- To start, could you tell us a little about CreateFuture and your role leading its iGaming division?
“CreateFuture is an AI-native, digital consultancy. Effectively, we help organisations make smarter decisions and move faster through technology and product. It’s about bringing together strategy, design and engineering to solve complex business challenges.
“I lead CreateFuture’s iGaming division, which has grown out of six/seven years of working with leading operators in the sector. My role covers the commercial health of the portfolio and our global go-to-market strategy.
“I’m also taking our CF/Beyond proposition to market, identifying where operators can move beyond isolated AI experiments and use it to create measurable business value.”
- AI adoption is accelerating across every sector. Why do you believe iGaming has become a strong example of AI transformation?
“The iGaming sector has many of the conditions that make AI both valuable and urgent. It’s a relatively young, technology-enabled space that moves quickly and is hugely competitive. If one operator launches a new product, finds a new way of operating or invests in a new technology, then competitors are hot on their tails.
“It’s also a transaction-heavy industry. World Cup finals, Super Bowls and NBA playoffs create enormous spikes in activity, putting platforms, data and operational resilience under real pressure. AI has the potential to help operators make sense of that volume and act on it much faster.
“But iGaming also shows why AI transformation is not simply about buying a new tool. Many operators have grown through M&A, leaving them with stitched-together platforms, fragmented data, and a creaking legacy architecture. You can’t just roll out a new piece of tech or AI without sorting out the backstage of your organisation first.”
- Where are you seeing AI deliver the biggest impact across the iGaming value chain today, and what challenges are organisations having to overcome as they scale these applications?
“There is no single application that will transform the whole value chain. And there probably never will be.
“Instead, the opportunity is spread across several areas.
“Data orchestration and governance are fundamental, because operators need to bring scattered information from across the organisation and turn it into something usable. AI has a huge role to play here.
“AI can also improve the customer experience, helping operators understand when contact is relevant, what a player is interested in, and what support they need and when.
“Behind the scenes, AI can reduce manual work, improve operational efficiency, and help modernise expensive and old infrastructure. Hugely important when regulated operators are facing greater pressure on margins. It can also identify patterns associated with fraud or potentially harmful gambling behaviour at a scale that would be difficult for people alone.
“The biggest challenge, though, as already mentioned, is those fragmented foundations. You cannot scale safely on organisational systems stuck together with sellotape and lollypop sticks. Operators need modern platforms, clear data governance and an orchestration layer that defines how models and agents are built, monitored and used.
“Without that, organisations risk creating a collection of disconnected pilots rather than a coherent capability that delivers value across the business.
- What can organisations in other highly regulated industries learn from iGaming’s approach to adopting AI?
“The first lesson is the value of experimentation.
“There is a strong appetite for new ideas in iGaming. Despite it being a tightly regulated environment. It’s something that other sectors such as financial services, healthcare, insurance and telecoms can learn from – that willingness to move, test and iterate rather than waiting for absolute certainty.
“But perhaps the biggest lesson is what happens next.
“Regulation has not necessarily kept pace with adoption, and operators cannot take that as a carte blanche to create black box systems. They need an open dialogue with regulators and must be able to explain how an AI-supported decision was reached, what data informed it and who is accountable for the outcome.”
- What separates businesses that are now creating measurable value from those still stuck in pilot projects?
“Pilots are useful because they create energy, buy-in, and excitement within an organisation. It is a chance to help people understand what the technology can do.
“The problem comes when businesses mistake experimentation for transformation.
“The organisations creating measurable value from AI are the ones that are taking a step back and looking at the business as a whole. That means it’s people, processes, platforms and products, how they work today and how they need to work in the future. They are prepared to have difficult conversations about where the value actually sits, which problems matter most and what they should stop doing.
“More often than not, it’s less a technology challenge but a leadership or an organisational one.
“Momentum is also key. Identify value quickly, prove it and then scale it through a measurable 90-day run. It also makes the return on investment visible, which is essential if AI is going to be a lasting business capability within your organisation.”
- As AI takes on greater responsibility in areas such as customer support and compliance, how should organisations balance automation with human judgement and accountability?
“Be pragmatic about what AI is good at. And where human judgement still matters.
“AI is great at triaging huge volumes of information, pattern identification and guiding a customer to the right support faster than any human. But it can’t interpret individual circumstances. Particularly when somebody is vulnerable, distressed or dealing with, for example, a complicated financial issue.
“The best systems use AI to get customers to reach the right person more quickly, while giving that human agent the information they need to make a better decision. There needs to be clear escalation points, humans in the loop for sensitive cases, and accountable owners for the outcome.
“You can’t outsource judgement and accountability to a model.”
- iGaming has to balance innovation with regulation, customer trust and player protection every day. How can businesses innovate with AI without compromising trust?
“Trust starts with doing what you said you would do. If a customer has been told that they can withdraw their money (after the normal checks, of course), then that promise needs to be met. If they need support on a particular issue, then that needs to be addressed within a reasonable timeframe.
“Operators also need to be transparent about when and where AI is being used, ensure their systems are auditable and traceable, and retain human intervention where the consequences for a player are significant.
“Responsible gambling is a good test of intent. The same capability that can identify a potentially vulnerable player could be used to send them more targeted marketing or to reduce that contact and offer support. The technology does not make that ethical choice. The organisation does, which is why governance and accountability must sit alongside innovation.
- Looking ahead, what is the biggest lesson iGaming can offer other industries as they move from AI experimentation to transformation?
“Speed of experimentation helps, but it’s the foundations that determine whether it actually has a transformational impact.
“As I previously talked about, iGaming’s competitive culture has meant that operators have been experimenting with AI perhaps quicker than other regulated industries. Yet, like every other sector, it will only release value at scale when its data, platforms and operating model are ready.”
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