Eagle Eye has AI built into its product DNA, but we’re also empowering our non-technical staff to get more done with AI. You can too!
Caitlin Stephens, Chief of Staff, APAC, Eagle Eye
Many organisations are struggling with workplace AI adoption. How to effectively deploy it? Should it be allowed? How much should it be used? Used for what? How do you make it maximally impactful for productivity while avoiding any potential risks?
The reality for most companies is that many staff will be using LLMs to assist them with their work, whether it is sanctioned or not. This is a not-uncommon phenomenon known as shadow IT, when employees are using tech outside the prescribed organisational solution stack to help them get their jobs done.
Shadow IT can be a problem, but I believe in the case of AI, it’s critically important for companies to think about how to bring these capabilities into the light so that everyone can benefit.
Eagle Eye, as a company with AI in its product DNA, has also embraced workplace AI. However, while we are all passionate about AI, we appreciate that not everyone is highly technical. We believe that by providing the right tools, support, and encouragement, all employees, including those who are less technical and short on time, can leverage AI to enhance their work. Our embedded product AI knowledge and expertise has given us great confidence to pursue this.
The potential for AI to benefit non-technical team members is significant. Technical and non-technical staff are using AI for productivity gains, from content drafting and support with deep research to progress tracking, automating tasks and reducing toil. We’re even seeing AI used as a coaching tool to help with professional development planning.
Bringing AI solutions into an organisation that lacks experience with such tools can be daunting, but it doesn’t need to be. For companies at the early stages of adopting AI for productivity gains, straightforward generative AI tools like Gemini, Claude and ChatGPT are great places to start.
At Eagle Eye, for example, we also use a tool called ‘Glean’, which is an enterprise AI solution that connects all of our digital tools and data to provide instant Eagle Eye contextualised information for employees in a secure way. It reduces time spent searching for the right information, helps us generate personalised content and can build out automations.
Overcoming the Barriers
There are several things organisations can do to help their non-technical teams who are short on time get something out of AI. For us, as well as adopting tech solutions that are tailor-made for office productivity, we’re focused on encouraging experimentation and proactive engagement with AI.
We recognise there’s often a stigma around AI use for some companies. This fear stems from concerns around security and risks, lack of knowledge of the tools and their capabilities, and the ultimate fear of being replaced. But we see AI as our collaborator, not a threat or replacement for people.
A lack of leadership communication on the topic can lead to teams feeling insecure about what they can leverage AI for; many might question, for instance, if it’s ‘cheating’ to use AI to accelerate tasks and output.
In such cases, it is really important to remember that leadership’s role is to set the mandate, ensure there is structure around it to cover risks, and encourage safe experimentation and sharing wins.
One of the ways we’ve helped our teams grasp and benefit from AI is to identify champions. These are people with a curiosity for exploring AI solutions who can help lead the way for others who are less technical.
AI is moving fast, so through our champs we can focus on continuous learning, knowledge-sharing and relatable use-cases that demonstrate the possibilities. With our champions guiding the way, we’re building confidence within teams to find a starting point.
One of the great things about the currently available LLMs is they can help people to further develop their AI skills, all they need to do is ask for it!
Then, staff can take the examples from their peers and start to think about what might be related to their roles. Don’t forget, with so many vendors now embedding AI functionality into productivity tools, it’s often easy and accessible to get started. Many daily enterprise systems, for example, now have AI assistants, such as Slack and Google workspace giving people tools at their fingertips.
At Eagle Eye, we’ve also encouraged this exploration through an internal hackathon to give people the opportunity to build cool things and show them off. This has not only been fun, we’ve also discovered potential innovations to bring to our day-to-day work.
It is also helping to highlight the power of what’s possible to others in the company who might have previously been less engaged with AI.
Our hackathon involved teams from across the company taking on various use cases and seeing how AI can be used to solve daily challenges. This collaborative learning hits all departments, leans into our champions and those early adopters to lead the way and share back success to educate the business.
Technical Leadership: Executive Perspectives
In addition to developing industry leading AI solutions that are helping to empower organisations in the next phase of loyalty and one-to-one personalisation, we’re staying focused on our ‘why’ when it comes to AI for day-to-day productivity. We feel strongly about being transparent in our vision for AI which will help to enhance our people in the workplace, not replace them.
I recently spoke to Eagle Eye’s Chief of AI, Jean-Matthieu Schertzer, who was appointed as our organisations first ever AI leader to help drive AI innovation in our products and our productivity.
Jean-Matthieu emphasises that even for companies with existing technical AI expertise, the rapid pace of change levels the playing field
“At Eagle Eye we have existing strength in predictive AI, where we’ve built deep expertise over years of deploying, monitoring, and scaling models embedded into our product,” he said.
“That foundation positions us strongly to keep levelling up into the generative and agentic era. But the truth is that the pace at which AI is growing means we all need to keep moving with it; no one knows it all, which levels the playing field.”
For technical leaders, Jean-Matthieu identifies several key responsibilities in driving company-wide AI adoption. First is creating a mandate.
“We have been clear that AI is a core part of our future vision for innovation and productivity. It’s built into our company goals,” he said.
Second is measuring and sharing success:
“Share the tools that are available, present examples from across the business where AI is being utilised, be open sharing data on the gains and talking openly about the failures and learnings to build engagement and breed innovation into other teams for potential future use cases.
Thirdly, Jean-Matthieu said it’s important to explain the risks and limitations clearly.
“AI usage should be backed by policy and awareness of the risks. Leadership teams need to ensure their teams know how to experiment safely, reiterating the basic risks around IP and working on live systems,” he said.
Of course, the space is moving fast and advances in the AI tools available are occurring almost on a week-by-week basis. But that shouldn’t prevent organisations from starting small, starting somewhere.
“We are looking to the external market to learn,” Jean-Matthieu said. “But we don’t need to adopt everything. We want to select the things that are right for us, our team and products. Don’t try to revolutionise your product or ways of working in one iteration.
“Start small. Test your use case, collect the data and look for the gains to select the right thing.”
Additionally, as a leader of teams, I believe the key is maintaining a growth mindset throughout this journey and recognising not everyone will use AI for the same tasks. Individuals should be empowered to look for the best ways that they can collaborate with AI to help them make an impact in their roles.
Real Examples of Impact
Our early quick wins using AI demonstrate what’s possible when you start small and build momentum.
Product and engineering teams have used AI to quickly build prototype UIs, helping teams to collaborate and conceptualise ideas in a matter of minutes, not days. The experimentation is iterative. Through the innovation, our customers’ user experience is transformed with a smoother UX and 65% reduction in clicks to set up functionality in our platform.
This initiative isn’t a magic wand, it still requires human interaction and multiple iterations, but it shows the gains to be made from allowing this experimentation, starting small and growing as we go to increase our speed to market and value to customers.
Additionally, our QA team has leant into AI tools for testing, and early trials are showing significant reduction in time to test as well as improved accuracy. The team has been picking up new skills and gaining confidence as they go.
Meanwhile, our HR team is using AI for data insights. AI is a powerful tool to analyse information much more quickly than even our most data-savvy team members. The result is we can get information more quickly and rather than spend time in analysis mode. We can move to action from our insights with greater velocity and validity.
Leveraging technical expertise, adopting the right solutions for the right users, identifying champions who are really interested, and running activities to research, explore and demonstrate AI’s potential has worked really well for us.
While Eagle Eye does have a built-in advantage when it comes to recognising the benefits and value that AI can bring, highlighting champions to help those less inclined to play with AI, deploying easy-win tools or solutions, and demonstrating what’s possible is something most companies can do to uplift staff engagement with AI.
You don’t have to overhaul everything to get started with AI. Leveraging the right mix of tools, peer champions, and providing teams with freedom to experiment with ongoing learning whilst sharing successes, you can help everyone unlock value from AI.
About Eagle Eye
Eagle Eye is a leading SaaS and AI company, enabling retail, travel and hospitality brands to earn lasting customer loyalty through harnessing the power of real-time, omnichannel and personalized marketing. Our powerful technology combines the world’s most flexible and scalable loyalty and promotions capability with cutting edge, built-for-purpose AI to deliver 1:1 personalization at scale for enterprise businesses, globally.
Our growing customer base includes Loblaws, Southeastern Grocers, Giant Eagle, Asda, Tesco, Morrisons, JD Sports, E.Leclerc, Carrefour, the Woolworths Group and many more. Each week, more than 1 billion personalized offers are seamlessly executed via our platform, and over 500 million loyalty member wallets are managed worldwide.
AI-powered, API-based and cloud-native, Eagle Eye’s enterprise-grade technology is fully certified by the MACH Alliance and has received recognition from leading industry bodies, including Gartner, Forrester, IDC and QKS.
Web – www.eagleeye.com
About Caitlin Stephens
Caitlin is Eagle Eye’s APAC Chief of Staff, leading teams to help brands benefit from the future of loyalty and one-to-one personalisation. With over a decade of people and culture expertise across Australia and the UK, she brings a unique generalist perspective to driving business transformation and growth.
Throughout her career spanning HR leadership roles at SS&C Technologies, Unigrain, and now Eagle Eye, Caitlin has developed a passion for helping people and businesses to be successful whilst creating great places to work. Her experience ranges from developing HR strategy and talent management to leading L&D, employee engagement initiatives and supporting high-growth technology businesses.
