Today, teams across the group are applying GenAI to enhance products, boost productivity, and enable faster, more informed decision-making. These are not pilots or proofs of concept; they are live solutions delivering value now.
Rob Smith, Flutter's Associate Director of Emerging Technologies and Insights, says preparation has been critical. "We've been building technology expertise for years, so when generative AI arrived, we already had the foundations, data, talent and platforms to start experimenting and scaling quickly."
Flutter’s federated operating model has also been a core advantage. Brands can test ideas tailored to local market needs while sharing proven approaches across the Group. As Smith notes, this has resulted in “hundreds of AI use cases” and a culture that rapidly scales what works.
"We've been building technology expertise for years."
- Rob Smith, Associate Director of Emerging Technologies & Insights
Building trust at scale
All AI experimentation takes place within Flutter’s Responsible AI Framework, which sets expectations around ethical use, privacy, and regulatory compliance. It provides clear principles around fairness, transparency, and data integrity, giving teams a practical way to assess risk and validate new use cases.
The framework is designed to be pragmatic. “When we mapped out our projects, it added only a few extra questions to what we already do,” says Tomas Bacon, Sportsbet’s Head of Enterprise AI. “It provides guidance that is easy to apply.”
It also ensures ethical considerations are addressed early. One proposed use case involving handwriting analysis was rejected due to privacy concerns — a reminder that not everything technically possible should be pursued.
AI on the frontline
With AI now supporting colleagues internally, Flutter’s brands are also using GenAI to enhance customer experiences. In the US, FanDuel has AceAI, an AI chat experience embedded directly within the Sportsbook.
AceAI is designed to help customers research, explore, and build using natural language. Through the chat interface, customers can analyze player and team performance trends, surface relevant statistics from FanDuel’s extensive data sets, discover bet ideas, and construct their parlays more efficiently.
“As the leading sportsbook, we think there’s an opportunity to set the pace on what customer experiences should look like,” says Jon Sadow, FanDuel’s VP of Product Transformation and Innovation. “We have a tremendous amount of data we can put in customers' hands to empower educated decisions.”
By applying AI to discovery and decision-making, AceAI demonstrates how AI can deepen engagement and improve the betting experience at scale.
Turning ideas into solutions
While FanDuel is reshaping customer care at scale in the US, Australia’s Sportsbet is taking a different but equally ambitious path - using GenAI to streamline internal operations and accelerate product delivery through a tool called Jeeves. Built in partnership with Salesforce, it uses natural language processing to interpret informal customer messages and generate appropriate draft responses.
"Team members were spending huge amounts of time manually handling routine requests," says Bacon. "We built Jeeves to understand the intent behind informal messages and automate much of that process."
A message such as “Can you double‑check my bet settlements?” once required several manual checks. Jeeves now identifies the request, retrieves account information, and prepares a personalized response for review.
Many of Sportsbet’s innovations come from its Six-by-Six ideation approach, which involves a team of six taking ideas from concept to prototype in six weeks. One outcome – a business analysis agent that converts plain‑English feature requests into structured specifications - reduced tasks that once took six hours to roughly one.
Successful tools are then assessed by other Flutter brands, allowing local innovation to translate into global value.
One platform, unlimited potential
In Europe, Flutter SEA has taken a different approach: centralizing AI development through its AI Hub, a single environment where teams build, test and share tools under consistent governance.
“All AI work now happens inside this one environment,” says Katia Colucci, Flutter SEA's Innovation Strategy Director. “That gives us governance and full visibility into what’s being built.”
Completed models are published to the Hub’s internal marketplace, allowing approved teams to adopt and reuse solutions quickly. Early examples include tools that draft development requirements and automate product testing.
Flutter SEA has also invested heavily in AI literacy, with more than 1,200 colleagues participating in workshops that help teams identify and validate new use cases. As Colucci puts it, “If you want AI to succeed, you need people to use it. Awareness and governance must grow together.”
Where we’re heading next
Taken together, these examples show a Group‑wide ecosystem where local experimentation feeds global progress. What began as isolated tests has become a coordinated movement supported by shared platforms, governance, and knowledge flow.
Flutter plans to build on this momentum by expanding access to core tools, deepening platform capabilities and refining Responsible AI standards — giving teams well‑governed ways to embed GenAI into everyday work. “GenAI is making expertise more accessible,” Smith says. “Whether you’re a developer, a marketer or an analyst, it helps you work smarter. That is the real opportunity.”
While many organizations are still circling pilots, Flutter’s experience shows that sustained investment, clear governance and a culture that scales what works can turn generative AI from promise into performance.
More to come as we unpack each AI case study in depth.
