs). It should also be as free as possible from bias. Have potential downstream uses for any data gathered and included in a model been considered?
Any ‘large’ machine learning model (large as in processor and energy heavy) will inevitably create a carbon footprint of its own. A board needs to ask about this issue and can get access to the calculated net effect of such a model to be comfortable that it aligns with their ESG goals.
#### **Community**
Whilst an AI project might serve a particular customer group, it may also have impacts on a community group – the same one that affords the organisation a social licence to operate. Take for example a marketing oriented model that identifies customers and can send them to a specific location for a benefit – like free fuel for a window of time at a service station location. There is a risk of this causing significant upset for a community group wanting to access the same area and only getting choked roads.
There is also potential for a model to have inherent bias – consider for example an automated front end to a home loan application process, if this is biased then it may have a negative impact on one or more groups in the community. Any model replacing human judgement to any degree needs to be fair or unbiased.
#### **Government**
Regulators are an increasingly important stakeholder group. For Artificial Intelligence, a technology developing at speed - regulation is often lagging behind the technology itself. This provides the opportunity for an organisation or group to inform and influence regulation as it is formed. Whilst this is usually the domain of larger entities, like self driving cars or UAV’s, your organisation may have a specific niche requirement or application that crosses over into regulatory territory.
#### **Watching for Pitfalls in planning AI projects**
The examples listed are on balance, negative ones. They simply serve to illustrate the potential for [unforeseen consequences as AI](https://www.elementx.ai/post/when-ai-goes-wrong) serves niche purposes. These pitfalls may not be obvious at the outset or in a business case that only considers numbers.
If the board starts with the intended outcome of an AI project and then considers how that project serves the purpose and the strategy of the organization - then you’ll be able to begin the process on a solid footing and to identify the key stakeholder groups you need to take into account. Often seeing potential impacts on one group will start to open up new groups you may not have considered.
##### In addition to his position as Executive Chairman of ElementX, Richard McLean has over 20 years of experience helping New Zealand businesses tackle growth challenges and bring new products to market.
Richard's AI Governance series can be found here:
1. [An introduction: Does Artificial Intelligence deserve a place on the board agenda?](https://www.elementx.ai/post/including-ai-on-the-board-agenda)
2. [Consideration of key stakeholders in the board decision making process](https://www.elementx.ai/post/ai-governance-considering-stakeholders)
3. [Preparing for and avoiding roadblocks to adoption](https://www.elementx.ai/post/avoiding-roadblocks-to-adoption)
##### Subscribe below to be the first to know when new posts are published, or [follow Richard on LinkedIn.](https://www.linkedin.com/in/richardmcleannz/)
---
## What is Explainable AI, and Why Does Your Business Need It?
URL: https://www.elementx.ai/blog/explainable-ai
Published: 16 November 2022
Updated: 25 January 2024
**An introduction to Explainable AI (XAI), covering why it matters, the difference between global and local explanations, and the methods like LIME and SHAP that make complex models legible to humans, regulators, and end users.**
Artificial Intelligence (AI) has long been considered to be an unsolvable black box. As the field of AI matures and becomes more accessible, there is a growing need to understand what is driving the decision-making of AI models. In cases where the inputs have a high level of complexity, it is difficult for humans to understand the factors that influence the output. Explainable Artificial Intelligence (XAI) is a result of the desire to understand these outputs, and it is a series of methods and processes that can be used to describe an AI model and its potential outcomes.
## Why we need Explainable AI
While adding explainable functionality can increase the workload on development teams, its numerous advantages can provide a more robust model for the future. Bias in models - whether it is gender, race, age or location - has always been a risk when training models. Moreover, the performance of an algorithm can drift and degrade over time, leading to unexpected results. As a business it is crucial to understand model behavior, first to identify when unwanted biases or model drift has occurred, and secondly so that it can be easily rectified.
Ethical / Responsible AI is becoming a large focus for researchers and companies alike, as it is no longer just about how good the model can be, but also ensuring that it conforms with legal and ethical requirements. The [2022 IBM Institute for Business Value study on AI Ethics in Action](https://newsroom.ibm.com/2022-04-14-Responsibility-for-AI-Ethics-Shifts-from-Tech-Silo-to-Broader-Executive-Champions,-says-IBM-Study) found that being able to provide trustworthy AI models is becoming a strategic differentiator for organisations providing services and products backed by Artificial Intelligence. From all of the respondents to the survey, 75% of them indicated that they believe that ethics is a source of competitive differentiation. This is backed by the fact that 79% of CEO’s would be willing to adopt ethical AI practices into their models, which is a massive increase from 20% in 2018. To help organisations adopt AI responsibly, ethical principles such as trust and transparency through XAI must be an essential design factor.
Transparency is crucial to create trust between the model and the end user. IBM’s AI Ethics survey found that 85% of IT professionals agree that consumers are more likely to choose a company that can show exactly how their models are built and how they work. Therefore, being able to provide a human-centred explanation of what factors have contributed to their output can help foster that sense of trust, which will lead to a more productive use of AI tools and an increase in consumer uptake.
Generally this human explanation will show what inputs have had the biggest effect on the output. This can be incredibly helpful in businesses where AI models are making decisions that have a direct impact on their customers. As an example, if a loan company rejects an application for a loan, it is helpful to explain the reason for the rejection rather than simply providing an emotionless computer output.
An AI model needs to be trained for a high level of accuracy, and that can be time consuming and resource intensive. Complex AI models like neural networks can be hard to understand even for an expert, and tuning model parameters to achieve that last percentage of accuracy can sometimes seem impossible. By showing what key decisions were made to produce a specific output, XAI can help developers optimise and debug their model more efficiently, thereby saving time and resources.
## How does Explainable AI work
The work of Explainable Artificial Intelligence is to make complex models simpler. This can be done at a global or a local level. Global Explainability aims to give a general overview of how the model is performing and what features are holistically important across the whole dataset. A survey of housing prices within a city could show that on a global level, larger houses are generally worth more. However, what happens when there is a small house with a price tag that is more expensive than a larger counterpart. This gives rise to Local Explainability, which aims to explain a particular result that may not agree with the global consensus. In the case of the housing example, a local explanation would show that the house was close to the city and therefore had a higher value.
Global explainability is achieved by applying a simpler model, such as a decision tree or linear regression model, to a more complex model like a neural network and then training it to mimic that model's outputs. These are known as Global Surrogate models. Generally these can be understood a lot easier and it is easy to see what is globally important to achieving an output. Although these models end up performing in a similar manner to their complex equivalents, they do not have the same level of accuracy and therefore couldn’t be a direct replacement.
While Global Explainability is good at providing a holistic view of a model, Local explainability is considered a more effective and accurate tool for XAI. At its core, Local Explainability uses feature analysis methods to provide a quantitative number which highlights a features importance to a model's output.
The first of these methods is called Local Interpretable Model Agnostic Explanations or LIME for short. It works by manipulating the original dataset around a certain input value to create a new smaller dataset that can be fed through the original model to create a new set of outputs. Once fitted, LIME analyzes how close the new sampled observation was to the original sample of interest by fitting a more interpretable model, such as a linear regression model. By identifying what features had the biggest change for that particular sample, the model is able to build a picture of what features have the biggest impact on a particular output. For example if the house pricing model gives the following output for these inputs:
Then it could be useful to understand what feature had the biggest influence on that house price. A LIME model would take that particular input set and 'perturb' it, which means it would change the values and create a new dataset of x number of values distributed around it. The new house size dataset could have values from 100m2 to 200m2. This creates a new range of house prices that LIME can then use to identify whether house size, land size, or distance to city was the most important feature.
Shapley Additive Explanations (SHAP) is another popular method for local explanation, although it can also be used for global explanations, which is based on Shapley values and game theory. Historically these values have been used to calculate the individual contribution of a team member to a game. In theory, it would be easy to just remove each player in sequence and then calculate how the team goes without them. The problem with this is that it doesn’t take into account the relationships between players and how they perform when paired with certain individuals. This is the same with features in an ML dataset. In the above housing price example, House Size and Land Size could produce fairly standard results, but when both House Size and Distance to City are considered together this could generate a much better result which in this case is house price. A SHAP algorithm divides all available features into all possible unique subsets, then calculates the performance of each feature within each subset. To get a holistic overview of any features’ contribution to a given output, it is necessary to average all performances.
The above methods of LIME and SHAP values are just two examples of local explainability methods; a number of other methods of local explainability, such as partial dependence plots, accumulated local effects, and integrated graphs, are also available. At their core they all try to explain the local importance of individual features, yet all will be more suited for different problems. No matter what method is chosen, it is crucial to start embedding local explainability in any AI project.
## Industry Solutions
As legislation is getting passed around the world regarding AI governance and ethics, it is only a matter of time before Explainable AI is a requirement for many large scale enterprise projects. Not only that, but the obligation to ensure Responsible AI practices are followed has seen industry leaders like Google and IBM are incorporating XAI methods directly into their products.
One of the most exciting developments in this area has been the release of an open source API, [AI Explainability 360](https://aix360.mybluemix.net/), for explaining complex models by IBM's research centre in 2019. Now, these have been integrated into its Cloud Pak for Data platform so that model outputs can be explained seamlessly. Due to the API's open source nature, the number of explainability methods is only likely to increase as the community contributes and develops future explainability tools.
In addition to offering AI and machine learning services, Google has XAI tools such as Shapley Values built into their AutoML Tables, Vertex AI, and Notebooks that detect bias, drift, and other errors during the design phase. They also offer a 'What if' tool that allows you to experiment with different parameters so you can easily optimise your model. Once a model is in production, they offer a panel that lets you analyse a model’s output with a source of ground truth making it easy to follow performance.
## TL;DR
In conclusion, Explainable AI is a field of Artificial Intelligence that is concerned with making complex models simpler so that they are more easily understood by humans. There are many reasons why we need Explainable AI, including the fact that it can help us debug and optimise our models more efficiently, as well as fostering trust between the model and the end user.
As legislation is beginning to prioritise ethical AI practices, it’s important for your business to be ahead of the game. This is where we can come in - Spark 64 is an artificial intelligence agency on a mission to make AI more accessible. We specialise in language, vision and data to accelerate your business, streamline processes, and uncover meaningful insights through data.
By [Morgan Davies](https://www.linkedin.com/in/morgan-davies-06a66a164/), Full Stack AI Developer, and [Erica Fogarty](https://www.linkedin.com/in/erica-fogarty-0865a4215/), Marketing Coordinator
---
## What is Edge AI and How Can I Leverage it Successfully?
URL: https://www.elementx.ai/blog/what-is-edge-ai-and-how-can-i-use-it
Published: 16 November 2022
Updated: 25 January 2024
**A primer on Edge AI: what it is, the trade-offs around speed, security, location, cost, and processing power, and the practical case for a hybrid approach that mixes on-device inference with cloud processing.**
The opportunities for artificial intelligence in business applications are virtually endless, but selecting the right AI idea to pursue for your business is no easy task. One of the key reasons for this is a lack of understanding around the massive scope of AI applications, and which combination of tools will be most beneficial in meeting the specific needs and goals of your business.
In Gartner’s latest Hype Cycle for AI, edge applications are right at the “Peak of Inflated Expectations” - and we have definitely seen the rise in interest here at Spark 64! While we’ve worked on a few projects that truly illustrate the amazing capabilities of the edge, there are a few considerations for edge implementations based on the outcomes your project needs to achieve.
Perhaps the key reason we choose edge implementation is speed - if your requirements include a time-critical component, chances are you’ll need to leverage an edge application. At the end of the day, every solution is as different as the problem it solves, and the best application of AI technology is the one that provides the best results for you.
### **What is Edge AI?**
To understand the benefits and limitations of edge AI applications, we must first understand their relationship within a greater ecosystem. Imagine a spiderweb: A dense array of woven silk near the centre, with strands reaching out in every direction and becoming more sparse as you approach the perimeter of the web. Now instead, imagine that this spiderweb is a network of devices - sensors, wearables, phones, and computers all linked to a central processing hub (the centre of the web) and sharing information with each other, much like the web’s vibrations share information with the spider.
Traditionally, this central processing hub took care of the majority of computing tasks, but as technology has advanced, it’s become more and more important for some tasks to be completed at the “**edge**” of the network. To take things one step further, edge AI applications are simply artificial intelligence applications that occur locally on edge devices. This is more ubiquitous than you might think - in fact, there’s a good chance you have a device using edge AI in your pocket right now! The iPhone’s Face ID authentication uses an on-device deep neural network to identify you and unlock your phone, even if you’re not connected to wifi or a cellular network.
### **What are the Benefits and Limitations of Edge AI?**
> “_Time is money, and when it needs to happen now, it needs to happen on the edge._” [Forbes](https://www.forbes.com/sites/forbestechcouncil/2021/03/15/computing-on-the-edge-can-be-transformative---but-look-before-you-leap/?sh=60762936f3a5)
In the case of the iPhone’s face ID feature, there are several critical benefits to carrying out this task on the edge (in this case on the iPhone) as opposed to sending face ID information through the networks to be processed remotely in the cloud. In fact, Face ID would likely not be achievable - at least not as we use it today - without the application of edge AI. When you’re considering whether the application of edge AI is suitable for your business case, there are several factors you should consider:
**Speed:** When time is of the essence, edge applications can truly shine in their ability to minimize latency. While it depends on the data being processed, in a GoogleNet image classification example, the processing speed makes response time 6x faster when processed on an edge device [\[Source\]](https://iq.opengenus.org/latency-ml/). Sending data back and forth over an internet connection can slow down decision-making processes that need to happen in real time. For example, a robot arm choosing which object to pick up on a moving conveyor belt can’t spare an extra few seconds, and our iPhone user would get annoyed having to wait three or four seconds to unlock their phone.
**Security:** If sensitive data is being processed, edge applications can eliminate privacy and security concerns by ensuring that the information is held locally, rather than processed on remote servers which may breach privacy laws or project requirements.
**Location:** Edge applications can run anywhere. Even in the places where the wifi or cellular coverage isn’t great. For applications on the move (like self-driving cars) or those held in remote locations (like oil rigs, airplanes, or satellites) sending data away to be processed can be slow, if it’s possible at all. Choosing an edge model is perfect in these instances, as the device doesn’t have to rely on a connection that may not be reliable or have suitable bandwidth.
**Cost**: Processing costs are generally volume based, but edge devices can add an additional layer of complexity to this pricing structure. Although technology advances every day, using a cloud platform to process large volumes of data is still a more cost-effective option. For larger volumes, it’s also more efficient to take advantage of the higher processing power of the cloud. Is your AI project heavily reliant on high volumes of data? If so, a cloud application may be more suitable.
**Processing size:** We hope to report back that this has changed in a few years, but for now, the processing power of Edge devices is a limiting factor in their utilisation. The sensors, devices, and other hardware available for edge AI are still being developed, so one of the keys to ensuring a successful edge project is ensuring that there is hardware available to meet your processing requirements. Some projects are too resource intensive to be completed on the edge and must be sent away for cloud processing.
**AI application:** This is linked to processing size above, but we have found as a general rule that many NLP models are too resource intensive to be useful for edge applications, that Computer Vision is fantastic in edge applications and is one of the most popular use cases, and that AI automation also vastly benefits from edge applications. Each application has different benefits and limitations, and understanding how best to utilise each one is a key to success.
### **What’s the Business Case for Edge AI?**
By now, you may have some ideas about your project and its suitability for an Edge AI implementation. There are a multitude of things to consider here, and we’ve only scratched the surface, but for now I’ll leave one final thought in your head to get the gears turning. _Often, a hybrid approach is actually best_: You can do as much as possible to take advantage of the speed that Edge implementations afford, but offload strategically to improve output, using a cloud or central hub processing model to maintain efficiency with high volumes of data processing. Between the two options, the opportunities become almost limitless!
If you’re considering a project involving an edge AI implementation and would like to explore the options available, we would love to hear from you. ElementX specialises in bespoke, innovative solutions to the hardest AI problems: If it’s a challenge, we want to hear about it! [Get in Touch](/contact)
---
## Does Artificial Intelligence deserve a place on the board agenda?
URL: https://www.elementx.ai/blog/including-ai-on-the-board-agenda
Published: 2 November 2022
Updated: 25 January 2024
**Part one of the AI Governance series. Why AI belongs on every board's agenda, the questions directors should be asking, and what readiness looks like for organisations considering an AI investment.**
_This image is not a photo: It was generated using DALL E 2 using the prompt "Artificial Intelligence sitting at the table in a board room meeting". DALL E 2 creates realistic images and art from a natural language description, and it's just one of many examples of disruptive AI that is becoming more and more mainstream._
## Where does AI fit on a board agenda?
No board pack or agenda would include the simplistic question “Should we buy some Artificial Intelligence?”. If you have one in front of you, then it may be best to quickly consider an adjustment to your portfolio as a director. If however, you and your fellow directors are thinking about and discussing how AI in its various forms might affect your organization - then you are most likely on the right track.
Artificial Intelligence is a term used to describe technology that can replicate or enhance the way human intelligence approaches tasks and that is sometimes able to constantly improve that approach without intervention (learning) – mirroring some human traits. Common understanding of AI varies from science fiction tropes (like Skynet) right through to a good grasp of how Google Maps is able to predict travel times or how Netflix recommends your next binge watch.
## Finding Opportunities in a fast moving landscape
With advances in computer hardware processing capacity (things like scalable cloud infrastructure and graphics processors) the challenge of building and testing AI models is fast becoming less expensive and more accessible. This in turn means that research and development into the various types of AI is moving at a very high speed. Consider Speech Recognition technology for example. We might be quite used to Siri or Alexa in our everyday lives now. But this technology has been around for a long time, making gradual improvements until the 2010’s when competition, focused R&D and available resources allowed it to [close the gap](https://sonix.ai/history-of-speech-recognition) with a human level of accuracy in 2017 in just a few short years. This year, OpenAI has released a model with even better accuracy - which not only transcribes ([try our demo](https://www.elementx.ai/demo/openai-whisper)), but can translate into other languages as well.
## Expect the unexpected: sudden pivots, disruptions, and stops
As with anything else moving at a very high speed, there exist opportunities for impressive outcomes but there are also attendant risks of sudden and painful deceleration. For a board, this simply means aspects of the operating environment can change very quickly. This could manifest in many ways, the sudden appearance of a disruptive competitor is just one example. Consider the challenges presenting right now in the boardrooms of Getty Images or Shutterstock. Two US listed companies that supply images and who are facing new technologies like [DALLE](https://openai.com/dall-e-2/) - able to generate realistic images from a written prompt.
Whilst the probability of a thinking machine helping a disruptor to take big bites out of your market share may be low today, the probability of AI altering the requirements and behaviours of the stakeholders you serve is significantly higher. It already makes sense for a board to regularly consider the organisation’s most valuable assets and understand how customer value is best derived from them.
## Applying a Governance lens
It is only one step further for a board to consider how the organization (or a competitor) might reasonably make use of AI to bring a step change to the value created by the organisation and the way customers are served. Questions like this are one approach for a board to usefully fit the subject into the board agenda.
This is where curiosity matters - an essential director’s quality. Advances in AI as well as practical examples from many different sectors abound and are available to be read and understood – usually in plain language. A little searching and reading can significantly upgrade understanding and the quality of discussions around the table.
All the possible ways in which different types of AI might affect the business model can be a fun and enlightening discussion, which may surface exciting opportunities. Where things get more complicated is where a board wishes to consider investment in AI or adoption of relatively new AI technology into the business. Of course, it is the same as considering any other investment in technology which requires discernment and foresight.
## Successful AI projects have important precursors
But, before spending time and effort on technology choices and constructing a detailed business case for investment a board should consider the wider context of requirements for success – which often includes a need for a bare minimum state of internal readiness before any AI project can begin. For example;
Automating claims processing for insurance is a current real world application of AI technology. The efficiency comes from reducing the manual interpretation and input required. But readiness will involve having historical data to train the model on, that data being of sufficient quality and also understanding the long term requirements (human and technical) to maintain the model.
Thus some good high-level questions for a board agenda item considering an AI opportunity for an organisation would be;
- Which data sets will the project need access to?
- If it is a machine learning model, does training data exist yet?
- How will the proposed project integrate to existing workflows and technology?
- Do we have the resources to maintain the model?
- Will AI make the most impact or might some other small change?
It is not uncommon for organisations to investigate the use of AI only to find that they are not ready. This is hardly a fail though, simply a recognition of the potential for the technology and an option to decide whether to close the gap or not. Broadly speaking, the greater the digital maturity of an organization the more likely they are to be ready to adopt different forms of AI.
AI has a place on any board agenda. At a bare minimum as part of a broader annual technology or digital strategy discussion. More usefully as a specific topic of discussion or even a workshop at least annually, based on questions that probe how the organisation’s assets are currently deployed and how customers might be better served with advances in Artificial Intelligence.
This series will examine further how Artificial Intelligence can be explored and understood in a Governance context, including;
- Common roadblocks to successful adoption of AI
- Consideration of key stakeholders in the decision making process
- Build vs Buy for AI
- Regulatory and Compliance considerations
##### In addition to his position as Executive Chairman of ElementX, Richard McLean has over 20 years of experience helping New Zealand businesses tackle growth challenges and bring new products to market.
Richard's AI Governance series can be found here:
1. [An introduction: Does Artificial Intelligence deserve a place on the board agenda?](https://www.elementx.ai/post/including-ai-on-the-board-agenda)
2. [Consideration of key stakeholders in the board decision making process](https://www.elementx.ai/post/ai-governance-considering-stakeholders)
3. [Preparing for and avoiding roadblocks to adoption](https://www.elementx.ai/post/avoiding-roadblocks-to-adoption)
##### Subscribe below to be the first to know when new posts are published, or [follow Richard on LinkedIn.](https://www.linkedin.com/in/richardmcleannz/)
---
## Introducing ElementX
URL: https://www.elementx.ai/blog/introducing-elementx
Published: 24 October 2022
Updated: 25 January 2024
**Spark 64 has become ElementX!**
Last week, after months of secrecy, we made some pretty significant changes to our website and Spark 64 became ElementX! This is a big change and has been such a long time coming. We’re very excited to finally be able to share it with you!
Spark 64 started over nine years ago - Dan and Ming (Our CEO and CTO) met at university and collaborated to build an app that tracks real-time UV radiation to help keep users sun safe (you can still download UVLens in the Apple app and Google Play stores!). Throughout the company’s history, they maintained their focus on providing solutions that would improve the lives of end-users, though since these humble beginnings the company has evolved beyond sun safety. We now have a team of AI experts across fields like machine learning, computer vision, conversational AI and more, working on cutting-edge projects that help businesses prove and productionise unique AI use cases in a way that drives results through a customer-first lens.
“Spark 64” was a nod to two very important things - the initial, brilliant spark of an idea that brought these two together, and the Nintendo 64- everyone’s favourite gaming console at the time. The name served us well over the years, (and we still play a mean round of Mario Kart!) but it was time for a change.
#### **Why ElementX?**
When we begin working with a new client, we start by discussing all of the different components of a successful AI project. Things like data quality and collection, ML Ops frameworks and best practices, choosing the right cloud service provider and processing tools, and designing solutions that fold seamlessly into existing business objectives without adding unnecessary layers of complexity. You can think of these components as some of the _elements_ of a successful AI implementation.
Assembling the right expertise - a team with the right engineering skills as well as the business focus required to consider how a project will work out in the real world - is the key element that binds all of these disparate components together. That’s why we chose the name “ElementX”. We are the human element that brings it all together - from an AI assistant that provides student assistance to New Zealand’s largest university, to a product recommendation engine that The Warehouse Group uses to increase online sales, to digital humans that can help answer health insurance questions (Southern Cross) and guide you through a mortgage application (Arcus lending).
Our team has a strong background in R&D and a proven track record of delivering impactful results to some of the most difficult problems. Our nerdy nature also hasn’t gone anywhere - although we have upgraded to a Nintendo Switch these days, but if you (like us) are a consumer of cartoons and comics, you might recognise “Chemical X” as the special ingredient that gives the Powerpuff Girls their superpowers, and the “Tenth Element” (sometimes displayed with the roman numeral X) as the material that everything in the DC comics universe was created from.
Although we don’t claim to be superhuman, we do believe in the immense power of the human element - especially when it comes to our amazing team of experts!
Functionally, you can think of the new name as more of an update. We’re still the same team we were as Spark 64, and our focus on providing impactful solutions with a customer-first focus hasn’t changed. That said, we do have a couple of very exciting projects on the horizon that we’re excited to share with you, so if you want to stay up to date with all things ElementX, be sure to subscribe to our newsletter and follow us on [LinkedIn](https://www.linkedin.com/company/elementx-ai/)!
---
## Fireside AI Episode 3: DALL-E with Ming
URL: https://www.elementx.ai/blog/fireside-ai-episode-3-dall-e-with-ming
Published: 30 September 2022
Updated: 14 October 2022
**This week on the Fireside AI podcast we talked all things DALL-E.**
**What is DALL-E?**
This week on the Fireside AI podcast, our guest was Ming Cheuk, CTO of ElementX. Together with Daniel and Morgan, he discussed the DALL-E image generation model, which is a new AI system that can create realistic images and art from a written description. It uses a 12-billion parameter version of GPT-3 (a natural language model) to generate images from prompts like the ones our hosts explored in the episode.
You can see the results of those prompts below.
> Ming: We've seen AI creating images for a while now, and in the early days the images that these type of models would create would be very abstract. It doesn't reflect any realistic photo or, you know, image that you'd see in the world. But over the years, especially the last one year or so, they've really made a big breakthrough in creating images that look very realistic, whether it's photorealistic or just realistic in general, even if it's of a cartoon character.
**Listen to Fireside AI Episode 3: DALL-E for more on the following:**
- DALL-E’s ethics around photographer credits.
- The future of DALL-E 2.
- DALL-E’s rollout restrictions.
#### **And the beautiful images they made:**
If you’ve made it this far, you’re most likely here for the stunning works of art they generated during the podcast. Be warned: once viewed, you’ll never unsee them.
Listen to the full episode [here](https://plinkhq.com/i/1627835366?to=page).
Play with DALL-E yourself [here](https://huggingface.co/spaces/dalle-mini/dalle-mini).
---
## Defence against Big Tech
URL: https://www.elementx.ai/blog/defence-against-big-tech
Published: 15 September 2022
Updated: 14 October 2022
**Three strategies for legacy businesses to compete against tech-first disruptors using AI and data: move up the value chain by productising data, automate operations to improve efficiency, and differentiate on customer experience.**
Amazon recently announced its move into the healthcare space through the [acquisition of primary care organisation One Medical](https://press.aboutamazon.com/news-releases/news-release-details/amazon-and-one-medical-sign-agreement-amazon-acquire-one-medical). It’s a move that is likely to bring a new wave of disruption to the healthcare industry. Amazon specifically mentioned the immense opportunity to make healthcare more **affordable**, **accessible**, and **enjoyable**. When a company that knows more about you than yourself, coupled with their unparalleled logistics and automation capability makes a statement like that, you tend to believe them. This is another example of a new technology-first company that just so happens to operate in a particular industry. Tech companies like this have a strong competitive advantage as they generally have no legacy systems, are data and AI driven from day one and have a strong magnet for talent. So how do you compete against such a force of disruption? Here are three strategies from an AI and data perspective that can help your business play defence.
#### **#1 Leverage data to move up the value chain**
[Shapeways](https://www.shapeways.com/) is a 3D printing business founded in 2007 that prints bespoke components in low quantities which are difficult and expensive to make for traditional manufacturing. The business started by allowing customers to send in their CAD files directly to Shapeways for printing. However over time, as 3D printing technology was becoming commoditized, the cost of printing also came down. This lowered the barrier to entry for new competitors and enabled a new market of 3D printing businesses which could compete against Shapeways by being local and offering faster turnaround times. Shapeways thus needed to change its business model. Fortunately for the company, over the years of operation, it had amassed a large dataset of print designs, configuration settings and pricing. The business was able to leverage this data to create a new software-as-a-service offering [OTTO](https://www.ottosoftware.com/), which provides the 3D printing market with instant price quoting and configuration software.
This classic example of transitioning from selling hardware to selling software is a way of moving up the value chain in a commoditized market. A number of companies such as [WayBeyond](https://www.waybeyond.io/) and [Spidertracks](https://www.spidertracks.com/) offer insights-as-a-service to unlock new value for customers. Consider the unique data you have and how it could be leveraged to create new products and services for your customers.
#### **#2 Use AI and automation to improve efficiency and profitability**
Another way to compete against technology-first companies is to use automation to improve your operating efficiency and profitability. Businesses with a large labour force not only struggle with recruitment and staff absenteeism (especially during Covid) but also have high operating costs that results in low profitability. Companies like [Reynolds Group](https://rgl.co.nz/) offer a range of automated labelling, inspection and handling technologies for manufacturing and primary industries.
Other avenues of improving profitability can come from the clever use of AI to efficiently allocate and utilise resources. Cloud file storage company [Dropbox uses Machine Learning to save over $1.7M a year](https://dropbox.tech/machine-learning/cannes--how-ml-saves-us--1-7m-a-year-on-document-previews) in infrastructure costs by predicting which documents users are likely to view to cache document previews. [Uber uses real time demand forecasting](https://www.uber.com/en-NZ/blog/forecasting-introduction/) that considers the weather, traffic and event information to optimise pricing, which increases their profit. [RosterLab](https://www.rosterlab.com/) uses AI to schedule nursing rosters for hospitals and aged care, saving countless hours of manual work.
Think about where the inefficiencies and bottlenecks are in your business and how you can leverage AI and automation to increase productivity and reduce costs.
####
#### **#3 Focus on differentiation through customer experience**
Customers have increasingly higher expectations for speed, personalisation and accessibility. This presents an opportunity to win by delivering a better customer experience. Here are 3 ways of improving your customer experience with AI
**Speed**
Insurance companies such as [Lemonade](https://www.lemonade.com/) offer faster claims processing by leveraging natural language processing (NLP) to instantly read insurance claims and supporting documents. They then couple this with machine learning to categorise and validate claims and also identify fraud. Real Estate company [OpenDoor](https://www.opendoor.com/) uses data science to instantly price the value of a house by considering similar properties that have recently been sold and house features including square footage, backyard space, number of bathrooms and bedrooms, layout, natural light.
**Personalisation**
eCommerce and retail companies commonly use a wide variety of personalisation tools to help shoppers discover and purchase products. We recently helped New Zealand’s largest retailer, [The Warehouse, develop a Gift Recommendation Engine](https://www.spark64.com/project/gift-recommendation-engine) to help shoppers find gifts. Kiwi company [Fingermark](https://www.fingermark.tech/) uses cameras and computer vision to detect vehicles in the drive-thru for quick service restaurants and uses AI to learn trends and predict patterns to optimise the customer journey.
**Accessibility**
The traditional 9-5pm call centre is a common point of frustration for many customers.
Customers now want to be served at the time and place that is most convenient for them, and in their preferred language. [The University of Auckland developed a 24/7 chatbot](https://www.spark64.com/project/university-of-auckland-ai-chatbot-case-study) to answer commonly asked questions from students and staff. This has helped reduce the load on their contact centre and allows contact centre reps to work on more important calls. The chatbot also speaks English, Chinese, Te Reo Māori and Samoan.
Customers are also expecting businesses to be available across a number of different social channels. Insurance companies like [Cove](https://www.coveinsurance.co.nz/) allow customers to purchase insurance via their Facebook chatbot. [UneeQ](https://digitalhumans.com/) is taking this to the next level and building digital ambassadors and virtual assistants for the metaverse.
Consider how you can leverage AI to reduce customer wait times, increase personalisation and extend your offering across multiple languages and channels.
#### **Final thoughts**
AI and data have a transformative power to help businesses move up the value chain, increase profitability and enhance the customer experience. In the wake of a growing number of technology-first companies disrupting traditional markets, the best form of defence is to play offence and think like a tech company. Here are a couple of additional considerations as you embark on your AI journey
**Develop a data strategy**
Data is a key ingredient for AI and machine learning and a real competitive advantage for companies that can build a data moat. Understand what data you have and what data you need to enhance your existing offerings and to create new products and services. At ElementX, we use a ‘work backwards from the end customer experience’ approach to create a roadmap on how to get there.
**Identify and automate bottlenecks in your operations**
A key to improving profitability is to leverage automation technologies to increase efficiency and output while reducing operating costs. Understanding how things like chatbots, computer vision, and document processing could be leveraged to increase speed and scale up existing workflows and processes will have a big impact on profitability as well as your customer experience.
---
## How AI Chatbots are Transforming Customer Support Services
URL: https://www.elementx.ai/blog/how-ai-chatbots-are-transforming-customer-support-services
Published: 15 August 2022
Updated: 14 October 2022
**Chatbots designed to relieve bottlenecks in customer support centres can drive incredible results for your business. Chatbots can take many forms and perform many tasks - have you considered all the available options?**
Chatbots have come a long way in the last few years and advances in AI, Natural Language Processing (NLP) and Machine Learning (ML) have brought along a huge transformation in the options available to handle customer support. These advances bring with them several benefits: chatbots that leverage conversational AI can facilitate more personalised and empathetic service, faster response times, and they can present relevant, accurate information to customers in the moments when it is most needed; even when companies are dealing with high volumes of enquiries that have variable-dependent answers.
These chatbots are designed to relieve bottlenecks in customer support centres that cause customers to wait in long queues before their questions can be answered, and provide better access to call centre agents when customers have queries that are too complex to be answered by the chat. As more and more of our business moves online, it’s more important than ever to build smart, scalable solutions to support your customers. You may be ready to graduate from a live chat agent to automated assistance, or perhaps you’re skipping straight to AI to speed up the process. Here are a couple of considerations you should keep in mind as you decide which solution will best suit your business.
### What types of chatbots are there?
Chatbots take multiple forms. We recommend putting your customer at the centre of the discussion when determining which one will best suit your business’s needs. What type of service will best benefit the customer? How will they use this new service? Here are a few examples:
**Rule-Based Chatbots:** This option is the simplest to implement, and doesn’t require AI - but the result can only perform a specific task. These chatbots are useful if your customers need guidance performing a pre-set task, like choosing the best purchasing option from a selection of similar items. Apple uses a rule-based chatbot to automate the process of helping users select the correct size wristband when purchasing an Apple watch, for example.
**Pro:** Help guide customers through specific tasks; automated. These are often out-of-the-box solutions.
**Con:** Rules are pre-set - these chatbots won’t be able to assist with queries outside of the pre-set flow: customers are likely to encounter a “Sorry, I don’t understand” answer from a rule-based chatbot.
**NLP Chatbots**: Natural Language Processing can be used to add conversational intelligence to chatbots. This option does use AI, and the resulting chatbot can understand the sentiment and intent of a customer’s query. They can identify the best information to share with a customer with a question like “What type of car insurance coverage should I buy for my teenager?” or a student asking “How do I apply for scholarships next semester?” - even though these answers have multiple dependencies.
**Pro:** These chatbots understand your customer’s needs and can identify and present the best solution. They can identify chats that need to be escalated to a human agent, while efficiently answering other queries.
**Con:** The best NLP chatbots are built using your business data, so there will be some waiting time during implementation and training.
**Virtual Assistants:** If you’ve interacted with Siri or Alexa, you’re already familiar with this type of chatbot. These assistants are designed to assist a human with a task when prompted. In the case of Alexa and Siri, these tasks include searching the internet, adding a reminder, or setting a timer. For your business, they may be tasks like applying for a mortgage or completing learning modules for an online course. [Clearhead](https://www.clearhead.org.nz/), for example, offers a virtual assistant focused on helping users track their mental health, and can identify the need to connect users with a human therapist when needed. Our client, Instamortgage, employs a [virtual assistant named Rachel](https://rachel.instamortgage.com/) to help customers through the process of applying for a mortgage.
**Pro:** Virtual assistants can help your customers with a variety of complex tasks, alleviating stress from your operational teams, improving customer experience and speed to result. They can also be connected to an ML engine to perform business operations like claims processing or gathering information for a quote.
**Con:** These assistants can be complex to set up, and often require integration from multiple systems. Engaging with an AI partner that can identify and implement the best solution for your needs will be critical to success.
## Additional Considerations for Chatbot Selection
You may already have a pretty good understanding of what type of chatbot you’d like to implement. Before you proceed, here are a few more things to consider before you begin the process of implementing your chatbot.
### Conversational AI for a more natural chat experience
Conversational AI is the difference between a simple, pre-scripted conversation and the ability to recognise intent and empathise with your customers. Although some interactions are easy to script, you can transform the way customers think about and interact with your business by providing automated support that truly understands their needs. Conversational AI allows for a semantic understanding of customer queries, and it identifies the meaning in customer queries rather than triggering pre-populated responses based on keywords.
In many cases, Conversational AI chatbots benefit from the presence of a specialist conversation designer on the implementation team. Conversational designers consider how the customer will interact with the AI, and anticipate things like slang, passive aggressive statements, and customers who can’t quite explain what they’re looking for. This way, they can ensure that the discussion can be orchestrated in a natural, straightforward way.
Your business’s conversational AI experience can be built on a variety of platforms, including IBM Watson Assistant, Google Dialogflow, and Amazon Lex. ElementX is technology agnostic, which means we work with all of these platforms (and more) to ensure success. Each option has its own benefits, and we’ll work with you to identify the best solution for your unique business case.
You can learn more about Conversational AI platforms by [reading our white paper.](https://www.spark64.com/whitepapers/conversational-ai-platforms)
### Anticipating the customer journey
How common and straightforward is the customer journey for your business? An extremely straightforward customer journey like shopping for shoes online most likely doesn’t require Conversational AI - a rules-based chatbot that can offer direct information about shoe sizes might suffice. However - if the journey is a little more complicated (are your customers applying for mortgages, insurance claims, or university?) then anticipating their needs can be critical. Your chatbot may open with a different question depending on what page the user is currently on, or pages they’ve visited leading up to their query.
The chatbot can also gather information about the user to help identify what information will be most relevant to present to the user - for example, by asking an user who is interested in purchasing car insurance for information about their vehicle and driving habits.
### Ticketing and CRM system integration to solve complex customer queries
One of the most common types of chatbot implementations we see is designed to identify answers to customer queries from complex knowledge bases and serve up accurate information. This is a worthy implementation, but it only scratches the surface of conversational AI capability. Your chatbot can be integrated with existing CRM and ticketing tools along with other internal systems so that they can actively assist with queries that require account information (such as “What’s my account balance?” and “when is my next payment due?”) as well as track the status of support tickets - all without the help of a human agent.
### Ease of knowledge base updates
In the last few years as COVID-19 has dramatically changed the landscape of customer interaction, providing the most up-to-date information to customers can be a challenge. One of our clients, a large university, uses a chatbot to assist with student queries. They quickly realised that communicating accurate, updated information about campus closures and protocols was critical as COVID situations changed so rapidly.
ElementX helped to build a system that allowed their contact centre team to update the knowledge base without code so that they could get changing information out to students quickly.
Designing a solution that allows for ease of access to update information can be a critical consideration in the long-term success of a conversational AI project.
Any AI automation project should be viewed during the lens of customer experience. By prioritising projects that will positively impact this experience, you’ll be able to drive meaningful, quantifiable improvements to your business and create lasting customer relationships. There possibilities of chatbot implementation are nearly limitless - from simple bots that answer frequently asked questions all the way up to complex systems that can assist with mortgage applications, insurance claims, and even digital humans that can empathise with your customers.
Whichever option you choose, the best automations you can introduce are the ones that work seamlessly with the rest of your business. If you’d like to learn more about our work, and be the first to hear about innovations in the AI space, sign up for our newsletter below!
---
## The Essential Chatbot Terminology You Need to Know
URL: https://www.elementx.ai/blog/the-essential-chatbot-terminology-you-need-to-know
Published: 15 June 2022
Updated: 14 October 2022
**Chatbot terminology can be overwhelming for individuals without a technical background. This guide breaks down the concepts associated with this emerging customer service tool.**
Gartner [predicts](https://www.gartner.com/smarterwithgartner/top-cx-trends-for-cios-to-watch) that this 2022, 70% of customer interactions will involve emerging technologies such as machine learning (ML) applications, chatbots and mobile messaging. Mordor Intelligence, on the other hand, [projects](https://www.mordorintelligence.com/industry-reports/chatbot-market) that the chatbot market will be valued at $102.29 billion by 2026.
Meanwhile, Salesforce's 'State of Service' report [showed](https://www.salesforce.com/content/dam/web/en_us/www/documents/research/state-of-service-4th-edition.pdf/) a 67% uptick in chatbot usage between 2018 and 2020, with 66% of the survey's 7,000 respondents agreeing that self-service chatbots have helped their organisations reduce case volume, especially during the pandemic.
As digital technologies become more prevalent in our lives, customer expectations have changed. Nowadays, people expect businesses to be available 24/7, ready to interact or reply to questions immediately. From ordering pizza to finding details about a hotel booking, chatbots can handle all sorts of tasks quickly and efficiently.
At their best, they make customer interactions more streamlined and efficient. They're making it possible for businesses to provide answers and support in real-time, and they've become essential for delivering a great customer experience.
When reading about chatbots, the sheer amount of terminology can sometimes be overwhelming for individuals without a technical background. Suppose you're a decision-maker in the retail sector who needs a custom chatbot or are interested in a solution that can help your company improve customer service response times. You might have a good idea of the issues you need to address, but the jargon-filled language of engineers and developers can make it difficult to understand what you're actually getting.
To make informed decisions about your technological investments, you need a grasp of the essential concepts and features associated with this [customer support tool](https://www.spark64.com/post/how-ai-chatbots-are-transforming-customer-support-services). This article will give you a better understanding of how chatbots operate for you to get a clearer idea of what to look for when considering a solution for your business.
## Artificial intelligence (AI)
In its most basic form, artificial intelligence is the simulation of human intelligence through computers programmed to mimic human thought processes and actions. The term also applies to the 'smart' machines capable to perform tasks that typically require human intelligence such as learning and problem-solving.
AI is used to process and interpret actual customer queries so that chatbots can understand the customer's intent and respond accordingly. Chatbots are constantly learning from past interactions and getting better at understanding customer needs over time.
## Natural language processing (NLP)
Chatbots use natural language processing (NLP) algorithms to process the text input by customers and convert it into a format that the chatbot can understand to interpret customer queries, identify the key elements of the conversation, and determine how best to respond.
For instance, a customer trying to make a purchase on your website might ask, "Do you take credit cards?" or "Are you cash only?" while another might ask, "Can I pay with Mastercard?" By implementing NLP techniques, engineers can train their model and enable the chatbot to respond with a correct answer such as, "We accept VISA and Mastercard" regardless of the pattern in which the question was phrased.
## Natural language understanding (NLU)
Natural language understanding (NLU) is an AI-driven capability that uses syntax and semantics to analyse and understand user input and identify the intent behind the message. Computer code is structured, so it is the job of NLU to account for the subjective, messy human language it encounters. For example, if a customer types in "I want the Return of the Jedi DVD," without any additional context, the chatbot relies on NLU to understand that the customer is most likely buying a disc and not looking to return a purchase.
This understanding of the customer's intent is what enables the chatbot to provide a fitting response such as, "I'm sorry, we don't have that in stock. Would you like to see our other options?"
### Intent
The intent is what the customer wants to achieve with their message. Helpshift [offers a great explanation](https://www.helpshift.com/glossary/intent-in-chatbot/) on the importance of intent in chatbots:
_"Intent is a critical factor in chatbot functionality because the chatbot’s ability to parse intent is what ultimately determines the success of the interaction. In order for a chatbot to be good at this, it must be programmed well and trained with a useful model involving a lot of training data and take advantage of machine learning to constantly advance and improve."_
A customer might start with, "I need a flight to Los Angeles." This statement is asking the bot to browse its database of available flights to LA.
### Entities
Entities are the important pieces of information a chatbot can extract from a customer's input that are relevant to the user's intent and can be later on used in the conversation to generate useful responses or perform specific actions.
For example, a customer might say, "I want to book a flight from New York to Los Angeles on June 12th." In this case, the entities would be:
- Location (New York)
- Location (Los Angeles)
- Date (June 12th)
### Utterances
Utterances are the different ways in which a customer might phrase their query. A chatbot would need to derive intents and entities from this input. For example, customers looking to book a flight would have different ways of stating their intent, such as:
- Please book a flight from New York to Los Angeles, or
- Pls book plane to LA
- Find me the next flight to Los Angeles from New York
## Self-service or self-serve chatbots
Self-service or self-serve chatbots are designed to enable customers to serve themselves without the need to contact a customer service staff. It uses NLP and NLU to parse customer queries and provide appropriate responses to simple inquiries such as, "How can I change the password on my account?", or more complex queries like, "I am experiencing an issue with my order. Can you help?"
The key advantage of self-service chatbots is that they can handle a large number of straightforward queries simultaneously. This results in lower operational costs for businesses and higher satisfaction levels for customers as they are able to get their issues resolved faster through the database of answers the chatbot can pull from.
## Machine learning (ML)
ML is a subset of artificial intelligence that enables chatbots to learn from past conversations and improve their responses over time. The more data a chatbot has, the better it can become at understanding customer queries and providing accurate responses. This is why it is important for businesses to constantly train their chatbots with new data.
Chatbots, through ML, can also learn to recognise patterns in customer queries and offer proactive support before an issue arises. A chatbot might recognise that a customer who initiated a chat prior and frequently asks questions about returns is likely to make a return soon and could proactively offer assistance.
## Conversational AI
[Conversational AI](https://www.spark64.com/services/conversational-ai) is the subset of AI that leverages concepts like machine learning, NLP, and NLU (among others) to power chatbots and digital humans across a range of industries. Leveraging conversational AI could mean further developing a conventional chatbot to support tasks in customer support, lead qualification, or even sales.
By taking advantage of conversational AI, businesses can create chatbots that are more natural and engaging, providing a better overall experience for customers.
## Conversational UI (CUI)
A conversational user interface (CUI) is a type of user interface that allows users to interact with computers and digital devices through natural conversations. It can take form either through text (via chatbots) or voice (via voice assistants). Slack’s slackbot is a classic example of CUI in action on the chatbot front, while tech giants Apple, Google, and Amazon are shining examples of companies betting big on CUI through their voice assistants.
## Sentiment analysis
Chatbots aren't just engineered to provide the right answers; they are also designed to understand the emotional context of a customer's inquiries. This is where this subfield of computer science comes in. Sentiment analysis allows chatbots to evaluate how users feel, flag any issues, and align responses to the customer's emotions.
Sentiment analysis is important as it allows businesses to improve their chatbots by addressing areas that are causing negative sentiment. A customer might say, "I never received my order." The chatbot would be able to detect that the user's sentiment is negative and immediately offer the appropriate assistance.
### Other chatbot terms
There are a number of other key concepts associated with chatbots, which you'll probably hear a lot of from technical conversations or read about in project documents, such as:
- **API** - or application programming interface; the messenger that takes requests and tells a system what you want to do and then returns the response to you.
- **Webhooks** - automated API responses that retrieve information from chatbot conversations, like email addresses or telephone numbers, and pass them automatically to web services; these can also call out to external services to provide a richer experience through integrating with other tools and part of the business.
- **Software integrations** - the process of connecting two or more applications to work alongside each other, such as when deploying a chatbot to your company's Facebook page.
As people become increasingly reliant on digital tools when interacting with businesses, it's essential for organisations like yours to look for more innovative ways to deliver outstanding customer service. By taking advantage of the various AI-powered capabilities available today, you can pave the way for your company to provide even better customer service tomorrow.
No one will be willing to invest in technology they don't fully understand, so at ElementX we take out the explainability gap and get stakeholders on the same page with our AI conversations. We'll work with your team to ensure that you get the best results from your chatbot project, regardless of the industry you're in.
[Talk to us](https://www.spark64.com/contact) to learn more about how your business can leverage chatbots and other AI technologies today!
---
## Our Third Hackathon Retreat
URL: https://www.elementx.ai/blog/hackathon-retreat-recap
Published: 15 May 2022
Updated: 14 October 2022
**Check out what the ElementX Team got up to over our company Hackation weekend!**
## Where Great Ideas Begin
For the ElementX Team, hackathons are one of the company events we look forward to the most.
Over the past weekend, the team gathered together for our third Hackation Retreat, where we have a hackathon within a getaway vacation.
It was the ultimate bonding and learning experience for our team - check out what we got up to!
#### Getting settled
After our usual afterwork Friday drinks, the team convoyed our way to an out-of-Auckland retreat. We went into this trip with few expectations - we just wanted to see what could be created within 24 hours. All we knew was that we were in for a weekend of great food, amusing board games and potentially the first steps towards disruptive innovation.
We had a family meal together and got in some well-deserved R&R. A few rounds of foosball, pool and board games (Captain Sonar FTW) got our competitive spirit going for the next day’s Hackathon!
#### Getting Started
After the banter and board games, we gathered the next day for a team brainstorm to exchange any and every idea we had bubbling away in our minds - for work and for play! We sought out concepts that could be used in a variety of applications someday, as well as to build upon projects we already have in the works. With a flurry of Post-Its, we set off as individuals and in pairs to tackle a few different projects.
## Our Pitches
In under 24 hours, we started the foundations of four applications and pitched them to the rest of the team.
Here's what we came up with:
#### Finger Doodle
We created an app that allows you to draw using only your fingers and hand. This was made possible with deep-learning models trained for precise hand tracking, that were efficient enough to run on mobile hardware. The same underlying technology can be used for controlling your devices using only your hand (think while in the kitchen), reading sign language, gaming; the possibilities are endless!
#### How Do You Feel...?
_Are you a business trying to gauge your customer’s reactions? Or just a humanoid trying to avoid public faux pas?_
We experimented with browser-friendly, on-device vision model to register levels of emotions, such as happiness, sadness, surprise and disgust. This could be utilised to support focus groups for gauging customer reaction to new products.
#### AI Table Tennis Referee
We’re totally over keeping score at the office Pong Arena! Using Google’s [Teachable Machine](https://teachablemachine.withgoogle.com/), we worked on an application to automatically score our lunchtime table tennis matches. By gathering audiovisual data of us playing, we segmented and classified the frames and sounds before feeding them into their separate models for training. The aim of the models was to determine the ball's location via object detection, using the different sound signatures. The output of these two models could then be combined to determine our score and winner!
#### No Ingredient Left Behind
We were able to create the building blocks of a computer vision model that would allow us to detect and classify grocery items. This could have a great number of uses and applications, including checking you have all your shopping at the supermarket and quality control for food production lines.
### Key Takeaways
- Hackathons are one of the most valuable ways for ElementX to encourage innovation. This weekend escape was a great opportunity to boost new ideas, from thought to implementation, in a couple fun-filled days.
- Having the opportunity to hang out with one another was key to our creativity! We wouldn’t have come up with the applications that we did without bouncing our mad genius (or just mad?) thoughts and ideas around.
- More table tennis games are tp be scheduled… For R&D purposes, of course! ; )
> _Want to be at our next hackathon retreat?_[_ Contact us about our open positions and opportunities!_](https://www.spark64.com/contact)
---
## The Humble Beginnings of the Imposter Bot
URL: https://www.elementx.ai/blog/the-humble-beginnings-of-the-imposter-bot
Published: 15 March 2022
Updated: 14 October 2022
**The story of how Team Humble Wisdom's Hackathon project - the Imposter Bot - came to life. Written by team captain and fearless leader Ming Cheuk, CTO of ElementX**
__It was through the 8th iteration of the ElementX hackathon that the Slack application “Imposter Bot” was born. This app is able to impersonate anyone (with their permission) on your slack Workspace, inserting replies in conversations at unsuspecting times using the most advanced AI language models.
The idea started as a joke, a seemingly useless invention — but we realised it had so much more potential. This is the story of how it came to be.
On a sunny Thursday morning, and the team assembled together in the quiet neighbourhood of Hobsonville Point to start the hackathon. We had our idea ready, the team ready, and we were ready to win this hackathon. For these two days, we were known as Humble Wisdom, and we were the first, second, third, and fourth Wise ones.
Straight to the tools we know in our sleep, we started planning our feature set on Miro and Github.
##### Planning
We wanted to build something realistic in the two days we had, but we also wanted something that would impress. Working backwards, we first figured out what we wanted to present at the end of the two days. From there, we decided what features we needed to support the plan and how we achieve it in the technical solution. Finally, we wanted to build iteratively to ensure we had something working in each release, following general best practices of short release cycles of product development.
What good is a product without some solid marketing behind it? Another thing we wanted to come out with a bang with was solid content. None of us were video producers, writers, or songsmiths, so why not employ some AI to help us with that? We decided very early on to let AI dictate our product launch content and to stay as true as we can to the output from AI.
It was probably one of the fastest and most efficient planning sessions we’ve done! All set in stone within 20 minutes.
#####
Execution
The responsibilities were quickly distributed amongst the team. The first and second Wise Ones were in charge of content and creative, and the third and fourth Wise Ones were in charge of performing the engineering feat needed to make this work.
**__**
##### Engineering
Everything we did we wanted to do as true to a real-life product launch as possible. We had setup a Github repo, Kanban board with the tickets to make the bot work, and finally a new Slack Workspace for testing. One of the first things the we did after scaffolding the project was to setup continuous deployment pipeline so that all the code merged into the main branch was pushed automatically to a production instance (thanks fourth Wise one!). This reduces the human-error-prone nature of manual deployments and made it much easier for everyone to contribute to the code.
For natural language understanding, we used GPT-3, one of the most famous language models in the world. Trained on billions of text samples, it’s able to comprehend human language and generate convincing outputs to mimic the rest of the input or be instructed to carry out some natural language task such as sentence classification. This was something we were also quite familiar with, having worked on several projects for clients that use this technology.
We started with a bit of [prompt engineering](https://medium.com/nerd-for-tech/prompt-engineering-the-career-of-future-2fb93f90f117) to design the best prompt to make it work. With a combination of experience and experimentation, the second and third Wise Ones were able to create a prompt that will take a conversation history and both recommend the next person who should reply as well as what they should reply with.
__
To integrate with Slack, we made use of the Bolt SDK, which is the modern day way of interfacing with the Slack API and makes development of Slack bots much cleaner. This library was new to us but the third Wise One was able to quickly get it all integrated for us to understand all the capabilities and shortcomings…
**__**
##### Creative
Every legend has a story, and we wanted to create one for Humble Wisdom. Using the GPT-3 playground, we generated a very well written in a single attempt.
__
But why stop there? We decided to have this narrated with AI synthesised voices, using [WellSaid Labs](https://wellsaidlabs.com/). We then decided that the narration should come with some artwork to go with the story, which we generated using [dream by WOMBO](https://www.wombo.art/) (based on GANs). The second Wise One pulled all this together along with some royalty free music and voilà: we had an awesome video (before we discovered AI generated music, more on that later).
Now we needed a video for the product. To keep on brand, we had GPT-3 generate us a script as well. Too bad it didn’t quite understand the concept of a Slack bot impersonating people (to be fair most people wouldn’t), so instead, we decided to market it as an “employee engagement product”. Behold, it generated a video script for us! We needed some cheesy advertising music to go with it as well, at which point we found [Soundraw](https://soundraw.io/) which uses AI to synthesise music.
__
After some fun filming it around windy Hobsonville, sticking as close as we could to the script GPT-3 generated (we decided on one deliberate phrase replacement and we had some human error in remembering the lines).
Our final touch was a team profile on the website. We generated profile pictures that represented the roles we had in the team.
__
Yes, we did create a theme song, with lyrics generated by GPT-3 and music generated with AI. But that’s for ElementX eyes only.
##### Roadblocks
What’s a project without a few roadblocks? They say that you’re not pushing yourself hard enough if you don’t run into challenges.
The first problem we hit in the engineering department was that the bot could easily impersonate anyone in a Slack workspace, but it would show the “APP” label, which would easily give it away. Also, it wouldn’t take on the person’s status, and if you clicked on the profile, you’ll see the Imposter bot’s profile. This is probably a good thing otherwise you’ll have applications maliciously posing as people in the Slack workspace which opens up phishing problems. _But_ _not_ _what_ _we_ _wanted_. After much scouring through Slack developer forums, we finally found that you can have the app properly impersonate someone if they explicitly authorise the application to post as them.
Time to test! Except this took us hours of trying to get OAuth permissions to work properly as we had to setup proper authentication for this to work. We had to overhaul how we did things, and the fourth Wise One had to build a whole new database to store workspace installation configurations (which had tokens and everything per workspace). Even after all the re-engineering, it still wasn’t working!
__
What do you do when you’re not getting anywhere? You take a break and play some board games. The second Wise One brought along Here to Slay, which was heaps of fun. I might need to grab a copy myself some day!
__
During the game, we had a eureka moment and we managed to get the authentication flow to work correctly! It was 11 pm by then and time to retire for the night and recharge for the morning…
**__**
##### _It’s_ _alive!!_
After everyone headed home, I did some more cheeky testing and development to confirm everything was working. Everything was in a good spot!__
__
__
Once we re-convened early morning, we made it a priority to merge all the code together and doing some integration testing. It was mostly there, but there were a few bugs here and there that took us the rest of the day to fix — typical of the last 10% of work to do. We also had quite a bit more filming and editing to do, which we managed to do simultaneously as the engineering was happening.
In typical project fashion, everything was coming together in the final hour before presentation. We were filming the last scene, the videos were rendering, and we were trying to get some conversation going on the ElementX workspace. We managed to get it working live in the company workspace, without anyone noticing! We were happy. It was time to present…
Did we win in the end? No. But what we gained were awesome AI generated & directed videos and we had loads of fun making them. What we created was an awesome Slack bot waiting to wreak havoc on your team’s Slack workspace 😈
_Due to security restrictions, the Imposter Bot is not available to wreak havoc in your Slack workspace but you can learn more by_ [_visiting its website_](https://www.imposterbot.com/)_. If you'd like a live demo of the Imposter Bot, please contact us or the Wise Ones to see it in action._
---
## Is Google Vertex AI right for your project?
URL: https://www.elementx.ai/blog/is-google-vertex-ai-right-for-your-project
Published: 15 October 2021
Updated: 25 January 2024
**What are the advantages of using Vertex AI? Who should be using it? And why Vertex AI over other platforms?**
Are you in the process of deciding whether to implement [Google Vertex AI](https://cloud.google.com/vertex-ai)? Our Senior AI Developer, Nic von Ahsen, has put together this guide to choosing whether Vertex AI is the right fit for your project.
Google Vertex AI describes itself as ‘a unified UI for the entire ML workflow’; in simplistic terms, it’s a centralised platform for ML operations. It’s a great tool for companies who don’t have an AI developer specifically, are looking to reduce the hassle of employee turnover, or want to reduce the number of people needed for a project.
### Vertex AI has a lot of advantages: centralisation, accessibility, and reducing the amount of operational work needed.
Vertex AI as a platform promotes visibility and transparency, making it a great tool for a developer who’s new to AI and wants to see what their options are within ML operations. Its visibility and centralisation also makes it much easier to hand over your work to another person. If you need to swap developers, Vertex AI makes it easy to pick up where the last person left off because of a. how visible and streamlined the platform’s options are, and b. because Google requires you to work their way, meaning there will be less difference in two developers’ workflow and code.
Equally, it’s great for teams that want to hand over the maintenance once the initial project has been completed. Because of how accessible Vertex AI is to developers who are new to AI specifically, it allows for the ML developer to hand over the project to a company that doesn’t have a full time AI developer on staff. What once required a team of ten, now becomes a team of one or two.
Another big payoff for Vertex AI is that ultimately, if you play by Vertex AI’s rules and systems, a significant amount of the operational work is done for you. However, adjusting to Vertex AI’s way of operating can be a disadvantage in some cases depending on the developer.
### Vertex AI can be a hassle to transfer pre-existing code and workflows into.
Because you have to play by Vertex AI’s rules, it means you can become locked into Vertex’s way of doing things. If you’re bringing in old code or code you’ve developed before transferring to Vertex AI, you’ll have to redo it to fit in with Vertex’s. There may be difficulties working on certain tasks or libraries that don’t play nice with how Vertex AI works. This also means that if you build something with Vertex and want to transfer it outside the platform, it may not work either.
### So who should be using it?
There are three key groups of people who would benefit the most from using Vertex AI:
1. **Developers who are new to AI and don’t know what their options are within ML operations yet.**
Because of how centralised and clearly laid out Vertex AI is, it’s great for people who want to play around and learn what options are available within ML operations. A lot of your work can be done through a visual user interface and using AutoML, meaning you can avoid writing much code. The barrier to entry is reduced in that you no longer need a team to implement separate infrastructure; with Vertex AI, everything is in one place and accessible to developers to AI newbies.
2. **Small to medium enterprises who want ML architecture, but don’t have a team of people for it.**
Normally you would need an Operations developer to set up servers, a backend developer to set up APIs, a data-scientist or ML developer, and a method to facilitate communication between them. All of these are different skill sets, and these developers would normally be working in separate, decentralised platforms with little visibility within the different stages and tests. If you use Vertex AI right, you only need one AI developer to implement your project.
There’s also no worry around a developer leaving and having to transfer their work - with Vertex AI, it’s all in one place, and set to one standard.
3. **AI developers, data scientists, or researchers, who have either trained a model or have datasets and want to deploy them in a scalable, maintainable way, but don’t know their way around Cloud and operational tasks.**
Vertex AI aims to consolidate and simplify all the operational tasks related to deploying, versioning, and maintaining models and datasets. Data scientists and researchers who specialise in developing models often won’t have the operational experience to make the most of cloud providers’ offerings and operations best practices.
Vertex brings all of these tools into a single place with documentation and a nice user interface. As such, it’s a great way to start aligning your model with best practices when you’re looking to move out of the lab and onto the web.
### Why Vertex AI over other platforms?
It’s less a matter of Google Vertex AI vs. Stagemaker or Studio XX; none stand out more than the others. **The question should be more focused on why someone should be using one of them as opposed to not.** Vertex AI - as well as Stagemaker and Studio XX - should be seen more as a useful tool to developers within ML operations.
The benefits of having ML operations in a centralised location ultimately outweighs the downsides - though, this depends entirely on how skilled someone is within AI. A centralised ML operation works best for those new to AI, or people who already have models and datasets; a large team of trained developers would benefit more from their current system.
For companies already working within the Cloud, Vertex AI is an option for improving and simplifying operational processes, or to simply get into ML in the first place. The appeal of Vertex AI is in its lowered barrier to entry; with Vertex AI, having an ML operations developer or full-stack engineer is optional.
---
## Lunch and Learn: An Introduction to Reinforcement Learning
URL: https://www.elementx.ai/blog/lunch-and-learn-an-introduction-to-reinforcement-learning
Published: 15 August 2021
Updated: 15 September 2022
**This Lunch and Learn webinar provides a detailed overview of reinforcement learning, its applications and use cases, and a selection of resources you can explore to learn more about reinforcement learning.**
Recently, we hosted a Lunch and Learn session with Data Fusion Engineer and PhD Candidate, Jérémie Bannwarth. His discussion focused on providing an introduction to reinforcement learning: the area of machine learning focused on how intelligent machines (called “agents”) learn to interact with their environment to achieve a desired outcome. As Jérémie highlights, the agent’s behaviour is significantly influenced by its environment, as the rewards and punishments provided are dictated by the model.
Deep reinforcement learning is a natural progression of the simple agent model - in this scenario, the agent is a deep neural network which allows its decision-making process to be much more complex in its relationship to the reward, providing more complex routes to outcomes.
Jérémie’s talk provides a great introduction to the key aspects of reinforcement learning, including how sparse and non-sparse reward models can impact the agent’s response and how punishments impact behaviour, along with a snapshot view of use cases and tools available for reinforcement learning projects and some examples of how it can be used. He also provides some great resources should you wish to continue reading about reinforcement learning, which we’ve linked below.
### Resources to Learn More About Reinforcement Learning:
[David Silver’s Lectures with Deepmind](https://deepmind.com/learning-resources/-introduction-reinforcement-learning-david-silver)
[OpenAI Gym and Stable Baselines 3 documentation](https://stable-baselines3.readthedocs.io/en/master/)
[Reinforcement Learning by Sutton and Barto](https://www.bookdepository.com/Reinforcement-Learning-Richard-S.-Sutton/9780262039246)
Are you interested in hearing more about the business applications of AI technologies like Reinforcement Learning, Computer Vision, and Natural Language Understanding? [Get in touch](https://www.spark64.com/contact) to find out more.
---
## Improving CX with AI automation: Six opportunities to delight your customers
URL: https://www.elementx.ai/blog/improving-cx-with-ai-automation-six-opportunities-to-delight-your-customers
Published: 15 June 2021
Updated: 15 September 2022
**The relationship between AI automation and improved customer experience cannot be understated - here are six ideas you can employ to improve CX through automation.**
When we think of the benefits surrounding business automation, it can be easy to focus solely on operational results - things like improved first-call resolution rates in a contact centre, or increasing basket size with personalised product recommendations, or simply the removal of monotonous tasks that enable employees to do more meaningful work. In some cases, these improvements alone can make a compelling case for AI automation, but when combined with the profound customer experience improvements that come tied to these updates, the call for a solution becomes even more critical.
Every business is unique - each with different challenges, systems, and strengths, so identifying the perfect opportunity for AI automation can be difficult. Think about the flow of information through your business, and identify the bottlenecks and dependencies: repetitive, manual, or arduous tasks that your employees encounter regularly. These often hold opportunities for automation, especially as your business scales and keeping up with personalised service becomes more difficult to manage. Overlay this with the customer journey through your business, and you’ll likely find a few key priorities. To give you some ideas, we’ve pulled together a list of ways to improve customer experience through AI automation.
### Email Prioritisation
You’re likely already familiar with AI Email Prioritisation - email providers like Outlook and Gmail use Natural Language Processing (NLP) technology to sort spam, promotions, and updates into different categories, making your inbox easier to handle. On an individual scale, this is already significant - but if your business receives high volumes of queries - especially queries that are repetitive or have standardised answers, automating replies and triaging emails has an even greater effect.
In the insurance industry, email triage may involve linking an internal ticketing system to enquiries to automatically answer questions like; “What’s the status of my claim?” or “what is my premium for a windscreen repair?” These systems can be designed to process your unique business data, providing accurate information or distributing queries to the most appropriate department to answer a question.
### Document Processing
Documents present another challenge for businesses, especially those that may be relying on legacy systems. Using natural language processing to automate these systems can improve customer service by decreasing the time it takes to arrive at an outcome. If your business relies on this type of paperwork to deliver customer outcomes, this may be a perfect opportunity for AI automation. If your business is still processing hard copies, optical character recognition (OCR) can be implemented to digitise and then process these documents.
In the mortgage industry, we’re helping clients process applications more efficiently using this type of AI automation. This relieves operational pressure on the business, as well as providing the mortgage applicant with an answer to their application quickly and easily.
### Contact Centre Support
AI Automation has a huge range of applications within contact centres - and in some business structures, the Email Prioritisation solution we’ve already discussed may fall into this category. However, there’s much more to consider! Voice or text chatbots can provide conversational support to customers, answering simpler queries so that human agents can more quickly resolve complex customer concerns. Additionally, Agent Assist programs can be developed that process caller details and provide information directly to call centre employees, giving them access to real-time answers to complex questions like “Can you tell me your pricing” and eliminating the need for agents to search knowledge bases manually for information.
In the education sector, we’re helping clients answer student queries that have multiple dependencies with a system that understands these complexities and can present the correct information in mere seconds, decreasing resolution time and improving interaction quality.
### Product Recommendation Engines
Product recommendation can take many forms - if you’ve ever shopped online for clothes or shoes, you’ve most likely come across at least one scrolling banner entitled “You May Also Like!” with related products - often more clothes or shoes. When used effectively, these tools can improve conversion rates and basket size, but they’re often manually created. This approach doesn’t scale, and it’s not personalised per customer. For example, if you’re like me, you’ve probably also received an email recommendation for something like a fitted sheet set a day or two after purchasing the very same set in a different size. AI Product recommendation engines can prevent this type of unhelpful repetition.
Recommendation engines create robust lists of products based on a list of pre-set qualifiers. This adds a critical layer of intelligence to the process - not recommending sheet sets to those who have just purchased, but identifying that they may be interested in pillows or duvet covers. They can also be built around specific gift giving occasions, with prompts like “Father’s Day Gifts for outdoors-lovers” to drive holiday purchase conversion.
We’re working with one of New Zealand’s largest retail businesses to drive conversion and improve basket size by helping them provide customised recommendations for a variety of gift recipients.
### Analytics and report distribution
If your customer journey is largely digital, you likely already have a wide range of analytics at your disposal to outline key metrics like conversion rate and speed, lifetime value, and churn - but can you accurately predict customer behaviour? Implementing AI predictive analytics can be a valuable way of gathering critical information.
Opportunities in this area include using NLP to identify and report on critical customer feedback before a customer makes up their mind to leave, and using machine learning to analyse behaviour paths that lead to churn so that you can take steps to retain customers before they’ve even actively thought about leaving. In this digital age, companies can’t afford to let these kinds of insights go unnoticed.
### Condition Monitoring with Computer Vision
We’ve included this bonus example to show you that the opportunities for AI automation truly are only limited by your imagination! If you’re just starting your journey towards automation to improve customer experience, you likely won’t land on Computer Vision as a solution - but it’s a critical step for some businesses!
Computer vision can be used to identify conditions like flow patterns in retail stores (providing data on the best arrangement of both shelving and product), as well as in object detection to enable accurate sorting and packaging on production lines. We’re working on a solution in the meat industry that automates the process of identifying and sorting meat cuts to improve packing line production. This reduces costs and also improves packaging accuracy, providing a better outcome for the customer. If you have a blue-sky idea you’d like to see implemented in your business, get in touch!
### Where does AI Automation fit into your CX Strategy?
These opportunities only represent the tip of the iceberg. The ways in which AI automation can be used to improve customer experience truly are only limited by your imagination. When considering opportunities to automate business processes, we suggest starting by thinking about the most manual and repetitive aspects of your business. How could automation help your employees work more efficiently? What benefit would this provide to your customers?
In working with businesses that are new to AI automation, we’ve found that it helps to begin with projects that will work seamlessly with the rest of your processes. Introducing additional layers of complexity is exactly the opposite of what we hope to accomplish! Instead, the result should be a streamlined, effortless automation that leaves your staff - and your employees - delighted.
If you have an idea for an AI automation project for your business, we would love to hear from you! [Get in touch with us here.](https://www.spark64.com/contact)
---
## TechWeek 2021: Integrating AI into your Contact Centre
URL: https://www.elementx.ai/blog/techweek-2021-integrating-ai-into-your-contact-centre
Published: 15 May 2021
Updated: 14 October 2022
**ElementX held a Techweek 2021 webinar highlighting you how you can integrate AI into your Contact Centre. We demo GPT-3, phone bots, UneeQ Digital Humans and more!**
---
## Building Chatbots People Actually Want to Use: Customer Perspectives on Chatbots
URL: https://www.elementx.ai/blog/building-chatbots-people-actually-want-to-use-customer-perspectives-on-chatbots
Published: 15 April 2021
Updated: 15 September 2022
**With all the information on how to best build Chatbots, there's surprisingly little that tells us what customers actually think about them. This article will give you some insights on customer perspectives on Chatbots and how businesses can best facilitate their adoption.**
The method with which websites and other communication channels are created have in many ways been designed to push away conversations, forcing users to adapt and fit into these rigid structures, languages and technologies. **Chatbots provide us with a new framework of designing and interacting with our digital and online experiences.** These frameworks flip the model that we’ve accepted as the norm - instead of humans trying to fit around the technology, the technology adapts and responds to us. Seeing as we are so diverse in our experiences, backgrounds, access needs and life stages, having technology that adapts to us makes a whole lot of sense!
However, the technology is still in its infancy. We’ve got a way to go in order to fully understand how we can get the best out of each other. Emerging research on the psychological side of human-computer interaction can shine some light on how real customers perceive Chatbots. In this article, we’ll provide insights into true customer perspectives on Chatbots, as without understanding the real human needs for this novel technology, we’re bound to face a lot of avoidable mistakes and failures.
> New to Chatbots? Check out [our detailed article on exactly what they are](https://www.spark64.com/post/what-are-chatbots).
#### **Previous experiences can taint future perception**
This one is pretty obvious, but important to point out; customer expectations of Chatbots vary based on previous experiences. Chatbots have suffered from the same evil that all new technologies face - emerging technologies have no baseline and therefore customers are as likely to have had a negative experience as a positive one. This means that we need to put a little bit more time into changing their perspective as the technology evolves.
#### **Level of tech savviness**
Digital natives, technology early adopters and tech professionals will be more open to using Chatbots. In saying that, one of the major benefits of a Chatbot is that it resembles instant messaging apps that the majority of customers will be familiar with using. This makes it easier to motivate customers to interact with a Chatbot, but more needs to be done to build a trusting relationship, so people will allow them to perform tasks, assist them or provide advice.
#### **Nature of the task will distort perceptions of Chatbot capability**
Our perceptions on what Chatbots can do, and what we are comfortable with them doing changes depending on what tasks your customers require. As a general rule, customers won’t want to use a Chatbot if the task has a high emotional load, has costly risks and/or is urgent. For instance, customers may feel uncomfortable telling a Chatbot to send money to a friend but be completely fine with calling customer service to assist or to do it online themselves.
#### **Effort required to use the Chatbot**
The amount of effort it takes to make a problem known and understood by a Chatbot, and the level of typing necessary, impacts on whether a person is likely to use one. Customers feel that it’s easier to use a more familiar channel or interface than a Chatbot if the effort exceeds the benefits, or the task feels too complex for a Chatbot to handle and execute.
Another implication is that if the Chatbot can’t help, the customer has wasted time and energy on a task that then needs to be repeated to a human to fix. People feel that going directly to a human for help will bypass the risk of having to repeat themselves, and avoid added frustration.
#### **Knowing how to speak Chatbot lingo**
Knowing how to make a Chatbot understand you can be an art. We’ve all experienced this when asking a Chatbot for help and it tells us that it doesn’t understand, can’t help, provides incorrect information or sends us on an infinite loop (cue: table flip!).
Chatbots that tell customers how to speak to them, what keywords are helpful, what they specialise in, or enabled clickable pre-populated answers are a great first step. Customers don’t want to have to negotiate their needs.
#### **Leaving a trace and providing non-bot confirmations**
The lack of accountability and trackability of actions completed with a Chatbot can trigger fear in customers. What happens with their conversations, and where they can access records of their Chatbot interactions?
This is important for building trust and confidence in human-bot interaction, as the customer then feels they have a safety net or hold the Chatbot accountable, should something go wrong. It’s particularly important with newer technologies, as mistakes are bound to happen during the teething period.
### **The closest we can get to getting Chatbots right,
is to involve the customer**
We’ve talked about some aspects that can impact people’s perceptions of Chatbots and whether they will use them. But how can businesses cater to these underlying issues and mitigate them faster?
One overarching answer is to **apply user/human-centred design methodology or co-design**. Both approaches ensure that the customer is placed at the heart of every decision made in the research, design and development process. The added benefit of co-design is that the **customers actually facilitate and actively contribute** to the research and design process - they are the designers themselves.
My favourite example is from Australia, where the [NDIS co-designed the Digital Human, Nadia,](https://www.abc.net.au/news/2017-09-21/government-stalls-ndis-virtual-assistant-voiced-by-cate-blanchet/8968074) to help their clients with disabilities to access their online services. They found that co-design “produces natural contextual human-like embodied conversations: the ebb-and-flow of natural conversational interactions that are impacted by illiteracy, disadvantage, and bureaucratic and technical language” ([Centre for Digital Business, 2020](https://medium.com/@mariehjohnson/human-conversations-and-digital-humans-not-just-a-pretty-face-d5abfaaa2c5f)).
**Chatbots and Digital Humans have the immense potential to be incredibly empowering technologies - if we get it right.
**
#### Provide Value-Add
At the core of it, we need to understand what’s at stake for the customer and remember to provide value-add. If people are content with the status quo, what extra, unique and valuable things can your customers do with the bot rather than other methods of interacting with your product/service? While you’re at it, make the benefits really clear, otherwise they will fall back on the usual ways of doing things, existing habits.
Look for new opportunities and problems that exist in your business, and explore how a Chatbot could be helpful in this situation, but also how it could be a hindrance. Be honest with yourself and team about the actual value. The adoption of new technology is always difficult, and the last thing you want to do is contribute to the problem of abandonment.
**Create design sprints and research experiments to answer some critical questions, for example:**
- Where’s your Chatbot sweet-spot and what tasks fit there? The sweet spot is where customers have a real-world need for the Chatbots, but also provide a more desirable experience? Think of specific use cases and context to prototype and test around.
- Which markets and populations are excluded by your products and services and how can a Chatbot unlock these new customers?
- How could we make these experiences more human-like and naturally conversational?
The questions you ask will inevitably vary depending on the product/service/market. Chatbots have a unique and important opportunity to make our online experiences seamless, equitable and accessible. As long as we ask ourselves the important and necessary questions surrounding their use cases and to uncover what people value and desire. If we don’t, then we risk repeating the same mistakes, where information is made available in the context of the _organisation and its structures or products_, instead of the context of the _customer_.
> Want to build a Chatbot your customers actually want to use?
> Check out the [Chatbots](/customer-stories/cove-insurance-chatbot) we have created and [contact us](/contact)!
---
## [AI]nnovation News: Feb 2021
URL: https://www.elementx.ai/blog/ai-nnovation-news-feb-2021
Published: 15 February 2021
Updated: 14 October 2022
**Things in the AI world keep on moving forward, don't they? In this instalment, ElementX highlights the emerging AI technologies across fashion retail, healthcare, photography and more!**
Day-by-day the world of Artificial Intelligence continues to change and grow in the most amazing, revolutionary and even sometimes controversial ways. Staying up to date can be so difficult!
That’s where we come in. ElementX keeps a constant finger on the AI pulse. Our team gathers regularly to share news of the latest AI tools and use cases. Here’s the latest we’ve recently discovered in fashion retail, healthcare, photography and more.
#### **DEEP NOSTALGIA BRINGS FAMILY PHOTOS TO LIFE**
MyHeritage has released their [Deep Nostalgia tool](https://www.myheritage.com/deep-nostalgia) to animate faces in family photos. With the power of deep learning, the service is exceptionally good at smoothly animating facial features and expressions. The AI-powered results are surreal - seeing a lost loved one smiling and winking can be an emotionally moving experience… Or potentially odd and unnerving?! You can decide for yourself! Beyond family photos, many have used this tool to reanimate historical paintings and statues for remarkable and hilarious results.
#### **MORE RESTFUL NIGHTS WITH NANIT’S AI BABY MONITOR**
[Nanit has raised another $25 million](https://techcrunch.com/2021/02/22/nanit-raises-another-25m-for-its-ai-powered-baby-monitor/) for its innovative baby monitoring system. Founded in 2014, Nanit has the only baby monitor that connects parents to their baby's health and well-being as it has features which utilise [computer vision](https://www.spark64.com/services/computer-vision), such as breathing motion monitoring and night vision.
The recently launched [Nanit](https://www.nanit.com/global/) Pro also allows parents to measure their baby’s height, for growth tracking between doctor visits. This could alleviate many anxieties and concerns over keeping a constant eye over their loved ones, providing more restful nights for parents. The company intends to broaden its product ecosystem with items like changing pads and nightlights, that can be amplified by the Nanit app experience.
#### **SHAZAM BUT FOR LUXURY BAGS?!
THE WORLD'S FIRST IMAGE RECOGNITION TECHNOLOGY FOR DESIGNER RESALE**
The online designer fashion resale platform [Rebag](http://www.rebag.com) has launched Clair AI, the world’s first image recognition technology for luxury resale. Short for Comprehensive Luxury Appraisal Index for Resale, the software uses computer vision to instantly recognize and relay the resale value of designer handbags across a list of over 50 brands, recognising over 15,000 existing references with 91% accuracy. Clair AI can help customers identify the value of their luxury handbags and decide whether to hold onto them for investment posterity, sell or trade through Rebag and other channels, or to even buy more of them!
#### **AN AI-DRIVEN HUMAN RESOURCES DEPARTMENT**
China-based [Bello](https://medium.com/behind-the-great-wall/bello-choosing-human-workers-with-ai-4df334e6f079) provides an AI-driven human resources management system for small to medium-sized companies. The main problem Bello tries to solve is the amount of time companies spend searching for and selecting job candidates, leading to high recruitment costs. The company claims that its software can analyse 25 English or Chinese resumes within one second, taking that data to highlight job history, education background, risk factors and even exaggerated claims or falsified credentials. Bello could help many companies recruit candidates quicker and cheaper by reducing repetitive and tedious tasks, saving time and labour costs.
#### **A FASTER WAY TO FILTER THROUGH PHOTOS**
One of New Zealand’s fastest-growing start-ups, [Narrative](https://narrative.so/), is building AI tools for professional photographers to slash their time in the editing suite and send them back out behind the camera. Backed by some of the largest venture capitalists in the US and NZ, Narrative seeks to speed up, improve and simplify a photographer's workflow. Their professional culling tool [Select](https://narrative.so/select) (now out for free on Beta!) uses industry-leading computer vision and machine learning to understand photo desirability, helping photographers leave out photos that are unfocused and blurry, or quickly find faces of subjects to easily identify them blinking or looking away.
#### **LEARNING MORE FROM YOUR MICROBIOME**
The human microbiome composed of communities of billions of microbes, bacteria, viruses and fungi live on the human body, helping to maintain our skin condition and defending us from external pathogens that can harm us. IBM has shared research highlighting the [use of AI to explain your body’s microbiome](https://www.ibm.com/blogs/research/2021/02/ai-explains-microbiome/), identifying your age and other factors such as whether you’re a smoker or menopausal. These new insights offer the potential to support the development of personalised treatments for healthy skin. A better understanding of our bodies’ microbiomes could help improve overall health and wellbeing, by accelerating the development of treatments, including prebiotics and probiotics.
---
## How Do I Choose a Conversational AI Platform?
URL: https://www.elementx.ai/blog/how-do-i-choose-a-conversational-ai-platform
Published: 15 February 2021
Updated: 14 October 2022
**Conversational AI is steadily becoming synonymous with great Customer Service. But with all the platforms available out there, how is your organisation supposed to choose the conversational AI platform that best suits your needs? In this blog, we go through the key aspects you need to look out for in your conversational AI journey.**
> "Which conversational AI platform should I choose?"
We get asked this question A LOT.
Unfortunately there's no single, nor simple, answer to this question.
It's a big decision for any organisation embarking on a conversational AI journey, whether in the form of a chatbot, digital human, or smart IVR solution, and one that is important to get right at the beginning of the project to save the headache and regret in the future.
Our team at ElementX has worked with several of the major platforms in the past and we want to answer the common questions that crop up and share what factors you should consider with choosing a conversational AI platform.
##### Starting with the simple stuff:
#### What is a conversational AI platform?
If you're building any digital assistant, you're going to need an engine to interpret the user's query and respond appropriately. Conversational AI platforms provide you with the platform to do just that, wrapping all of the latest advancements in natural language processing and tried and tested patterns for designing your dialog.
They're also known as chatbot platforms, but I prefer the term conversational AI because they can be used in solutions far beyond chatbots; digital humans, sophisticated IVR, and even as part of a web/mobile application for interpreting search queries!
#### What's out there?
Since 2016, there has been an explosion of conversational AI platforms available that range from simple drag drop interfaces to full blown programming framework allowing you to implement a custom solution in code. These are offered by open source communities, large cloud vendors, contact center software providers, and also independent software providers (some of which have been rolled up into a cloud offering).
#### Why not build your own?
Unless you're in the business of building conversational AI platforms, a lot of common patterns and requirements have already been thought out by the existing solutions. Even if you're concerned about data security, there are solutions you can self-host and never leave your premise or private cloud environment. Virtually, all conversational AI platforms can integrate with your backend so there's really no need to go full custom.
### Considerations
##### These are the questions you should ask yourself before picking a platform:
#### How complex are the dialogs?
Is the bot going to be a simple FAQ bot, where it's a single answer for each question? Or do you intend to take the user down a conversational journey with various paths, depending on their responses along the way?
We've seen two main types of platforms. There’s platforms that make it very easy to build simple dialogs, but get more complicated when deviating beyond. Then, there’s those that make complex dialogs very easy to manage, but may have a higher initial learning curve and some options that might not make sense to someone less familiar with the platform.
#### Who's going to maintain the dialog and content, once in production?
The analyst, contact center staff, developers, or both? One of the major factors of success is the ability to iteratively improve on the conversational ability of the digital assistant over time.
Some of the solutions are designed to be used by more business facing users, whereas some are intended more for developers. Traditionally, it would be an inversely proportional to the complexity of the dialogs you can build on it, but some of the platforms these days strike a good balance between the two.
#### Which contact center or helpdesk solution are you currently using?
In many applications, it is critical to be able to hand the user over to a human agent for complex questions. What's worse than having your users get stuck in a dead end, and have to call in anyway with one extra complaint?
Some of the platforms have out-of-box integrations with helpdesk solutions, which will save you heaps of time in building your own (which is possible with almost any option).
#### Other minor considerations
##### Some of the other less important considerations are:
- The cloud provider you're already on - Obviously it is nice to have it on the same platform as the rest of your infrastructure, but most of the platforms are quite stand-alone and not hugely tied into the cloud provider that's offering it.
- Budget - None of the solutions charge exorbitant fees, and all scale with the number of users talking to your assistant. Having said that, some solutions are considerably more expensive than others, all with good reasons such as a reduction of time for operational staff to maintain it.
### Which is the best conversational AI platform?
There's far too much for us to go into in this blog post, so we've created a report containing a review and comparison of the platforms. This has been put together with the top business questions in mind, as presented above. You can download it here:
> [Which Conversational AI Platform Should I Choose? A broad review & comparison](http://www.spark64.com/whitepapers/conversational-ai-platforms)
In this report, we will be focusing on the conversational AI platforms that are backed by the top cloud vendors: AWS, Google, Microsoft, IBM. These have been around long enough to be considered quite mature, and are amongst the most popular. We’ve covered:
- Amazon Lex
- Google Dialogflow ES and Google Dialogflow CX
- IBM Watson Assistant
- Microsoft Bot Framework / LUIS / Composer
Again, this is not an exhaustive list; there are plenty others out there as well (notably Rasa) - some tailored for specific use cases or integrated directly with a particular contact center suite. If you've found one you've really liked, [we'd love to hear about it](https://www.spark64.com/contact)!
### Where to from here?
If you're embarking on a conversational AI journey, it is worthwhile taking some time to pick the platform you're about to put the time and effort building content on, and training staff to use. We've put a few comparisons and commentary on some of the top conversational AI platforms out there and we hope it is useful for you to start your journey.
ElementX has experience in using all of these platforms. As a tech-agnostic AI agency, we will guide you to select the best platform to suit your company’s needs and specifications. Feel free to [reach out to us](https://www.spark64.com/contact) if you want to have a chat about your own requirements and we can give you an impartial opinion on which you should choose!
#### [Find out which is the best conversational AI platform for your business](http://www.spark64.com/whitepapers/conversational-ai-platforms)
---
## [AI]nnovation News: Jan 2021
URL: https://www.elementx.ai/blog/ai-nnovation-news-jan-2021
Published: 15 January 2021
Updated: 14 October 2022
**The world of AI is rapidly evolving - it's hard to keep up! In this recap, ElementX highlights what innovations are developing and emerging across health, finance, fashion and beyond.**
The world of Artificial Intelligence is evolving at an exponential rate - it can be difficult to stay up to date with what's happening! As AI is ElementX’s primary focus, our team gathers monthly to share and collaborate over the latest and greatest (and weirdest) AI tools and use cases. Here’s a recap of what we’ve discovered in health, finance, fashion and beyond.
#### Dropbox slashes costs with machine learning
Dropbox has converted the [predictive power of machine learning](https://www.spark64.com/services/data-science) into a massive cost savings for the organisation. By optimising the Dropbox Previews feature, which allows users to view a file without downloading the content, they have created an annual [$1.7 million infrastructure costs savings](https://dropbox.tech/machine-learning/cannes--how-ml-saves-us--1-7m-a-year-on-document-previews).
> _“While not all our ML applications are directly visible to the user, they still drive business impact in other ways.”_
###### \- Win Suen, Machine Learning Engineer, Dropbox
#### Finessing and financing the future of fashion
The days of fashion trends being solely dictated by haute couture houses and traditional fashion publications, like Vogue and Elle, have long gone with social media and online influencers paving the way in what’s en vogue. Startup [Finesse](http://www.finesse.us) have raised $4.5 million in funding to [predict fashion trends with AI](https://techcrunch.com/2021/01/27/finesse-launch-seed-funding/), in an effort to reduce the immense amount of waste produced by the fashion industry and streamline design and manufacturing decisions.
#### Deep learning for early eye disease detection
Diabetes Singapore has deployed an [AI system to detect early signs of diabetic eye conditions](https://www.straitstimes.com/singapore/diabetes-singapore-deploys-ai-technology-to-screen-patients-for-early-signs-of-diabetic). Known as Selena+, the deep learning technology has slashed processing time for eye scans detecting three types of eye disease from an hour to just a few minutes. This has empowered them to strive to conduct 11,000 screenings in the upcoming year, compared to 8,000 conducted last year.
#### FinRL = Automated Stonks?!
With the craze surrounding GameStop shares and r/WallStreetBets, we’ve discovered [FinRL - a deep reinforcement learning library for quantitative finance](https://github.com/AI4Finance-LLC/FinRL-Library). Imagine creating a tool that can automatically trade stocks for you...!
#Stonks #DiamondHands #ToTheMoon
#### A new and UneeQ platform for personalised customer experiences
Digital humans are popping up all over the place - [we've helped build a few ourselves](https://www.spark64.com/project/worlds-first-mortgage-lending-digital-human-assistant)!
[UneeQ Creator](https://digitalhumans.com/creator/) has been launched, giving you the ability to design your own customised, AI-powered digital human into everyone’s hands regardless of coding ability.
#### Facebook AI expands speech recognition research beyond English
Facebook AI is set to release [Multilingual LibriSpeech](https://ai.facebook.com/blog/a-new-open-data-set-for-multilingual-speech-research/) (MLS). This is a large-scale data set that will help advance research in automatic speech recognition. Designed to help the speech research community’s work in languages beyond just English, MLS contains more than 50,000 hours of audio across eight languages: Dutch, English, French, German, Italian, Polish, Portuguese, and Spanish. With this open source library, more people around the world can benefit from improvements in a wide range of AI-powered services.
---
## When AI Goes Wrong
URL: https://www.elementx.ai/blog/when-ai-goes-wrong
Published: 15 December 2020
Updated: 15 September 2022
**Like any emerging technology, AI can be vulnerable to exploitation and attacks.**
---
## Human or Chatbot?
URL: https://www.elementx.ai/blog/human-or-chatbot
Published: 15 October 2020
Updated: 13 September 2022
**How to tell if you're talking to a chatbot or a human? We shed light on the different types of customer messaging support. **
The Turing Test was one of the original thought experiments about the philosophy of artificial intelligence. This is the scenario - you’re sitting at a computer and having two conversations by typing into two different boxes on your screen. In one conversation, a real human person is responding to your messages. In the other conversation, a machine “designed to generate human-like responses” is responding. Can you tell the difference between the two, and identify the human and the machine correctly?
The Turing Test has become a benchmark for AI developers - if they can fool human users into thinking that their chatbots are other humans, then they’ve succeeded. This may sound eerily familiar, with modern-day chatbots making it really hard to know whether we are talking to a person or an algorithm. The most common place where this happens is in customer support systems on the internet. On millions of websites around the world, you can ask companies about their products or how their websites work, often through a chat window that looks like an instant messaging system at the bottom-right corner of the screen.
But how can you tell if you’re talking to a human or a chatbot? There are actually three types of systems that are commonly used by these websites:
- Human-only: where real humans are responding to your messages
- Chatbot-only: fully automated system with no human intervention
- Hybrid: using a chatbot as a triage tool for simple questions, and passing on more complex or difficult questions to a human operator
To understand how we might be able to figure out who or what is responding to you, we should understand how chatbots work first. There are a couple of pieces of technology that get packaged together:
1. Our message goes through Natural Language Processing (NLP). This stage unpacks your message and tries to convert your message from plain English into a structure that the computer can process and understand. For example, it will try to separate the nouns, verbs, and adjectives in your message, so that it can figure out what the key subject of interest is.
2. The key terms go into a decision system, where the algorithm tries to figure out what sort of response it should give. For example, if you ask about the shipping cost for a particular item, then the algorithm goes through a database of information that the company has, and grabs the price. This part of the system is also responsible for maintaining a conversation and keeping track of previous messages - for example, if you asked about the weather in Auckland, and then asked about the weather next weekend, the chatbot should be able to remember both elements and localise the response correctly.
3. The chatbot responds with some text. The really sophisticated chatbots are automatically generating their responses in real-time based on the information in the decision system (give [Cleverbot](https://www.cleverbot.com/) a go). However, the vast majority of chatbots currently use pre-written messages from humans, where people have tried to guess what users might ask and have crafted responses for the chatbot. This is sort of similar to a script that a call center operator might use to ensure that there is a consistent and comprehensive experience across customers.
Understanding this process gives us a couple of clues about how we might be able to figure out if we are talking to a human or a chatbot. Each of these automated stages can suffer from some sort of failure, where the algorithm doesn’t quite operate the way we’d like it to.
At the first stage, if the NLP system fails, then the chatbot can reply with something that is completely irrelevant to what you asked. For example, you might ask a chatbot about the company’s privacy policy, and it tells you about public policy instead. Maybe the chatbot only saw the word “policy”, and didn’t realise that “privacy” was also an important part of that noun. It’s the sort of mistake that you wouldn’t expect a human to make, unless they were really incompetent (in which case, good luck getting help from them anyway!). Thankfully, these errors are less common nowadays, because NLP has become quite sophisticated and is pretty accurate (so this tends to only happen in older, outdated systems).
The other type of error at this stage is where NLP can’t figure out what you’re talking about at all, often when your message is very short. This is most obvious when the chatbot says “sorry, I didn’t understand that” and leaves users frustrated because they have to try and rephrase their question in a different way.
The second stage (decision system) is where things get interesting. A common scenario is where you ask about something that the chatbot has no knowledge about - whereas a human might say “give me a moment while I try to find out”, a fully automated chatbot may not have a human to ask, and can only say “sorry, I don’t know the answer to that.'' If you ask something that’s even a little bit outside of the chatbot’s body of knowledge, it will often just give up. Another is where the chatbot loses track of the conversation, and forgets things that you literally just talked about five seconds ago:
In the third stage, most chatbots currently use pre-written, canned responses. This means that if a chatbot starts repeating itself, you can be pretty sure that it’s not a human. If the answer doesn’t actually answer your question and just directs you to somewhere else, that can also be a sign that a pre-written response is being used. It can be very time-consuming and laborious to write all the possible responses, so often developers just don’t bother. It’s hard to have a conversation with someone when they only know how to say a few pre-written responses!
Grammar errors like speaking in the wrong tense given the question can also be a dead giveaway, because the pre-written response can’t dynamically respond to subtle changes in the question. However, if a system is responding with spelling errors, then that’s probably a human doing typos in real-time! Spelling errors were one of the signs that exposed [Zach](https://thespinoff.co.nz/the-best-of/06-03-2018/the-mystery-of-zach-new-zealands-all-too-miraculous-medical-ai/), an AI that was supposed to be able to interpret ECG results and patient notes, but turned out to very likely be a 25 year old in Christchurch with a penchant for deceptive marketing.
Lastly, the most obvious tell-tale sign is the response time - if it takes more than a couple of seconds for the system to respond to your message, then there’s probably a human behind it running a live chat operation. Systems like [Intercom](https://www.intercom.com/) just provide an interface for human customer support agents, and can tell you what the average response time is for that company (minutes, hours, or days). This is actually a good thing - human responses tend to be more accurate even if they are slower.
For now, chatbots still have many errors. While many vendors claim to have systems that can beat the Turing Test, when used in the real-world, there can be many contextual factors that give the game away. Fully automated chatbots struggle with maintaining contextual conversations with users, and unless they know when and how to escalate conversations to human operators, they frustrate customers and lead to lost sales. We still have a long way to go before we can hand over all of our customer interactions to the chatbots. The key lesson here is that humans still need to be kept “in-the-loop” - don’t move to full automation too quickly!
_Keep an eye out on our blog, as we will continue to cover chatbots in more depth, including the top providers in the market right now and how to pick one for your business. We will also be writing about other forms of artificial intelligence and machine learning, focusing on practical and real-world applications of this exciting technology._
> _Want to incorporate a chatbot onto your company's platform?
> Or just want to find out a little more?
> _[_Contact us_](https://www.spark64.com/contact) _and we'll get you sorted._
---
## Can Computer Vision Solve My Problems?
URL: https://www.elementx.ai/blog/can-computer-vision-solve-my-problems
Published: 15 August 2020
Updated: 15 September 2022
**Ever wondered if Computer Vision could help you? We'll walk you through the questions you need to answer, in order to assess whether it is the right solution-fit for your business/technical problems.**
With all the hype around artificial intelligence and computer vision, it’s easy to fall into the trap of thinking that everything can be automated and every problem can be solved. Unfortunately it’s not quite as easy as that - our previous blog posts have discussed why computer vision is so challenging from both a mathematical and philosophical perspective, and why we can’t expect computer vision to be as good as biological vision (yet). But there are real applications of this technology, and it’s clear that computer vision is working in some places. **__**
__**_What sorts of questions should we be asking to figure out if computer vision is a good solution-fit for any technical or business problem?_**
#### **1\. Is there underlying discriminability?**
A lot of machine learning and pattern recognition is based on the idea of being able to distinguish (i.e. detect or recognise) things from other things. For example, you might have video of rugby players on the sports field. Can computer vision distinguish the players from the crowd in the stands, or from the grass on the ground? “Discriminability” is a technical term that indicates how easily we can do this separation and distinguish different things.
Another example might be an app that looks at photos of shoes. Is there enough detail to separate photos of sandals from boots? What about separating photos of Nikes from Adidas? Assessing discriminability doesn’t necessarily require technical knowledge - it is about understanding whether or not there is enough underlying difference in the visual information to expect a computer vision algorithm to produce a reasonable result. We might logically reason that there are significant visual differences between sandals and boots, like the amount of material and the style of shoe, but sneakers made by Nike or Adidas might look similar enough to cause some misidentification or confusion.
An example of a case where there isn’t sufficient discriminability is the recent controversy around the computer vision “[gaydar](https://www.nytimes.com/2017/10/09/science/stanford-sexual-orientation-study.html)” that claimed to [determine the sexual orientation of people](https://www.theregister.co.uk/2019/03/05/ai_gaydar/) based on photos of their face alone. Putting aside the ethical issues and quandaries associated with this research, subsequent replication of the study has shown that there are actually no underlying visual features that can distinguish whether someone is gay or not. In other words, there is no visual discriminability - there’s no data or information basis for an algorithm to make a robust and repeatable decision. There may be other tools or data sources we might be able to use (for example, social media habits) but computer vision is simply the wrong tool for the job.
####
#### **2\. Do you have enough training data?**
Computer vision relies on having a good model of knowledge - for example, what does a chair look like, and what makes it visually different from a table? In order to build a model, we have to give the algorithm examples of the data that we want it to analyse. Think of it like reading a picture book to a toddler - we point at the cat in the book and say “this is a cat”, and eventually the toddler understands that the picture corresponds to the word cat. But imagine if we only ever showed that toddler one picture of a grey siamese cat, and then later in life they come across an orange tabby cat - would they be able to also identify it as a “cat”? The problem is that the word “cat” corresponds to a wide range of visual information with a lot of variety between size, colour, shape, breed, and so on. To only show the toddler one example of the cat is to limit their understanding of what a cat is.
The same thing happens with machine learning - if we ask the algorithm to build a model of knowledge from a single instance of data, then that model will be limited in its real world applicability when we show it real data that doesn’t quite match the original data. There is a lot of research effort being applied towards solving this problem (you might hear of terms like online learning, semi-supervised learning, or one-shot learning, which refer to algorithms that use very little amounts of data to build a model and then adapt over time). But at least for now, to successfully use a computer vision model you need a lot of training data to help the algorithm build a robust model that emulates the real world.
At a minimum, this means thousands (if not hundreds of thousands) of photos, segmented or labelled with the things you are interested in. If you wanted to use computer vision to monitor fishing stocks in a salmon farm, then you would need some photos segmented by a human to indicate where in the image the salmon are, and then a label that says how many fish there are in that image. When we feed these images into the algorithm, it uses the human-given labels as the “correct answer” and tries to learn so that the internal model represents those correct answers. If you don’t have access to existing images of what you’re looking for, then it will be very difficult for a computer vision algorithm to produce good results. Even if you do have images, you need to be prepared to face up to the cost of getting humans (who in some cases need to be domain experts) to label those images.
#### **3\. How good does the algorithm need to be?**
A lot of engineering depends on the tolerances around accuracy. If we are building a fruit sorting machine that separates apples and oranges, a couple of mistakes here and there probably won’t make a huge difference and people can pick out the wrong fruit later on. If we are building a surgery robot that cuts out brain tumours, then an error of even one millimetre could kill the patient. So we need to understand - what sort of accuracy rates do we need, and what is the cost or consequence of a mistake?
It is very rare for a computer vision algorithm to be 100% accurate. In fact, there are some researchers that argue it is impossible for a modern deep neural network-based algorithm to be 100% accurate. From a statistical perspective, unless we show the algorithm every single possible data combination when it is building the model, there will be some error in how the model represents the real world (even if it is very small). And if we had training data available for every possible case, we could easily solve the problem without the use of a deep neural network.
In some target applications the accuracy rates can be quite good - optical character recognition for printed characters is pretty close to 99% accurate and considered “solved”. But there are still a lot of challenging areas - determining whether a person is attacking another person in a piece of video footage is under 30% accurate. So before we expend a lot of effort into developing a computer vision algorithm to solve a problem, we should understand what sort of accuracy rate we might expect based on the performance of algorithms in similar or adjacent tasks, and what level of accuracy rate is acceptable for the end user. If these two don’t match, we can save a lot of time and money by moving on and trying to solve the problem in a different way. Developers can optimise their designs and work hard to incrementally increase the accuracy of a computer vision system, but this comes at a significant development cost and managers need to ask the hard question of whether an increase in accuracy of 0.1% is worth the spend.
#### **4\. What is the technical environment? what other information do we need to add?**
Apart from designing the computer vision system itself, it’s important to consider how ready the rest of the technical environment is. AI systems consume data, and so ideally there is an automated way to collect and feed in the data. This can be a non-trivial task - we could consider an application where a farmer wants to use a drone to monitor where their cattle are on a daily basis. If the farmer has to manually pilot the drone, pull an SD card out when the drone returns to base, copy-and-paste a file onto the computer, connect to expensive satellite internet, and then upload the video footage to a web portal, then the farmer may opt not to use the system at all. Interoperability of technical systems can be surprisingly difficult, so we need to evaluate how easily the data can be collected and fed into the algorithm.
Additionally, the output of an AI algorithm is rarely directly usable - we might need to combine it with other data or information in order to make a complete decision. For example, we might have a computer vision system monitoring bus stops to figure out how many people are waiting for buses. Normally they send three buses down a particular route at 3pm, but based on the actual number of people waiting, an AI system might recommend that they only send two buses instead and redeploy the third bus somewhere else. The manager likely needs other information to make a decision, like how many staff and buses are currently available, whether there are any special events happening in the area, and what the weather is like outside. Making sure that this information is also upfront and visible to the manager helps them make a good decision, rather than assuming that the computer is correct and following the recommendation blindly.
#### **5\. Will people accept the results from computer vision?**
The last question that we pose is not technical at all - it’s about the people and culture in the environment where the computer vision system might be implemented. You can spend a lot of money developing a complicated technical solution, but at the end of the day the end users have to be willing to use it. This is especially important in situations where computer vision is used as an analytical tool rather than for automated decision-making, and a human has to interpret the results and then decide on a course of action.
For example, IBM has been deploying their Watson artificial intelligence engine to medical applications for the last decade. They have produced some really impressive results, including achieving >90% accuracy at diagnosing certain types of cancers from radiology images. But at some hospitals where pilot trials are being conducted, the doctors [ignored](https://which-50.com/cover-story-watson-cancer-story-ibm-doesnt-talk/) the AI system’s recommendations because if they disagreed with the AI, they preferred to trust their own judgement, even though the human doctors had [lower accuracy rates](https://www.wired.co.uk/article/ibm-watson-medical-doctor) than Watson. To make matters worse, patients consistently show a [reluctance to trust](https://hbr.org/2019/10/ai-can-outperform-doctors-so-why-dont-patients-trust-it) the results provided from medical AI systems. As a result, some hospitals that were trialling the software have now decided not to use it, even though IBM would argue that it would lead to better clinical outcomes.
Managing expectations, and aligning those expectations as closely as possible to reality, is critical for encouraging adoption. While these algorithms are often hyped up, they are very rarely 100% accurate and the likelihood of there being some error is relatively high. People need to be prepared for this and know how to react appropriately. If people are likely to be replaced by automation, then they need to be given the knowledge and tools to prepare for that, or you might find that they will [sabotage the system instead](https://www.telegraph.co.uk/technology/2019/09/29/british-workers-deliberately-sabotaging-robots-amid-fears-will/). It’s generally helpful to explain that these AI tools can help people do their jobs more efficiently, augmenting their existing roles.
In this article, we’ve covered a couple of screening questions that you can ask when considering whether a computer vision system might be appropriate for the problem you are trying to solve. They include technical considerations about whether computer vision is capable of solving the problem, requirements engineering considerations about the needed level of accuracy, and broader people and culture considerations about how the system will actually be used by people. These are not the only questions, and there are many more to be considered like computation speed requirements, scalability considerations, and budget limitations, but these five are a good place to start. In the next upcoming article, we’ll talk about some of the computer vision technology platforms that enable developers to build these solutions.
> Think Computer Vision could be used to solve your technical or business problems?
> [Contact us](https://www.spark64.com/contact) to see how we can help you out!
---
## Which chatbot platform should you use?
URL: https://www.elementx.ai/blog/which-chatbot-platform-should-you-use
Published: 15 June 2020
Updated: 14 October 2022
**Unsure where to start with chatbots? We show you tools that can help you on your way.**
So you’ve decided that you want a chatbot - now what? Search online and you’ll find a huge number of different chatbot platforms and systems, and it can get a bit overwhelming. To make things worse, very few of these chatbots are actually comprehensive packages - most of them only fulfil one part of what is needed for a chatbot.
What do we mean by that? Well a chatbot can be divided into three key parts:
- The **language engine** that communicates with the customer
- The **knowledge engine** that stores all of the information that a customer might want
- The **interface** with the customer
In many cases, chatbot “providers” assume that you will have some technical staff who can integrate these three components together and build something for your business. Let’s understand the role of each component in a bit more detail.
The **language engine** is often called the Natural Language Processing (NLP) engine, which interprets the sentences being typed in by the customer. The job of this engine is to take the sentence, extract the different parts of speech, and figure out what the key terms of the query are. In most cases, this is where the AI part of the system is - researchers have trained machine learning systems on millions of queries and sentences and taught computers how to interpret human language.
One of the most popular NLP engines is [DialogFlow](https://dialogflow.com/), which is made by Google. Not only does it understand key terms and extract them out, it can also help keep track of the conversation between the customer and the computer, allowing it to understand context from previous parts of the conversation. An important point to also consider is that DialogFlow supports more than 20 different languages - not everyone speaks English, so if you are trying to reach customers around the world then it is crucial to have a language engine with multiple languages and automatic translation. For an open source option, consider [Rasa](https://rasa.com/) - it has an active developer community and has been used by large companies all around the world.
The **knowledge engine** is an element that is often hidden from non-technical users, so you might hear different names for it. Essentially, it is the database of information that the chatbot draws from when it answers queries from customers. For example, if you had a travel agent chatbot, then the knowledge engine would contain all of the flights and their prices. More commonly, companies write pre-prepared responses for the chatbot, so that the responses are well crafted and hit the right messaging. Importantly, the knowledge, whether it is structured (i.e. pre-written responses) or unstructured (i.e. a loose collection of information), needs to be tagged with the right key terms. This is so that when the language engine identifies a set of key terms from a customer query, the knowledge engine can retrieve the right piece of information.
Since this is often just a database, there are relatively few chatbot-specific knowledge engines. However, recently there has been a shift towards creating less technical interfaces that can help people enter knowledge into the system and train the chatbot to find the right piece of information at the right time. For example, [QnA Maker](https://www.qnamaker.ai/) from Microsoft lets users put company information in existing formats like product manuals or FAQs, and then processes it automatically to convert it into a format that is usable by chatbots.
Lastly, perhaps the most important part is the **interface** - how the user ultimately interacts with the chatbot. This may be a surprisingly complicated decision - you can have a chatbot interface on your website, but that assumes that your customers are on your website. Increasingly, chatbots are being made for social media and communication systems, like Slack, Facebook Messenger, Skype, WeChat, and more. There isn’t any clever logic happening in this part of the system usually - it is just a place for customers to talk to the chatbot. Almost all chatbots are still text-based, but in the future we will likely see more chatbots process audio and even video using speech recognition and computer vision to maintain conversations with customers. This is where the rise of digital agents like [Soul Machines](https://www.soulmachines.com/) and [FaceMe](https://www.faceme.com/) will enable new ways for companies to form relationships with their customers, although we are still a while away from mass adoption of these technologies.
While it may seem logical to put the chatbot on your website because you have control over that interface, it’s important to consider where your customers are and what platforms they already use. Most of the main platforms offer Application Programming Interfaces (APIs), which allow your language and knowledge engines to programmatically communicate with their systems to receive and send messages. One of the most common platforms for chatbots is [Facebook Messenger](https://developers.facebook.com/docs/messenger-platform/) - they have a huge reach in many countries, and have made it easy to build interactive and engaging experiences for marketing to potential customers, beyond one-way push advertising. Facebook was an early adopter of chatbot technology, and so many customers are used to interacting with chatbots over Messenger. But at the same time, lots of people (particularly younger audiences) are moving away from Facebook, so you might need to consider other platforms to reach them. It is critical that your chatbot presence is **where your customers are**.
Okay, so now you know that there are three key parts to a chatbot - but there are still a huge number of options out there, so how do you decide if something is good and trustworthy or not? Ultimately, it’s helpful to have a team of experts who have built many chatbots in the past on board to utilise their experience and expertise to make good choices. This is where an AI development company like ElementX comes in - our AI engineers and AI architects have built chatbots and recommendation engines for the insurance and retail industries, and have practical experience in what works and what doesn’t. Additionally, the team have partnered with Google for the latest chatbot technologies, which helps ensure that any system they build is reliable and scalable.
> Want to incorporate a chatbot onto your company's website?
> [Contact us](https://www.spark64.com/contact) and we'll get you sorted.
---
## TechWeek 2020: The State of Artificial Intelligence
URL: https://www.elementx.ai/blog/techweek-2020-the-state-of-artificial-intelligence
Published: 15 May 2020
Updated: 14 October 2022
**ElementX held our very first TechWeek 2020 webinar, all about what is happening in the world of Artificial Intelligence and how businesses can best leverage all that it has to offer to stay ahead of the curve.**
---
## COVID, AI, and Marketing
URL: https://www.elementx.ai/blog/covid-ai-and-marketing
Published: 15 April 2020
Updated: 15 September 2022
**The business world is constantly evolving - now more than ever due to Covid-19. Want to know what your organisation's marketing team can do to keep up with the help of AI? Here's how.**
As the [outbreak of COVID-19](https://www.spark64.com/post/ai-coronavirus-outbreak) rages on and the world races to find vaccines, we’re faced with increasing economic challenges and the need to do more with less.
Whether you’re grappling with smaller teams, a smaller budget or greater demands just to stay in the game - now is the time to step back and rethink how you connect with the market and get the data driven insights needed to navigate the turbulent year ahead.
#### **Why AI matters**
To understand the importance of AI, we need to understand what it is. Given the push by the big players to highlight their big data prowess a few years ago - you could be forgiven for thinking there is a fully conscious, neural network lurking in each of their clouds. The reality is (thankfully) simpler - but you might still need some help from the experts.
Speech and visual recognition technologies combined with Natural Language Processing and Machine Learning allow us to find patterns in data that might take years for even the smartest humans to process, understand and act on. We can identify objects in an image, the tone of someone’s conversation or connection points between seemingly unrelated concepts quickly and accurately.
We see this in everything from self-driving cars to medical research tools. However, the ability to reason and understand outcomes is where we come in - we’re still in the driver’s seat!
### **Putting it into practice**
##### So what does this mean for marketing? Let’s think back the challenges we identified earlier around doing more with less.
#### Campaign Optimisation
Managing campaigns is time consuming, and involves a lot of repetitive work to optimise and identify patterns that improve ROI. It’s a perfect opportunity for AI to help lessen the load and free up your time for more strategic work.
At its most simplistic, Google Ads will allow you to create responsive search and display ads that test different asset combinations to improve click through rates. But that’s just the start - with predictive analytics forecasting weather, people’s movements and changing interests, the scope quickly expands to automatic management of campaigns.
By taking into account hundreds of continually changing pieces of data, we can understand where the buyer is in the buyer journey and what to best present them with at a given point in time - [even at the mall with interactive signage](https://www.youtube.com/watch?v=Kj7Dm_i-OoM&ab_channel=PosterscopeWorldwide). A great example of leveraging AI is IBM’s Watson Advertising, which has introduced [COVID-19 triggers](https://www.ibm.com/watson-advertising/thought-leadership/covid-19-triggers) that can be leveraged to adjust campaigns in light of the impact of the pandemic.
[https://youtu.be/gCR9dJ5Kg_w](https://youtu.be/gCR9dJ5Kg_w)
####
Conversations at Scale
The next challenge is how to best engage online visitors in conversation. It doesn’t make sense to allocate your teams’ valuable time equally to every visitor - but what if you could use AI to make sales at lower values that might not have been viable where human intervention was needed, and still ensure that your higher value buyers receive the personalised attention needed to get them across the line?
This is where AI driven conversational bots are hugely valuable. With simple Natural Language Processing, they can respond to questions and connect visitors with the right people as needed. Some great examples include Lemonade, an AI driven [chatbot for processing insurance claims](https://www.lemonade.com), and [Cove’s insurance chatbot that allows people to buy and claim](https://www.spark64.com/project/coveinsurance) on Facebook by talking to their first digital employee. In an industry that has been slower to embrace technology, a bot-based approach is not just a cost saving mechanism but also provides unique value to digitally savvy buyers.
You may in some cases want to make yourself less accessible to those browsing your site that don’t fit your target personas and segments, but for those that are your ideal buyers it's time to get creative. Why not [have a coffee delivered to them automatically using a service like DoorDash](https://www.youtube.com/watch?v=cZTCmx6N7Xc&ab_channel=CNET). A clever idea during a pandemic when in person meetings might not be viable!
#### Better Lead Qualification
Finally, with your marketing campaigns delivering, your sales team prospecting and your chat bots adding to your contact database - the question is how to sort and qualify which leads make most sense to allocate your precious time to.
Traditionally lead scoring systems have utilised a selection of predefined demographic and behavioural criteria to assign a positive or negative score depending on how closely aligned a visitor is with your buyer personas. But this requires a lot of manual work, and needs to be constantly kept up-to-date. This might be okay for companies with limited activity and smaller budgets. However, for those with dynamically changing websites, large campaigns running and an active sales team, AI has a role to play.
Recently HubSpot has refreshed its automated lead scoring model, with a more advanced AI that determines the [likelihood of a contact to close with predictive lead scoring and assigns a constantly updating priority to ensure](https://www.hubspot.com/product-updates/predictive-lead-scoring-changes) that the right contacts are surfaced to sales at the right time. This is driven from analytics information, enriched data from HubSpot Insights, details specific to your business and interactions logged in the CRM.
In terms of your CRM, AI can also help with [identifying duplicate contacts](https://www.hubspot.com/product-updates/clean-up-your-database-with-ai-powered-duplicate-management) and [identifying data like phone numbers and addresses from emails](https://medium.com/snapaddy-tech-blog/machine-learning-for-signature-detection-7c62d838f520) to enrich your contact data on the fly. From tedious repetitive tasks, to complex analysis - artificial intelligence can help to increase the velocity of your pipeline.
### **Where to next?**
Many of these ideas may be new to you, or some might already be under consideration. The first thing to do is reflect on the skills within your team. You need a good mix of left and right brain thinkers - whether internally, or supplemented with the expertise of an agency.
The most important thing is to make a start. Pick one small thing, test it and then iterate. AI is new and everyone is learning about what works best for their business and their objectives.
As long as you keep your buyers in mind and are conscious of making sure the experience is authentic, helpful and not “creepy” you can’t really go wrong.
##### So what will you be talking about at next week’s standup?
#####
> Still curious about how your organisation could implement AI?
> [Contact us](https://www.spark64.com/contact) to find out how our team can help you navigate the new normal!
#####
---
## What are chatbots? How do we use them?
URL: https://www.elementx.ai/blog/what-are-chatbots
Published: 15 April 2020
Updated: 14 October 2022
**New to the world of chatbots? We talk about what makes chatbots work and how you might want to use them yourself.**
Computers have drastically changed the world over the last couple of decades, and they have changed how we humans communicate and interact with each other. As the powers of Artificial Intelligence and Machine Learning continue to improve, we have helped computers communicate with humans as well. Whereas once upon a time you needed to be a computer programmer to understand the weird languages and error codes of computing, we are now getting close to the point where computers can speak to us in everyday languages like English. When computers start to talk like people, it’s no surprise that businesses will find ways to use chatbots (or virtual assistants) to provide a new interface for customers to interact with the company.
The role of the customer service agent in many cases is one of interpreting questions from customers, finding the right information within the business or pushing the right buttons, and then responding to customers. For example, you might call up your bank to open a new transaction account. The customer service representative at the other end of the call is listening to what you’re saying, sustaining a conversation with you and asking you questions, filling out a form on their computer, and then informing you of the outcome. When the interaction is broken down like this, it becomes easier to see how we might use a computer to achieve many of the same goals - after all, the human operator is often just an interface for the computer because they are just entering information into the computer based on what the customer says. The computer already has the body of knowledge related to the company’s policies, shipping details, customer relationship management, marketing, and so on. So what’s stopping computers from taking over the world?
The tricky part of the problem has always been language. Computers natively speak in binary - sequences of 0s and 1s. The translation of English sentences into 0s and 1s is not so simple - in fact, anybody who has learnt English as a second language can tell you that the whole language is not so simple. It’s full of rules and exceptions to those rules, and then people add on metaphors and idioms, and that’s before we add colloquialisms into the mix. Natural Language Processing (NLP) is the field of computer science that focuses on interpreting language using computers. The two main problems are Natural Language Understanding (NLU) and Natural Language Generation (NLG) - in other words, being able to listen or read, and then being able to speak. There are a bunch of sub-fields related to NLP that you may have heard of: text processing, sentiment analysis, language translation, conversational intelligence, text filtering, information retrieval, and response generation. These all rely on computers being able to understand words and sentences from humans, even if we aren’t using the Queen’s English. Engineers have been working on these problems for decades.
So chatbots are all of this implemented in the real world by software developers. In a general sense, they aim to provide an easy-to-use, natural language interface for humans to communicate with computers. In a more specific sense, chatbots allow customers to interact with a company and its knowledge base via its computers. Now, let’s take a look at some of the use cases and applications of state-of-the-art chatbot technology.
The most common application of the chatbot is customer service portals. Many companies and websites already have question banks or FAQs, but the customer still has to spend a long time scrolling through many questions, or cleverly using the right key terms in a search bar to bring up the right questions. With the power of NLP and a chatbot, customers can strike up a conversation instead, and ask questions in a natural way that don’t necessarily have to match the question/answer pair as written in the question bank. For example, the question in the database might be “How do I book a flight?” but the user might type “I need to get a flight ticket” - the NLP engine needs to be able to resolve these as being the same question. The chatbot can try to infer intention in the question, remember previous parts of the conversation to provide more context, and detect when the customer is getting agitated and a human needs to take over. There are advantages for both the company and the customer here - the company can have fewer human support agents, and customers get 24/7 support and don’t have to wait in queues. All this results in a better customer experience and ultimately increases the net promoter score for the company.
Amtrak, the North American train and railroad operator, has a great example of successfully using chatbots to augment their customer service operations. Julie, their virtual assistant, understands queries from users such as requests for train schedules or rules around luggage, and points them in the right direction. The chatbot doesn’t need a sophisticated or fancy design, just a way for messages to get to and from Julie. [Within a year of putting Julie on their website](https://overthinkgroup.com/chatbot-case-studies/) in 2012, Amtrak **answered over 5 million questions, saved over $1 million in customer service expenses**, and generated more revenue by making the sales process simpler for customers and adding on upsells to the interactions. Something that’s important is that the limitations of Julie are well understood, and the system redirects conversations to human support agents when the questions are too difficult to understand.
Chatbots can also be used as a sales funnel to help companies understand what products specific customers need, and provide information to help them make the purchasing decision. For example, we at ElementX [developed an insurance chatbot for Cove Insurance](https://www.spark64.com/project/coveinsurance) that integrates into Facebook Messenger, which interacts with the customer to ask them the questions that a salesperson or insurance agent would normally ask. Based on these interactions, the chatbot can produce quotes for different insurance policies, or help submit a claim with the customer directly with photos on their phone. The key point here is that the chatbot isn’t just answering questions - it’s also collecting the necessary information from users in an automated way.
Chatbots are also great in situations that don’t have any sales involved. Some interesting chatbot applications include helping users [find recipes for dinner](https://www.foodnetwork.com/site/apps/chatbot), [learning new languages](https://www.theguardian.com/technology/2016/oct/06/duolingo-chatbots-learning-language), or [planning travel itineraries](https://www.hipmunk.com/) and [finding directions](https://www.theverge.com/2018/11/14/18095863/google-maps-business-chat-rcs-messaging-feelings). A particularly cool one is [National Geographic’s chatbot](https://www.facebook.com/NatGeoGenius/) that was used to promote their TV show Genius, which allowed users to interact with Albert Einstein or Pablo Picasso. This demonstrated how chatbots can be given some personality in the way that they respond to humans, and they don’t have to be stilted and robotic. At the same time, it helped [increase customer engagement](https://socialmediaweek.org/lagos/events/case-study-national-geographics-albert-einstein-chatbot/) between users and the brand, with the average conversation lasting 6-8 minutes!
It’s important to note that using a chatbot isn’t necessarily about getting rid of humans and pursuing automation at all costs - in many cases, chatbots help increase engagement and sales by adding another channel for customers to interact with the company. It can help with customers who are nervous about talking to people, or customers who have embarrassing questions. It can support a more natural form of communication for a new generation of digital natives who prefer to text or write messages rather than call people on the phone. It can also free up staff time from routine queries and allow them to focus on delivering better results for complex enquiries, and it can help protect staff from particularly aggressive customers. Chatbots are also helpful for maintaining a global presence, and allowing customers to interact with your company at any time, even when most of your workforce is asleep. But ultimately, chatbot technology should be targeted towards augmenting human effort, not replacing it.
In the [next article](https://www.spark64.com/post/human-or-chatbot), we explain a little bit about how to tell when you’re talking to a genuinely automated chatbot or a human, and what tricks are used to cheat a little bit to ensure that customers get good experiences.
> Want to incorporate a chatbot onto your company's platform?
> Or just want to find out a little more?
> [Contact us](https://www.spark64.com/contact) and we'll get you sorted.
---
## How AI is Helping Tackle the Coronavirus Outbreak
URL: https://www.elementx.ai/blog/how-ai-is-helping-tackle-the-coronavirus-outbreak
Published: 25 February 2020
Updated: 15 September 2022
**The advent of Artificial Intelligence and Machine Learning has revolutionised the way we are able to track and combat medical epidemics, like Coronavirus (Covid-19). Let's take a look what's happening in the world today.**
As governments attempt to mitigate the spread of the 2019 Coronavirus (Covid-19) by limiting travel and quarantining people, health authorities are following a well-known playbook. We’ve seen global outbreaks recently with the SARS outbreak in 2003 or H1N1 (swine flu) in 2009, which led to very different outcomes - SARS was almost entirely eliminated within a year due to an effective and coordinated program of isolating patients, while H1N1 settled and became a seasonal strain controlled by vaccinations with occasional ongoing flare-ups. But this time, the global effort has been emboldened by a new tool - Artificial Intelligence and Machine Learning are much more common now, and the advancements in these technologies are enabling better, faster responses.
For instance, take the development of vaccines, treatments and cures. Traditionally, this is a long and difficult experimental process that involves isolating samples of the virus and using trial and error with thousands of potential antivirals to see if they are effective. During the SARS outbreak, it took roughly 20 months for a vaccine to be developed and ready for clinical trials, by which point the outbreak was already over. This time, AI techniques are being used [to find patterns](https://fortune.com/2020/01/24/coronavirus-vaccine-nih/) between Covid-19 and other viruses that we already know about, so that the scope of any search for a cure can be reduced. This is dependent on understanding the virus and its genetic makeup, and we can now analyse that much faster as well. Baidu has reported that their AI tool has [cut the processing time](https://thenextweb.com/neural/2020/02/03/alibaba-baidu-google-ai-cornavrius-tools/) for analysing part of the coronavirus RNA from 55 minutes down to 27 seconds. Continuous sequencing is needed to help detect mutations of the original virus, so this isn’t a one-time task and time is critical. The most promising solution for developing a vaccine uses that genetic sequencing information to design a direct solution computationally, which also reduces the risks and logistical costs of obtaining and testing on actual virus samples. A couple of different labs say that they will have a vaccine ready for clinical trials [within one to four months](https://www.healthline.com/health-news/how-long-will-it-take-to-develop-vaccine-for-coronavirus).
However, the challenge with vaccines is that trials, manufacturing, and deployment can still be slow. There are a few groups [working on developing](https://venturebeat.com/2019/08/14/verisim-life-uses-ai-powered-biosimulations-to-replace-animal-drug-testing/) [virtual clinical trials](https://www.nature.com/articles/d41586-019-02871-3), where new medications and treatments can be tested in AI-powered simulations, but we are still a while away from having sufficiently accurate and reliable models of human bodies. Manufacturing of any vaccine will likely be limited to the hundreds of thousands or millions of doses per month, and a lot of coordination is required to make sure those doses get to the most needy areas and are deployed effectively. There are AI tools being developed that aim to help [by optimising vaccination strategies](https://bidinitiative.org/blog/artificial-intelligence-for-good-using-machine-learning-to-close-the-immunization-gap/) to mitigate spread as much as possible, but this is still a long-term solution.
For now, the strategy has to focus on containing the virus as much as possible. With modern transportation moving people quickly, diseases can jump large distances and appear in unexpected places. By picking up cases early, authorities can act fast to confirm and contain, and if done effectively this can suppress the disease entirely. However, containment is disruptive and [can have negative long-term impacts](https://actu.epfl.ch/news/epidemics-the-end-of-containment-measures-3/), so decision-makers need to be careful about where and when they decide to quarantine people. This is informed by surveillance efforts, with health workers on the ground reporting cases as they happen. AI tools are now augmenting this by [monitoring news and social media](https://www.wired.com/story/how-ai-tracking-coronavirus-outbreak/), which can help capture cases outside of formal health systems and corroborate data collected from other sources. Artificial Intelligence may also be able to [help model and predict how diseases spread](https://www.contagionlive.com/news/ai-could-present-a-new-paradigm-in-epidemiology), although this research needs a lot more data and it will be hard to know how reliable these tools might be. It’s important to note that AI isn’t doing this alone - [human analysts are still interpreting the results](https://www.vox.com/recode/2020/1/28/21110902/artificial-intelligence-ai-coronavirus-wuhan) of any algorithms and making the judgement calls, and balancing data collection and aggregation against privacy concerns.
Text mining of social media using natural language processing can also help decision makers monitor the morale of people in quarantined areas. Importantly, misinformation about the disease can also spread very quickly and lead to more chaos, so AI tools are being used alongside human moderation by [Facebook to remove harmful content](https://www.theverge.com/2020/1/31/21116500/facebook-instagram-coronavirus-misinformation-false-cures-prevention). Mapping tools built on top of AI analysis can also be shared publicly to help [keep people informed about the spread of the disease](https://www.zdnet.com/article/how-to-track-the-coronavirus-dashboard-delivers-real-time-view-of-the-deadly-virus/), although this should be accompanied with some analysis and guidance to help people understand the risks and manage expectations.
Lastly, there are a couple of AI-enabled tools being used on the ground by frontline staff. Computer vision techniques can be used to [process thermal imaging data](https://anyconnect.com/blog/smart-thermal-cameras-wuhan-coronavirus) to help detect individuals with virus symptoms, and the use of drones can help survey large numbers of people at once. Physicians can also use AI-enabled diagnostic tools to help differentiate Covid-19 from other types of flu or disease based on the symptoms and test results, although currently this is no replacement for lab-based testing of samples. The hope is that [lab-on-a-chip diagnostic tools](https://www.mobihealthnews.com/news/asia-pacific/veredus-laboratories-announces-development-detection-kit-wuhan-coronavirus) will be able to provide fast, remote results so that samples don’t have to be sent off site and healthcare workers can respond immediately.
None of this is to say that AI technology has provided a silver bullet that stops disease outbreaks and epidemics. The processes involved in controlling the spread of disease are still very human, but AI tools can help provide those humans with timely information for decision making. The spread of disease has significant negative impacts on people’s lives, so it is important that we can leverage all the tools we have available to mitigate the harm. In the future, we may see fewer and fewer outbreaks occurring as epidemic control protocols evolve to take advantage of new technology.
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## Why is Computer Vision Hard?
URL: https://www.elementx.ai/blog/why-is-computer-vision-hard
Published: 15 February 2020
Updated: 14 October 2022
**Computer Vision has come a long way, but why is it so challenging? We dive into this question to see how far Computer Vision has come and what factors make it so tricky to complete. **
In the [previous article](http://www.spark64.com/post/what-is-computer-vision), we established that digital images are based on numerical data. Computers are supposed to be good at processing numbers and doing math, so why is computer vision such a challenging problem that still faces low accuracy rates in many applications? It turns out there is some truth to the saying “a picture is worth a thousand words” - images have a lot of information density, because it’s not just about the underlying numbers but also the patterns that those numbers represent. The more numbers you have, the more patterns there could be, and the more possible meanings those patterns could have. There are a lot of pieces of information that we can extract out of an image, depending on the questions we might want to ask. Let’s explore this a bit more by referring to an example photo:
Your eyes perceive the image and send signals to your brain, which processes the patterns that it finds and refers to your memory and knowledge, allowing you to understand what is happening in this photo - all in less than a second! Now imagine that we have given this photo to a computer to process - what sorts of questions could we ask about this image?
We could ask **“how many people are in the photo?”**. This is already a complicated question, because we need to know what people look like. How would we describe the visual appearance of a person to a computer? We might say that people are somewhat rectangular and tend to be taller than they are wide, they tend to have two arms and two legs, they have patches of visible skin (although a lot is covered by clothing), and they have roughly oval faces made up of two eyes above a nose and a mouth. This is pretty broad, but we need a description of “people” that is _generalisable_, meaning that the definition is broad enough to cover all possible instances of people. The bodies of most of the people are partially obscured in the photo, and you can only see their faces - some faces are even partially blocked by other people as well, so our description of what people look like may have to be reduced to just a couple of facial features in order to detect everyone. This works until we realise that there’s even a person facing away from the camera - somehow our human brains know that this is a person, but how would we teach a computer to recognise that shape as being a person?
The description also contains a lot of keywords that need to be defined as well - for example, what defines a “nose”? These definitions have to be mathematical, looking for patterns across the numbers that make up the image. A lot of computer vision techniques look for edges, where the colour or brightness changes from light to dark or vice versa, indicating the boundaries of objects. The shape of those edges, combined with other measures of “texture” and “structure”, can give computers a pretty good idea of what objects they’re looking at. The above image shows some of the detected edges, and then those edges drawn on top of the original image in red. Just showing the edges loses a lot of detail for humans, but it reveals the underlying structure of the image and gives some indication of the shapes in the photo. It’s important to note that edges are just one of the features that computer vision algorithms look at. The image with the edges also shows something else important - the people in the back are a little blurry in the original photo, and the computer struggles to find the edges in the faces. While the human brain is pretty good at dealing with a little bit of blurriness, many algorithms do not perform as well. However, facial detection is one of the tasks in computer vision that is considered pretty close to solved these days - [the latest algorithms](https://www.sciencedirect.com/topics/computer-science/face-detection) using state-of-the-art deep learning can regularly achieve accuracy rates above 95% in uncontrolled environments.
Once we find all the people in the image, a natural follow-up might be **“who is in this photo?”**. Most of us would be able to take a quick glance at the photo and identify former US President Barack Obama, alongside his wife Michelle Obama. If you’re a political junkie, you might recognise former Speaker of the US House of Representatives John Boehner on the left, and there are a couple of other high-profile US politicians in the background. The human brain can be pretty quick at recognising people, commonly using facial features along with skin and hair. This is actually a skill that can be learned - looking at a lot of faces and matching them to names is something that can train your brain to perform better identification, literally rewiring the neurons and how they are connected inside your brain. The brain also reorganises memory to be efficient - people who you have seen recently or more frequently (like family members or celebrities) are easier and faster for your brain to retrieve and match, whereas other people (like old work colleagues or school friends from decades ago) sit in your long-term memory.
For a computer, identification requires that each person be reduced down to a numerical sequence that represents their appearance and structure. This could reflect the distances between the eyes and nose, or it could be more complex and capture characteristics like eye colour, nose shape, and cheekbone positioning. The computer then has to compare that sequence against a database of previously seen sequences (also known as templates), and try to find the closest match. Depending on the level of detail captured by that sequence, there could be a lot of potential for error - for example, if the sequence just represents a black hair colour and brown eyes, then there are plenty of people who would match that description. It also depends on who is in the database - if it is just a database of US politicians, then matching Barack Obama’s facial features might be relatively achievable, but if you have a database of every person in the US, then there is a higher likelihood that there is someone else who looks a little bit like Obama, and thus more of a chance for the algorithm to make a mistake. This is before we consider other aspects like ageing over time, the influence of lighting in the images, and the orientation of the faces. The facial recognition system being used by the UK police is reported to have [an 81% error rate](https://www.engadget.com/2019/07/04/uk-met-facial-recognition-failure-rate/) (although this depends on how you measure accuracy and error). Also, the bigger the database, the slower the matching process, because generally our target numerical sequence has to be checked against every sequence we already hold in the database. It’s a really long and difficult process, but it often seems simpler because computers are so good at math and can hide a lot of the complexity away.
Lastly, we might ask **“why is this photo important?”**. From the photo alone, this is almost impossible for a computer to figure out with current-day technology. A human looking at the photo would use context clues like Obama holding up his right hand, determining that the man facing away from the camera is wearing some form of robes (probably judge’s robes), that there are a lot of US politicians in the background, and so on. Many people (even those of us down here in New Zealand) would be able to quickly identify that this is from Obama’s inauguration or swearing-in ceremony, when he became the President of the United States. With more context clues like the approximate ages of the people in the background and the specific selection of the politicians in the background might help you identify that this is Obama’s first inauguration in 2009. If we are lucky, then the image might be captioned with some more details such as the specific date and the identities of other people (e.g. the person facing away from the camera is Chief Justice John Roberts), but this isn’t always available and if we really wanted to know more about the image and the context we would probably have to go look up some details. In theory, a computer should be able to do the same thing - it should be able to identify these context clues and gradually build a picture as it gets more and more detail, and search through the internet to find more information. But it’s still a really hard problem that requires knowledge, not just data. The human brain can connect the dots based on information adjacency - how contextually close pieces of information are to each other - but this is learnt over time and can be hard to teach a computer. It may take a genuinely intelligent Artificial Intelligence to be able to complete this task, not just narrow AIs that are good at one or two things.
By breaking down the visual tasks that our brains are completing, it exposes why computer vision is so challenging. A lot depends on the questions we are asking, but even the simple questions can be very difficult to answer mathematically and programmatically. The human brain is also extremely efficient, combining visual information with contextual knowledge in milliseconds, while computers might need seconds or minutes just to recognise a face. Computer vision has come a long way and achieved some amazing things over the last decade, but at the same time, computer vision still has a long way to go. Understanding human vision and how the brain processes the data from our eyes may give us the insights we need to develop better, smarter, and faster computer vision.
In the [next article](http://www.spark64.com/post/computer-vision-problem-solve), we will look at some questions that you can ask to understand if a particular problem can be solved with computer vision, and where the bounds of the current technology are.
> [_ElementX_](https://www.elementx.ai) _is an artificial intelligence agency on a mission to make AI more accessible. We specialise in language, vision and data to accelerate your business, streamline processes and uncover meaningful insights through data._
---
## Teaching Machines to Read
URL: https://www.elementx.ai/blog/teaching-machines-to-read
Published: 15 August 2019
Updated: 15 September 2022
**Is it possible to have a computer read an unfamiliar passage of text, comprehend, and answer questions from it? **
Is it possible to have a computer read an unfamiliar passage of text, comprehend and answer questions from it?
With the latest advancements in Natural Language Processing (NLP), this is one step closer to reality.
Machine reading comprehension, otherwise known as Question answering systems, are one of the most challenging tasks in the field of NLP. The goal of this task is to be able to answer an arbitrary question given an unfamiliar context. For instance, given the following excerpt from Wikipedia:
> New Zealand (Māori: Aotearoa) is a sovereign island country in the southwestern Pacific Ocean. It has a total land area of 268,000 square kilometres (103,500 sq mi), and a population of 4.9 million. New Zealand's capital city is Wellington, and its most populous city is Auckland.
We ask the question:
> How many people live in New Zealand?
We expect the machine to respond with something like this:
> 4.9 million
In 2017, there was a breakthrough in the NLP space with the introduction of the [Transformer model](https://towardsdatascience.com/transformers-141e32e69591). This took a different approach in the way sequences of data (e.g. words) were represented, which allowed relationships between words to be better captured. This model architecture then gave rise to [BERT](https://towardsdatascience.com/bert-explained-state-of-the-art-language-model-for-nlp-f8b21a9b6270), a technique developed by the Google AI Team whereby a transformer model is pre-trained with **massive** amounts of data (more than most people/companies can afford in resources) and later can be fine tuned for a specific task, making use of the word relationships captured during the initial training process. This technique achieved state of art results on 11 NLP tasks including sentiment analysis (positivity/negativity) and question answering.
Since the rise of BERT, other pre-trained models followed such as GPT-2 and XLNet, all of which became larger and larger in size. More recently, the Google AI team released [ALBERT](https://ai.googleblog.com/2019/12/albert-lite-bert-for-self-supervised.html) - which is a fraction of the size of BERT, yet performs even better on a popular [question answering benchmark](https://rajpurkar.github.io/SQuAD-explorer/).
> LITTLE ALBERT Demo - [Access Here](https://albert.spark64.com/)
To test out the question answering capabilities of ALBERT, I built a small demo to play around with. The transformers library developed by the amazing team at [Hugging Face](https://huggingface.co/) provides an incredibly clean and easy to use implementation of the transformer models.
Trying out a few passages and questions yielded some interesting results.
The first was a relatively straightforward test. I asked about the city with the most people, and it correctly answered it (note that the passage describes it as "most populous city", so is not word for word). Similar examples work just fine, such as "How many people does New Zealand have?"
It seems to also be able to handle relationships spread over two different sentences. It must have inferred it from the pronoun at the start of the second sentence.
Here is one example where it does struggle. In order to answer this question, you must be able to infer that the turtle got there first from the sentence "found the turtle there waiting for him". The question answer system is unable to handle this level of abstraction.
This one is interesting. Not sure how it picked this as the most unique feature of New Zealand, but it works very well as an answer.
##### Have a go at the Little Albert demo yourself: [**Access Here**](https://albert.spark64.com/)
##### Tutorial
If you want to try train this model yourself, I've prepared a notebook on Google Colaboratory to try out!
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## What is Computer Vision?
URL: https://www.elementx.ai/blog/what-is-computer-vision
Published: 15 March 2019
Updated: 14 October 2022
**With all the buzz about computer vision, what actually is it? We take you through the concept of computer vision, highlighting its current and potential uses.**
One of the hottest fields of technology is the rise of computer vision. Other people might call it image processing or machine vision - essentially, it is about giving computers the power of sight so that they can perceive and understand visual information. Our eyesight is something that many people take for granted, yet by some estimates, 80-85% of our sensory information comes from vision. Researchers have been trying to help computers perceive the world in the same way as humans and other animals for over half a century. Recent advances in computer vision have drawn inspiration from biological vision and cognition, leading to a clash between the fields of computer vision and artificial intelligence / machine learning. In the words of Stanford University Professor [Fei-Fei Li](https://www.wired.com/brandlab/2015/04/fei-fei-li-want-machines-think-need-teach-see/), “if we want machines to think, we need to teach them to see.”
Digital images are made up of pixels, which represent one data point with some brightness or colour information at a spatial position. If we have more pixels, then we have more detail about the image, which is represented by “resolution”. Having more detail means that we can make the image physically larger before we get blurry or pixelated effects. Having pixels means that we can have a numerical representation of the image, and then we can do math on those numbers! Once we figure out what math needs to be done, we can get computers to do a lot of calculations very quickly, far quicker than any human, and process all the data automatically.
There are two more related fields that tell us a little bit more about how computer vision is achieved - academic conferences showcasing the latest in computer vision technologies might use the terms “signal processing” or “pattern recognition”. You might normally think of a “signal” as a squiggly line that moves up and down over time, but there’s no requirement that the x-axis has to be time. A signal is made up of data representing one variable changing over any dimension - in the case of an image, we could measure the brightness changing as we move horizontally or vertically across an image. We can then apply all sorts of signal processing techniques like filters or compressors to our images, which means we don’t have to re-invent the wheel and can borrow from an existing body of knowledge. “Pattern recognition” gives us a hint that a lot of computer vision processing is about finding patterns in the signals. A pattern is just a defined sequence of data points - the pattern should be identifiable as being a particular pattern. For example, we might have an image of a shoe - the pixels that represent that shoe could be called a pattern. The important feature of the pattern is that if we have another photo of the shoe, then we want to find the same pattern so that we can say that the pixels correspond to the same object. This turns out to be quite hard, because the patterns are rarely exactly the same - there needs to be some room for variation and changes in lighting conditions, photo angles, lens differences, and so on. So for example, instead of looking for exact matches of pixel values, we might use signal processing to find the edges of an object, and then match the shapes instead.
While the physics of biological perception are relatively well understood, the cognition part is still somewhat of a mystery and there are a lot of unknowns in neuroscience. Instead of trying to copy how the brain works exactly, there has been an explosion of artificial intelligence and machine learning over the last decade or so. The good news is that these methods have delivered very impressive results, allowing for detection and recognition of objects in images with high levels of accuracy. The bad news is that these methods are often treated as “black boxes” because the level of complexity is so large that it is pretty much impossible for any human to understand what exactly is happening. These methods optimise or “learn” automatically, modifying an internal model in order to improve performance without human intervention. A lot of the researchers and developers who are designing these systems are doing it in a trial-and-error way, just trying to squeeze out better accuracy rates. After Microsoft won a top international computer vision challenge in 2015, one of their executives asked the research team why their system had 152 layers of computation instead of 151 or 153 - the lead engineer said “we don’t know, we tried them all and 152 had the best result”. For a lot of developers, the AI models are abstracted away even further with new tools and APIs that allow them to deploy an AI model with one function call, further hiding the complexity.
So what does this all mean? The point is that computer vision is a relatively complicated and difficult area of technology, even though it is just about finding patterns in numbers. But we have come a really long way with the technology, and it’s already in our everyday lives. For example, most cameras and smartphones have automatic face detection technology that helps the camera focus on faces rather than the background or other objects. Some cameras even have smile detection to make sure that photos are taken at the best time!
Optical character recognition (OCR) is another well-developed and integrated application of computer vision - essentially extracting printed text from images. They are very common for reading number plates on vehicles (using Automated Number Plate Recognition (ANPR)), converting receipts and other documents into searchable text, and digitising old books. Recognising handwriting is a bit trickier, but is being done with relatively high accuracy - most postal systems around the world are now automatically reading postcodes to help sort the mail.
You’ve probably also seen computer vision be used in sports and entertainment. Tracking players or objects like balls can be quite challenging for a computer, but it is now done with sufficient accuracy that computer vision models can be used to inform refereeing decisions. The Hawk-Eye system is set up in major stadiums around the world, and uses multiple cameras to help localise and track objects, and has a kinematics simulation to reconstruct events like a ball landing in or out of court.
These are just a few examples of how computer vision is already being successfully used in a diverse range of applications. Of course, there are a lot more - fashion and retail, interactive gaming, augmented reality, autonomous vehicles, person tracking and surveillance, medical imaging, and industrial robotics are just a couple more areas where there are active efforts in deploying computer vision techniques. The research is still ongoing as well, and every year we see more advanced and complicated algorithms appearing at international conferences.
If we can teach computers to see as well as humans, then we will unlock millions of applications and be able to use the technology in ways that simply cannot be predicted now. But achieving human-level vision is still awhile away - the [next article](http://www.spark64.com/post/why-is-computer-vision-hard), we'll explore why computer vision is so hard in a metacognitive way (thinking about thinking), and why biological vision is simply amazing.
> [_ElementX_](https://www.elementx.ai) _is an artificial intelligence agency on a mission to make AI more accessible. We specialise in language, vision and data to accelerate your business, streamline processes and uncover meaningful insights through data._
---
# Whitepapers
## What is RAG
URL: https://www.elementx.ai/whitepapers/retrieval-augmented-generation
Published: 19 January 2024
What Retrieval Augmented Generation is, how it works, structural and security considerations, pros and cons, and use cases across industries to help you identify the best, most profitable way to use RAG in your business.
> what is Retrieval Augmented Generation? This White Paper explores the definitions, functionality, pros and cons, and use cases you need to know in order to identify the best, most profitable way to use RAG in your business.
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## Tier 0 Customer Support: Best Practices in Leveraging AI for Self Service Support
URL: https://www.elementx.ai/whitepapers/tier-0-customer-support
Published: 13 May 2023
How AI-powered virtual assistants drive efficiency, increase customer satisfaction, and lower operational costs by helping customers help themselves with a Tier 0 support system.
> Discover how AI-powered virtual assistants can revolutionise your business's customer service experience, drive higher efficiency, increase customer satisfaction, and lower operational costs in this whitepaper, which explores the ways in which you can help customers help themselves with a Tier 0 Support system.
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## Which Conversational AI Platform Should I Choose?
URL: https://www.elementx.ai/whitepapers/conversational-ai-platforms
Published: 14 October 2022
Broad review and comparison of the major conversational AI platforms (Amazon Lex, Google Dialogflow, IBM Watson Assistant, Microsoft Bot Framework), with strengths, weaknesses, pricing structures, and key considerations for each.
> "Which conversational AI platform should I choose?"
This is something that we are asked often with those looking into building a chatbot, digital human or conversational AI experience. Sadly, there isn't a one-size fits all solution.
This white paper seeks to provide a **broad review and comparison of the major conversational AI platforms.**
It will highlight the strengths, weaknesses, pricing structures and key considerations of each platform.
This includes:
- Amazon Lex
- Google Dialogflow ES and Google Dialogflow CX
- IBM Watson Assistant
- Microsoft Bot Framework / LUIS / Composer
Before you dig into this white paper, check out [this blog](/blog/how-do-i-choose-a-conversational-ai-platform) about how to choose a conversational AI platform.
With our guidance, you can pioneer within your organisation, navigate the various conversational AI platforms available in the market and select the one that best suits your organisations needs and requirements.
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# Team
## Carol Cheng
URL: https://www.elementx.ai/team/carol-cheng
Title: CFO
Carol Cheng is a Chartered Accountant with over 25 years' experience in finance, governance, and risk across New Zealand, Hong Kong, and Mainland China. As founder of Hong Consulting Ltd, she delivers tailored virtual CFO services, helping businesses strengthen their financial foundations, improve performance, and stay on top of compliance. Her expertise spans financial reporting, mergers and acquisitions, governance, tax compliance, and risk management, underpinned by senior leadership roles including Executive Director at PwC New Zealand and Partner at Grant Thornton China. Passionate about sharing her professional experience, Carol helps businesses grow with confidence. Carol also chairs finance and risk committees for public-sector organisations, and is a Chartered Member of the Institute of Directors.
#### What do you find most rewarding about your work at ElementX?
‘I enjoy working with such a talented, energetic team. Bringing financial clarity and effective processes means they can focus on innovating, knowing the numbers have their back.’
#### If you could be any animal, what would you be and why?
‘I would be a ginger cat, curious, independent and playful, always exploring new corners and seeing the world from different angles. They’re friendly, adaptable, and love bringing a bit of joy to those around them.’
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## Carter Apas
URL: https://www.elementx.ai/team/carter-apas
Title: Senior Full Stack Engineer
As a developer working on software applications that utilise AI, Carter plays a key role in tasks such as working with databases, building and maintaining API services, and collaborating with our team of ML engineers.
#### What do you find most rewarding about your work at ElementX?
‘I like being able to work with a range of the most up to date full stack technologies.’
#### If you could be any animal, what would you be and why?
Carter: ‘Dog’
Erica: ‘Okay, but what kind of dog?
George: ‘Chihuahua I reckon.’
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## Daniel Jimenez
URL: https://www.elementx.ai/team/daniel-jimenez
Title: Engineering Operations Lead
With years of experience in software development, Daniel now assumes a crucial role of overseeing the development, implementation, and maintenance of AI systems. He leads the Development team and is responsible for ensuring alignment of AI initiatives with business goals by closely collaborating with other departments and clients.
#### What do you find most rewarding about your work at ElementX?
‘I find it rewarding to work on innovative projects that push the boundaries of technology and explore new frontiers in the field of AI. I also enjoy collaborating with my colleagues who are experts in their respective areas.’
#### If you could be any animal, what would you be and why?
‘I am a fox.’
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## Daniel Xu
URL: https://www.elementx.ai/team/daniel-xu
Title: CEO & Co-founder
As the CEO and co-founder of ElementX, Daniel has leveraged his PhD in Bioengineering and First Class Hons degree in Mechatronics Engineering to spearhead groundbreaking innovations in the field. With multiple licensed patents to his name and recognition on global media platforms such as the BBC, Dan’s visionary leadership has brought about the creation of the UVLens product, which has helped over 1 million people worldwide better manage their UV exposure and prevent skin cancer.
#### What do you find most rewarding about your work at ElementX?
‘When I meet our customers and hear about how much they appreciate our work and how we've helped solve their problems. Second would be seeing our team pick up new technology and coming up with some really clever use cases.’
#### If you could be any animal, what would you be and why?
‘Silver back gorilla, because they're smart and one of the few animals that know how to use tools to help them with tasks.’
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## David Kelly
URL: https://www.elementx.ai/team/david-kelly
Title: Independent Director
David lends his expertise and strong background in the New Zealand tech ecosystem to ElementX as an invaluable member of our board. He co-founded Zeald Group, a leading eCommerce and digital transformation agency in New Zealand, helping over 15,000 businesses transition to digital and driving over $3 billion in online revenue. David brings a strong background in both technical and commercial strategy, advising companies across various sectors, including tech, agriculture, and marine, as well as supporting impactful initiatives like GEM (Get Ecommerce Movement), which has helped businesses in New Zealand and beyond embrace eCommerce.
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## Emma Roberts
URL: https://www.elementx.ai/team/emma-roberts
Title: Operations Manager
With a strong background in both technology and recruitment, Emma brings a unique and valuable perspective to her role as Operations Manager. She manages and streamlines operations, drives efficiency and leads improvements across our systems and processes. Emma builds meaningful connections and team collaboration both internally and externally. Her leadership, insight and energy enable us to work smarter, operate at our best every day and scale with confidence.
#### What do you find most rewarding about your work at ElementX?
I love working with such a smart and passionate team leading the way in AI and technology. The dynamic nature of my role keeps me engaged, constantly learning and excited to tackle new challenges.
#### If you could be any animal, what would you be and why?
'ChatGPT says I'm an octopus which I quite like - adaptive planner, creative thinker, problem solver, expressive and persuasive communicator.’
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## Garab Dorji
URL: https://www.elementx.ai/team/garab-dorji
Title: Full Stack Engineer
Garab is passionate about all things AI and technology. He began his journey in engineering as a Mechatronics Engineer and engaged in a wide range of multidisciplinary projects in ML, robotics, automation, and immersive technologies. He is now a Full Stack Engineer at ElementX to focus on AI and software engineering.
#### What do you find most rewarding about your work at ElementX?
‘It is incredibly rewarding to collaborate with such a smart, passionate team of experts to push the boundaries of AI. Working with the latest full-stack technologies across a diverse range of industries keeps me constantly learning, engaged, and excited to tackle innovative new challenges.’
#### If you could be any animal, what would you be and why?
'If I could be any animal, I would be a grizzly bear because they are big and strong but surprisingly adorable.'
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## George Qiao
URL: https://www.elementx.ai/team/george-qiao
Title: Senior Full Stack Engineer
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## Jay Oh
URL: https://www.elementx.ai/team/jay-oh
Title: Junior Full Stack Engineer
Jay is a full-stack software engineer with a strong interest in AI-enabled products and workflows. He studied Software Engineering at the University of Auckland and enjoys building practical solutions end-to-end, from shaping requirements to shipping and supporting features in production. He's naturally curious and loves introducing automation to remove repetitive work, whether through tooling, clean system design, or AI. He works best in collaborative teams, values clear communication, and enjoys active discussions that lead to better outcomes.
#### If you could be any animal, what would you be and why?
‘A sparrow, for sure. I'd be able to fly through the sky using my own abilities and truly feel the air around me. I'd also be small enough to fit into tiny places, just like how my little Mazda Demio can squeeze into almost any parking space. And lastly, sparrows are unbelievably adorable. Since so many people find them cute, getting food would probably never be a problem. I'd just have to hop over to someone and look charming.’
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## Kat Klebenow
URL: https://www.elementx.ai/team/kat-klebenow
Title: Head of Marketing
A curious and versatile marketer with a background in visual communications and a passion for data-driven storytelling, Kat spearheads marketing efforts; including strategy, market entry, sales enablement, lead generation, and showcasing the team's innovative achievements.
#### What do you find most rewarding about your work at ElementX?
‘My favourite thing about working with ElementX is being able to share the amazing projects the team works on! Projects are so varied and we work with awesome technology, and I love learning about all the different applications of AI.’
#### If you could be any animal, what would you be and why?
‘If I were an animal, I'd be an owl. Owls are observant, curious, and they spend a lot of time sitting quietly watching for prey, and I spend a lot of time sitting quietly working on projects 🙂’
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## Kevin Adams
URL: https://www.elementx.ai/team/kevin-adams
Title: Conversational AI Designer
Kevin Adams is a conversation design consultant who has specialized in digital humans and generative AI chatbots since 2016. His expertise in conversational design, natural language processing, and the evolving landscape of conversational AI solutions has earned him a reputation as a leading consultant in the industry. He has worked with clients in healthcare, finance, education, retail, and other sectors to help deliver amazing and innovative virtual assistant solutions.
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## Ming Cheuk
URL: https://www.elementx.ai/team/ming-cheuk
Title: CTO & Co-founder
Driven by a love for creating elegant and effective digital solutions, Ming's journey began with a Mechatronics degree and a subsequent pursuit of a PhD in Bioengineering - six years later, Ming's efforts have resulted in a formidable team that empowers businesses with the latest advancements in AI technology.
#### What do you find most rewarding about your work at ElementX?
‘Being able to help solve some of the most challenging problems for customers together with a team of smart people, modern tools and technology, and efficient processes. I also love that I am able to work in an environment that fosters innovation; a team that isn’t afraid to execute on ideas that go beyond the status quo.’
#### If you could be any animal, what would you be and why?
‘According to ChatGPT which I prompt-engineered to interview me and pick the most suitable animal - Owl: Owls are introverted, solitary creatures that are known for their wisdom and intuition. They are also calm and observant, which matches your emotional tendencies. Owls can fly, which aligns with your dreams and interests.
It also told me I was an octopus in the last option, but I don't want to be an octopus.’
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## Niclas von Ahsen
URL: https://www.elementx.ai/team/niclas-von-ahsen
Title: Professional Services Lead
Niclas plays a vital role at the forefront of cutting-edge technological advancements, originally as an AI Engineer and now as our professional services lead. With a keen focus on seamless integration and optimal use, Niclas collaborates closely with cross-functional teams to ensure the successful adoption of these transformative technologies.
#### What do you find most rewarding about your work at ElementX?
Being able to work across a wide range of industries and technologies to explore how AI can be used in a broader sense and gain a better appreciation for the tech and its different use-cases. Also, what everyone else said about the team and culture.
#### If you could be any animal, what would you be and why?
‘According to ChatGPT I'm a Golden Retriever, which I somewhat vibe with. Alternatives that also kinda work are Brown Bear or Bald Eagle (although I don't think I'm American enough for that one).
‘Like you, Golden Retrievers are outgoing and adventurous but also need plenty of time to rest and recover. They are independent dogs who love trying new things and exploring the great outdoors. They are friendly and social, much like your outgoing nature, but also enjoy spending time on their own.’’
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## Richard McLean
URL: https://www.elementx.ai/team/richard-mclean
Title: Exec Chair & Co-founder
LinkedIn: https://www.linkedin.com/in/richardmcleannz/
Richard has been here at ElementX since the beginning as one of the three co-founders, and has 20 years of service delivery establishing new products and new markets. Richard is eager to explore opportunities, challenges, and productivity improvements in today's dynamic environment.
#### What do you find most rewarding about your work at ElementX?
‘One of the things I really like about being here is the dynamic nature of the technologies we work with; there is always lots to learn when things change fast.’
#### If you could be any animal, what would you be and why?
‘I would be a humpback whale - because they live a long time, they travel the world for free and can probably talk to dolphins.’
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## Yunu Choi
URL: https://www.elementx.ai/team/yunu-choi
Title: Full Stack Engineer
Back in 2016, Yunu took a leap and moved to New Zealand to pursue computer science studies at the University of Auckland. Balancing technical challenges at ElementX, he discovered his passion for shaping product strategies and managing projects with friends in a startup initiative. Outside of work, he volunteers as a primary school teacher at a Korean language school on Saturdays.
#### What do you find most rewarding about your work at ElementX?
‘Working with a talented team to deliver AI excellence and collaborate with clients is the most rewarding part of my role - seeing our work create meaningful, real-world difference keeps me motivated.’
#### If you could be any animal, what would you be and why?
'I would be a cat so I could take a nap with my cat on a cat tree.’
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