FEATURE: AI in iGaming - How iGaming Leaders are ACTUALLY Using AI

Artificial Intelligence has rapidly evolved from a future-facing concept into one of the most influential technologies shaping the global iGaming industry. From enhancing player experiences and streamlining operations to strengthening compliance, improving fraud detection, powering smarter CRM strategies and accelerating product development, AI is becoming increasingly embedded across every area of the sector.

However, while AI continues to dominate industry conversations, questions remain around how businesses are moving beyond experimentation and turning the technology into practical, measurable outcomes.

In this special feature, iGaming News speaks with leading operators, suppliers and technology providers to explore how AI is being applied in real-world environments. From improving efficiency and personalisation to solving complex business challenges, industry experts share how they are leveraging AI today, the opportunities it presents and the challenges that come with adoption.

Rather than focusing on predictions alone, this feature will highlight tangible examples of AI in action across the iGaming ecosystem. Through insights from industry leaders, we aim to provide a clearer picture of how AI is transforming the way companies operate, innovate and prepare for the future.

As one of the most technology-driven industries, iGaming has consistently been an early adopter of emerging solutions. This feature explores where AI is delivering genuine value today — and what the next phase of intelligent innovation could look like for the sector.

Tom Light, CEO, FIRST.bet

1. Please introduce yourself.

I'm Tom Light, Founder and CEO of FIRST.bet. We build sportsbook technology for operators around the world.

Over the last couple of years, AI has gone from being something companies experimented with to something that's becoming part of almost every decision we make. My job is to understand where it creates a real competitive advantage, whether that's helping our teams build better products internally or helping operators run a smarter sportsbook.

I think we're reaching a point where AI won't be viewed as another tool. It'll simply become part of how modern sportsbook companies operate.

2. AI has evolved incredibly quickly. What surprised you most?

What surprised me wasn't that AI became intelligent. I think most people expected that eventually.

What surprised me was how quickly it became genuinely useful across completely different parts of the business.

Not long ago, engineering, product, QA, trading and business analysis all relied on different specialist tools. Today, one AI model can meaningfully accelerate work across all of those areas.

That's the real shift. AI hasn't replaced expertise. It's made experienced people dramatically more productive.

3. How is your company using AI today?

We use AI in two ways.

The first is internally. It helps our teams write and review code, generate tests, produce documentation, analyse requirements, carry out research and speed up a lot of work that used to take much longer.

The second is inside the sportsbook itself, and that's where it gets really interesting. AI can help traders identify patterns, support pricing decisions, improve risk analysis, detect fraud, personalise the player experience and surface information much faster than a person could on their own.

We're also exploring areas like computer vision to extract additional context from live broadcasts that traditional data feeds simply don't provide.

The important point is that AI supports our people. We don't see it as replacing expertise. We see it as giving experienced teams better information so they can make better decisions.

4. Has AI influenced your product or business strategy?

Absolutely. Today, almost every product discussion starts with the same question: where can AI make this faster, smarter or more valuable for the operator?

That mindset influences everything from trading tools and CRM to personalisation and product development. In many cases, AI isn't replacing existing workflows. It's making them more effective.

We don't think about AI as a standalone product. We think about it as a capability that should improve every part of the sportsbook.

5. Looking ahead, what role do you see AI playing in the future of iGaming?

Looking ahead, I think AI will become part of almost every area of iGaming. It will help operators make better decisions, respond faster and deliver a more personalised experience to every player. The companies that use it well will simply build better products.

I don't believe AI will replace traders. I believe it will replace traders who don't know how to work with AI.

Over the coming years, trading will become more predictive, risk management will become increasingly automated, and sportsbooks will become much more personalised for every player.

In the end, I don't think the winners will be the companies talking the most about AI. They'll be the ones that use it every day to build a better sportsbook while keeping experienced people responsible for the decisions that matter.

Igor Pikul, VP of Engineering, GR8_TECH

Please introduce yourself. Tell us about your role, your company, and how AI fits into your day-to-day responsibilities.

I’m the VP of Engineering at GR8_TECH, having previously served as the Chief Architect. In my current role, I focus on shaping the company’s engineering culture, defining our architectural vision, and driving key architectural decisions.

One of our biggest challenges is evolving our engineering culture to embrace an AI-driven mindset. GR8_TECH has launched a company-wide AI strategy that outlines how we will build AI capabilities and integrate AI-powered ways of working across the organization.

A core element of this strategy is transforming our software development lifecycle into an AI SDLC process. We want engineers to use AI throughout the software development lifecycle, especially for creating the first version of the code. But AI is only a tool. Engineers are still responsible for defining the solution, guiding the AI, reviewing and validating its output, making the important technical decisions, and ensuring the final product meets our quality standards. Our goal isn't to replace engineers - it's to make them more productive by letting AI handle repetitive work while people focus on solving the hard problems.

Another important focus for us is applying AI and machine learning to our products. Over the years, we've built a number of in-house ML-powered solutions that deliver more relevant experiences for end users while helping our partners increase their revenue. One example is our recommendation platform, which personalizes content based on user behavior and preferences. Right now, our main investment is in sports recommendations, where we're continuously improving their quality and relevance. Over the next six months, we'll also be expanding our efforts into casino recommendations, bringing the same level of personalization to that part of the product.

In my day-to-day work, I use AI in several ways. It helps me automate and organize routine tasks, explore new ideas, and experiment with AI-assisted software development practices. I'm particularly interested in understanding how AI can improve the entire software development lifecycle—from requirements and design to implementation, testing, and documentation.

I also spend time experimenting with new AI tools and workflows to better understand what works in practice. This hands-on experience helps me shape our engineering strategy and identify approaches that can be successfully adopted across our engineering organization.

AI has evolved incredibly quickly over the past few years. Has there been a moment, use case or capability that genuinely surprised you or changed the way you think about artificial intelligence in business?

What has truly surprised me is the speed at which AI is transforming the world. If we look at software engineering, it's enough to compare how we were using AI just a year ago with what it is capable of today. The gap between those two points in time feels enormous. In such a short period, we've seen an incredible leap - not only in the capabilities of the models themselves, but also in our understanding of how AI can fundamentally change the software development process.

But if we look beyond engineering, the publication We Must Act Now probably had the biggest impact on me. It encourages you to think about AI from a much broader perspective. Its core message is very simple: during every previous technological revolution, society had time to adapt. People had time to learn new skills and professions, and companies had time to redesign the way they worked.

With AI, the situation is fundamentally different. The pace of change is so rapid that we simply don't have the adaptation time we've had with previous waves of technology.

How is your company using AI today? Which AI tools, platforms or technologies are you actively using, and what measurable benefits have they delivered for your business, customers or internal operations?

For software development, we primarily use Claude Code and models from Anthropic, which I currently consider among the most mature solutions in the industry. We also use Amazon Web Services technologies, including Amazon Q Developer and Amazon Bedrock, and we are planning to evaluate other tools that can help to improve performance of our employees .

In addition, we maintain a lot of AI tools that employees can use across the company. This includes solutions for tasks such as generating meeting notes, recognizing speech, working with documents, and automating and organizing routine tasks.

For our customers, AI-powered products such as our recommendation system drive higher engagement and revenue growth through more personalized user experiences.

The measurable benefits for our technology organization are increased engineering productivity and higher quality. AI reduces the time engineers spend on repetitive work, accelerates development cycles, improves code quality through better testing and reviews, and helps teams deliver new features to customers faster.

Has AI influenced your company's product or business strategy? Can you share any AI-powered products, features or initiatives you've launched recently, or are planning to introduce over the coming months?

Yes, AI has influenced both our product strategy and our broader business strategy.

From a product perspective, our main AI-powered initiatives are our recommendation system and anti-fraud system. We launched a new version of the recommendation system at the beginning of June and are currently working on improvements for all our main products, such as Sport and Casino.

From a development perspective, our main goal is to change the mindset of developers and adopt in development new practices, approaches and technology to significantly improve productivity and quality.

The overall strategy is to adopt AI at every level of the company, improve productivity and ensure that the technology creates value for the business.

Looking ahead, what role do you see AI playing in the future of iGaming? How do you expect artificial intelligence to transform the industry over the next three to five years, and where do you believe the biggest opportunities still lie?

To be honest, it's difficult to predict exactly how AI will transform the iGaming industry over the coming years because the technology is evolving at an extraordinary pace.

What seems certain, however, is that AI will dramatically increase the speed of innovation. It will enable companies to build products faster, streamline internal operations, and respond to customer needs more quickly and effectively.

I expect AI to reshape the iGaming industry just as it will transform many other sectors of the economy. In many respects, I believe we're already in a new industrial revolution driven by AI.

Giorgi Tsutskiridze, Chief Commercial Officer at SPRIBE

1. Please introduce yourself.

My name is Giorgi Tsutskiridze, Chief Commercial Officer at SPRIBE. I work closely with our innovative products and long-term commercial strategies. AI has become an important part of both our internal operations and the way we think about the future of player engagement. From analysing market trends to improving decision-making and accelerating collaboration across teams, AI is helping us move faster while remaining focused on delivering real value to our partners.

2. AI has evolved incredibly quickly over the past few years. Has there been a moment, use case or capability that genuinely surprised you or changed the way you think about artificial intelligence in business?

What impressed me most wasn’t a single technological breakthrough - it was how quickly AI became a practical business tool. A couple of years ago, many companies were experimenting with AI in isolated areas. Today it’s embedded into everyday workflows, helping people analyse data, generate insights, solve complex problems and make faster decisions. That shift has fundamentally changed how businesses can scale without simply adding more resources. AI is becoming less about automation and more about empowering people to focus on higher-value work.

3. How is your company using AI today?

Like many technology businesses, we’re using AI across multiple departments. It supports software development, data analysis, content creation, localisation, market research and internal knowledge management. Commercial teams also use AI to analyse market opportunities and better understand operator needs, allowing us to respond faster and more effectively.

The biggest benefit has been efficiency. AI helps us reduce repetitive work, accelerate execution and spend more time on strategic thinking, innovation and building stronger relationships with our partners. Ultimately, that enables us to bring new ideas and improvements to the market much faster.

4. Has AI influenced your company’s product or business strategy?

Absolutely. AI is influencing how we think about the next generation of player experiences and operator tools. The opportunity isn’t simply to make products more automated - it’s to make them more intelligent, more personalised and more responsive to player behaviour.

Across SPRIBE, we’re continuously evaluating where AI can create measurable value, whether that’s improving operational efficiency, supporting better decision-making or enhancing engagement. Our philosophy is straightforward: AI should solve real business challenges rather than exist as a feature for its own sake.

5. Looking ahead, what role do you see AI playing in the future of iGaming?

AI will become a core layer of the entire iGaming ecosystem. Over the next three to five years, we’ll see increasingly personalised player experiences, smarter CRM, more sophisticated fraud prevention, better responsible gaming tools and significantly faster product development.

For me, the biggest opportunity lies in helping operators better understand their customers in real time while giving teams the ability to innovate faster than ever before. Companies that combine AI with strong product thinking, creativity and human expertise will be the ones that continue to lead the market. AI won’t replace great ideas - but it will help great ideas reach players much more quickly.

Konstantinos Veletas, Product and Sales Solution Architect at RavenTrack

1. Please introduce yourself. Tell us about your role, your company, and how AI fits into your day-to-day responsibilities.

I am Konstantinos Veletas, Product and Sales Solution Architect at RavenTrack. My role sits at the intersection of product, sales and to some extent account management, where I act as a technical bridge between teams and help translate product capabilities into practical business value. A big part of my day-to-day work involves giving demos of our platform and guiding potential clients through its features in a way that is clear, relevant and commercially meaningful. I really enjoy being close to both the product and the customer, as it gives me the opportunity to understand client needs more deeply and help shape solutions that support their goals.

AI already plays an important role in how I work. In a fast-changing industry shaped by regulation, product development and infrastructure changes, it helps me stay organised, prioritise my day and work more efficiently. I also use AI in a more analytical sense to identify patterns, support predictions and speed up tasks that would otherwise take much longer to complete manually. That gives me more time to focus on judgement, context and decision-making, which are still essential.

2. AI has evolved incredibly quickly over the past few years. Has there been a moment, use case or capability that genuinely surprised you or changed the way you think about artificial intelligence in business?

What surprised me most was how quickly AI became genuinely useful in day-to-day business work. Creating detailed reports with trends and accurate predictions, or putting together a PowerPoint presentation in minutes, was impressive the first few times I used it. Also, the capability that has probably made the biggest difference is how it can turn conversations into practical output almost immediately, something which we’ve gotten so used to these days we nearly take it for granted.

In my daily workflow, the most valuable example is call summaries and task breakdowns. Being able to capture the key points from a discussion, highlight next steps, assign priorities and even surface stakeholder concerns or client pain points has changed the way I work. It has made follow-up faster, clearer and more structured. Something we are using on a daily basis, but we are also working to soon integrate to the AI build of the platform and make it available for our clients, is data summaries that identify trends, give important insights and recommend actions rather than simply displaying reporting tables.

3. How is your company using AI today? Which AI tools, platforms or technologies are you actively using, and what measurable benefits have they delivered for your business, customers or internal operations?

AI is embedded across our business, supporting engineering, product, commercial and marketing teams to improve productivity and reduce time spent on repetitive tasks. Our development teams use AI to accelerate software development, testing, debugging and documentation, enabling faster delivery without compromising quality.

We’re using AI internally to create proposals, analyse customer feedback, produce content and support day-to-day decision-making.

AI is beginning to enhance the customer experience, particularly around onboarding, where we’re simplifying implementation and reducing manual effort. The biggest benefit has been allowing our teams to focus more time on innovation, problem-solving and delivering value to customers rather than administration.

4. Has AI influenced your company's product or business strategy? - Can you share any AI-powered products, features or initiatives you've launched recently, or are planning to introduce over the coming months?

AI has become a key part of our product strategy, with a focus on making affiliate technology easier and faster to implement. We’re developing AI-assisted onboarding that will help customers configure integrations, map data and identify potential issues much earlier in the implementation process.

Longer term, we’re building AI into the platform itself to proactively identify trends, surface insights and recommend actions rather than simply displaying reports.

We also see AI playing an important role in customer support, helping diagnose issues and provide faster, more intelligent assistance. Our goal is to remove complexity from affiliate management so customers can spend less time configuring technology and more time growing their business

5. Looking ahead, what role do you see AI playing in the future of iGaming? - How do you expect artificial intelligence to transform the industry over the next three to five years, and where do you believe the biggest opportunities still lie?

AI will become a fundamental part of how operators, affiliates and technology providers make decisions, moving from reactive reporting to proactive intelligence. It will help businesses optimise marketing performance, identify opportunities faster and automate many of today’s manual operational processes.
For technology providers, as an example, AI will significantly reduce the complexity of onboarding, support and day-to-day platform management. As AI adoption grows, transparency and explainability will become increasingly important, particularly in regulated industries where trust and accuracy are essential.

The businesses that gain the greatest advantage will be those that use AI to enhance human expertise rather than replace it, combining automation with industry knowledge to deliver better outcomes

Euan Maskell: Co-Founder of Daw Global

1.Please introduce yourself. Tell us about your role, your company, and how AI fits into your day-to-day responsibilities.

I am Euan Maskell, Co-Founder and Director of Daw Global, a multi-currency banking provider with a connected set of financial services integrated. A client holds accounts in over 20 currencies, moves money internationally, transacts in cryptocurrency and reconciles all of it in one place, rather than assembling a separate provider for each part of that and attempting to join them together afterwards.

 iGaming is the sector we work in most closely, and particularly the offshore licensed space within it, along with offshore licensed businesses in other service sectors including cryptocurrency and broker-dealing.

These businesses are financially sound and properly licensed, yet the vast majority of financial institutions decline to offer them banking facilities, because the sector carries a risk profile that most compliance teams have decided in advance they will not touch regardless of how well the individual business is run.

 The consequence is that operators of real substance spend a disproportionate amount of their time on their banking arrangements rather than on running the business. Solving that problem is the reason we exist and providing banking and a financial ecosystem those operators can rely on is the point of it. 

2. AI has evolved incredibly quickly. What surprised you most?

The biggest surprise has been the extent to which I have come to rely on it. It began with refining emails and wording, and it now forms part of almost every element of the working day, which is not where I expected to be given that less than a year ago I was fairly sceptical about overly using it.

Part of the reason it has spread so quickly is that there is nothing to learn, because you describe what you need in ordinary language and the work comes back. The warnings also proved closer to the truth than the reassurances, in that a considerable amount of work which previously required a person no longer requires one, and that is more useful to plan around than to argue with. 

The caution I would attach to it is that the technology should not be permitted to think on your behalf, because it will produce a confident answer whether or not that answer is correct. In our working environment, as in everybody else's, a confident wrong answer is more dangerous than no answer at all. 

3. How is your company using AI today?

Compliance accounts for the vast majority of the use, and onboarding is where AI earns its place. An iGaming operator or an offshore licensed entity arrives with a substantial file of documentation drawn from more than one jurisdiction and not always in English and translating that material and working through articles of association and shareholder registers was previously an entirely manual read.

AI now handles over 50% of that review, extracting the ownership position and flagging the inconsistencies between documents. It does not replace the review itself, because every onboarding application is still generally examined by our compliance department, and the mechanical part of the work has gone while the judgement has not.

There is also a deliberate limit to how far we take it. A client dealing with a person who understands their business matters to them, and it matters to us and automating that relationship would remove the one of the key reasons they work with us in the first place.

 4. Has AI influenced your product or business strategy?

AI has had no influence on the product itself, and the service a client receives is unchanged by it, because no element of that service depends on it in any way.

 The amount of work we are able to handle has increased significantly. Our regulatory processes were heavily manual before and a proportion of that manual work remains. With comprehensive AI procedures supporting compliance, the team now gets through considerably more work and can complete the onboarding and opening process more efficiently.

The strategy itself was not rewritten, because AI is not a reason to change direction. It streamlines the process rather than replacing anybody, and the time and resource that frees up is put into strengthening procedures in other departments, such as business development and marketing. The compliance team is the same team doing the same work with better tools behind it, and the benefit of that shows up across the rest of the business.

5. Looking ahead, what role do you see AI playing in the future of iGaming?

I had a discussion about this recently with Nilesh Mistry, Chief Operating Officer (COO)of Bettor Faster, which builds AI systems for betting operators and gaming studios, and he put it more precisely than I would. The operators pulling ahead are the ones intelligently adding AI to what they already run rather than waiting to rebuild their stack around it. Nilesh’s point is that capability stopped being the limiting factor some time ago, and that the constraint now sits in governance and trust.

I agree entirely. Although we are a financial services provider rather than a gaming one, understanding where AI takes our clients and the markets they operate in is essential, because that is what allows us to adapt alongside them.

The other point I would make is that none of this is a future development. It is happening now, and considerably faster than most people expected two years ago. An operator treating AI as something to plan for next year is already behind the ones who started. 

Jake Agius, Head of Marketing, Stakelogic

1. Please introduce yourself.

I’m Jake, Head of Marketing at Stakelogic, where I lead the full marketing function across brand, communications, digital, employer branding and commercial activation. My job is to ensure marketing contributes directly to business performance through stronger game launches, better support for our commercial teams and a consistent Stakelogic presence across every audience.

AI is now part of how I work every day and is increasingly present within the marketing function. It supports research, planning, briefing, content development, analysis and quality control, helping us move faster from raw information to something useful.

My focus is making sure we use it with intent. It should improve the quality of our thinking as well as the speed of execution, while people remain responsible for context, decisions and the final output.

2. Has anything changed how you think about AI in business?

What surprised me most is how useful AI can be before the output stage. It is often described as a content tool, but I find it more valuable when it is organising context, identifying inconsistencies and helping a team examine a problem from several perspectives.

Marketing constantly moves between brand, product, commercial and player considerations. AI helps us bring those viewpoints into the same conversation and test whether an idea still holds up when seen through a different lens.

That changed how I think about the technology. I initially saw it mainly as a faster route to production; I now see it as a way to improve the thinking that happens before production. A faster answer is useful, but sometimes the real advantage is being prompted to ask a better question.

3. How is Stakelogic using AI today?

Within marketing, AI supports activities including synthesising research, structuring briefs, testing creative directions, refining content, supporting localisation and checking work against our brand principles.

We also use it to organise performance context and surface questions that deserve closer investigation. It does not make the commercial decision, but it helps people approach that decision with better-prepared information.

The clearest benefits are shorter briefing and review cycles, more alternatives evaluated within the same timeframe and greater consistency across outputs. This gives the team more time for work that needs human experience: judging relevance, understanding partners and improving the final result.

Factual verification, creative judgement and approval remain with the relevant people. AI accelerates the process; it does not replace ownership.

4. Has AI influenced Stakelogic’s product or business strategy?

Yes, but we do not treat AI as a separate strategy or a starting point. It has influenced how we think about workflows, how information moves between teams, which repetitive tasks can be simplified and where clearer context could improve a decision.

Our approach is to begin with a genuine business problem, define what a useful result would look like and only then determine whether AI can improve the process. That prevents us from adding technology simply because it is fashionable.

It has also reinforced the importance of structured data, consistent ways of working and clear accountability. AI can make a strong process faster and more scalable, but adding it to a weak process usually just gives you a faster weak process. The goal is practical improvement, not AI for its own sake.

5. What role will AI play in the future of iGaming?

Over the next three to five years, AI will become part of the operating infrastructure of iGaming, supporting product development, localisation, customer service, CRM, compliance, fraud prevention and commercial analysis.

The bigger shift will be from isolated tools to connected decision support. Companies will be able to identify an important change, understand the surrounding context and bring it to the right person much sooner.

Because iGaming is regulated, speed cannot come at the expense of accountability. Businesses will need secure information handling, clear controls and human ownership of decisions affecting players, partners and regulatory obligations.

Access to AI will not be the competitive advantage; most companies will have similar technology. The advantage will come from combining it with high-quality data, specialist knowledge and the discipline to act on what it reveals.

Andrii Savyntsev, Product Owner Team Lead at ICONIC21

1. Please introduce yourself. Tell us about your role, your company, and how AI fits into your day-to-day responsibilities.

I'm Andrii Savyntsev and I am a Product Owner Team Lead at ICONIC21. We're a B2B provider, building content for operators across live casino, RNG, and slots, rather than running our own player-facing brand.

As a relatively young provider, we have always needed to be deliberate about creating products that stand out rather than simply replicating what already exists. We didn’t want to make another blackjack table that looked like everyone else's. That is partly where AI comes in. There's AI as an internal tool that supports parts of our production, but always with people reviewing the output. And there's AI powering an actual product, which is the more interesting part of my role right now. The clearest example of that is iDealer Blackjack.

2. AI has evolved incredibly quickly over the past few years. Has there been a moment, use case or capability that genuinely surprised you or changed the way you think about artificial studio intelligence in business?

Yes, how players responded to AI surprised me.

iDealer Blackjack is a standard RNG blackjack table, but the dealer is a live, interactive AI-powered avatar. It talks, it listens and it remembers you between sessions. Going in, we expected players to treat it like a slightly more talkative RNG table.

On our own tables, that's not what we saw. A lot of players opened up more than they would with a human dealer. They ask the AI-powered dealer trivia and history questions, crack jokes, and start conversations they probably wouldn't start in a live studio. I want to be careful here: that's what we've observed on our tables, not a universal rule about how everyone behaves.

We believe that this is because we’re honest about the fact that it’s an AI-powered dealer. Nobody is pretending the dealer is human, which may reduce some of the social friction players experience in a traditional interaction. That changed how I think about AI in a product: a lot of people are thinking AI-powered content needs to be as close to human-made as possible. When we made a product that is clearly AI-powered, players responded better to it.

3. How is your company using AI today? Which AI tools, platforms or technologies are you actively using, and what measurable benefits have they delivered for your business, customers or internal operations?

The flagship is iDealer Blackjack, built with real-time AI avatar technology. The maths, speed and RTP are exactly standard RNG blackjack. It can understand players across a wide range of languages and respond in English while taking local context into account.

We deliberately place it in the RNG library, not live casino. In live casino, the player is there partly to see a human shuffling. iDealer reaches a different, underserved group inside existing RNG traffic: players who don't want to sit in silence but don't want a full live table either.

On measurable benefits: we’re seeing higher retention and acquisition numbers than other RNG games. We think this is because it opens up a segment of RNG players we weren't serving before.

4. Has AI influenced your company's product or business strategy? Can you share any AI-powered products, features or initiatives you've launched recently, or are planning to introduce over the coming months?

It has influenced our business strategy, but not in the way the phrase "AI strategy" usually implies. We didn't set out to add AI to everything. We used it in the content we saw fit, where we added personality to a category that didn't have one before. Our business strategy wasn’t to add AI to everything and hope for the best but to use it to address gaps in the market. In the coming weeks we're adding further tables as well as more options for customization for operators, building on the table layout and design settings we already offer. That gives operators a dealer that fits their brand rather than a one-size-fits-all avatar.

5. Looking ahead, what role do you see AI playing in the future of iGaming? How do you expect artificial intelligence to transform the industry over the next three to five years, and where do you believe the biggest opportunities still lie?

I think a lot has been said about AI but most of the predictions haven’t been backed up by concrete statistics. What I will say with confidence is that AI-powered dealers are more likely to grow into their own space, rather than replacing anyone or anything.

The area I'm most interested in is personalization and accessibility. We’re building an AI-powered dealer that adapts to the player in front of it, and that can hold a conversation across languages. Some have speculated that AI dealers can replace live, but we believe that live serves a player and a mood that an AI-powered RNG table doesn't and cannot, and pretending otherwise misreads why people play it. We are building something genuinely different and being honest that it's different.

The part I'd push back on is the assumption that "more AI" is automatically the answer. The providers that get this right over the next few years will be the ones who are precise about where AI adds something a player actually notices and disciplined about where it doesn't.

Matt Harbord, Chief Product Officer at Pythia Sports

1. Introduce yourself and how AI fits into your day-to-day role

I’m Matt Harbord, Chief Product Officer at Pythia Sports, which develops pricing, trading and risk management technology for sports betting.

AI is now part of quite a lot of my day-to-day work. Pythia began by using it heavily for product ideas and prototypes. We can take an idea and build something tangible to test. If that idea involves a user interface, we can interact with an early version and say, “This is roughly what we mean,” without trying to capture everything in a long written specification.

AI has since moved much further into the delivery process. We use tools such as Claude, connected through MCP integrations to systems including Jira, to help us break products down into tickets, manage backlogs and keep track of the roadmap.

For me, AI now has a role from the first product idea through to delivery. We use it in some of Pythia’s products and to help our teams build them. Right now, much of the immediate value comes from the work happening behind the scenes.

2. A moment or use case that genuinely surprised you or changed how you think about AI

My view changed gradually as I watched AI cross a series of thresholds and previously unusable ideas became workable.

In the earlier days of AI-assisted development, you could build a prototype quite quickly, but once it became sufficiently complex, everything started to fall apart. The model would lose context, you would fix one bug and introduce another, then fix that and break something else.

The models got much better. We also became much more disciplined about how we worked with them. We started applying test-driven development principles even to rapid prototyping.

That included validating the underlying data, testing API connections and creating “golden” tests using real data with known outcomes. New functionality could then be checked against something verifiable.

That allowed us to cross an important line from “AI can make a convincing prototype” to “AI can help us build something constrained but reliable enough to put in front of an actual client.”

Internally, we refer to some of these as “Kairo” projects. A major meeting such as Cheltenham is the kind of deadline where this approach can help. We may have a product idea that would be valuable to a client but cannot fit through the full engineering roadmap in time.

Rather than deliver nothing, the product team can sometimes use AI-assisted development to create something deliberately constrained but functional, put it in front of the client, learn from real usage and then take the successful ideas back into the normal development process to be hardened properly.

That was probably where my view changed most. AI-assisted development gave us a way to test real products and propositions with clients much earlier.

3. How does Pythia use AI today, and what measurable benefits has it delivered?

We use AI in several ways across the business. That includes direct product applications around some of the softer customer-facing content, alongside internal uses within our quant and technology teams.

Our quant and technology teams use AI to help with merge requests, understand the potential scope and impact of code changes, analyse logs and investigate issues.

Through integrations into systems such as Datadog, an AI can examine large volumes of logs, chase down a potential issue and provide a first view of where it thinks the problem lies.

I wouldn’t describe that as autonomous software development. We are deliberately not at the point of simply allowing an AI to make whatever changes it wants to a live production system.

We’re a relatively small team, so one way I think about it is that AI can sometimes give you the equivalent of having a junior developer spend a couple of hours looking into a problem before an experienced person picks it up.

The experienced person still checks the answer and challenges what the AI has produced. Instead of starting from zero, the team may already have a hypothesis, relevant logs and pointers towards the part of the system that needs investigation.

From a product perspective, I estimate that AI saves me around one day a week on routine work. On new projects or RFPs, I can now spend a day building a prototype user interface at a level of detail that would previously have taken a designer the best part of a week.

For the quant team, the savings often come in blocks of 10 or 20 minutes on repetitive daily tasks. I would put the overall saving at around one day a week per person. It also takes some of the routine thinking out of their day, so they can use their brains on the work that matters more.

We don’t track those savings as a formal metric yet. They are working estimates based on what the teams are seeing day to day.

4. Any AI-powered products or features you have launched or are planning?

Pythia is fundamentally a pricing and trading business. Our core strength is analysing large volumes of data and using that to make prices. Much of our work with LLMs today turns that underlying analysis into content that is easier for customers to consume. This includes comments, ratings, verdicts and explanations adapted for different languages and audiences.

There is an important distinction here. A premium handwritten racing product created by somebody with 20 years of experience has an enormous amount of human context behind it. They know the courses, trainers, riders and nuances of the sport.

I don’t think AI-generated content should pretend it can automatically reproduce all of that, and I don’t think replacing that expertise should necessarily be the objective.

At the mass-market level, strong underlying data can provide useful commentary and explanation at a scale and marginal cost that would previously have been impossible.

A conversational racing interface is on our wish list, although it is not on the roadmap yet. I think it is also a direction the wider industry may explore, particularly in racing, where form and runner histories can be complex.

A natural-language interface could allow somebody to ask how a horse has performed on similar going or over the same distance, and which runners have the strongest historical profile under those conditions. The same approach could also help identify patterns in football bet builders.

A conversational product would still need to offer enough value to justify the running costs.

Bet recommendations and pre-built bets are next on our delivery list. We already have some pre-built functionality, with the AI work still to come. That could involve using AI to generate bets internally or explain them to the customer.

5. Where do you see AI taking iGaming over the next three to five years, and where are the biggest opportunities?

I think there are three distinct areas.

The first is simply how companies operate and build products.

AI is going to become increasingly embedded across product management, engineering, testing, observability and customer support.

The important thing is that this is not necessarily glamorous. Some of the highest-value applications are operational, including backlog management, change reviews, log investigation and ticket triage.

I think we will increasingly see AI acting almost like an additional layer of junior capacity across organisations, with humans remaining responsible for judgement and verification.

The second opportunity is how customers interact with data and betting products.

iGaming and sports betting generate enormous amounts of structured information, often presented through fixed interfaces. AI can make those experiences more conversational and allow customers to interrogate the underlying information in natural language.

The third area is pricing and trading, where I am more cautious.

I come from a quantitative background, and we have traditionally preferred clear-box models where we understand why the model is behaving the way it is.

When the industry talks about AI today, it often really means LLMs. I don’t think you should assume that because LLMs are extremely capable at language and coding, they are automatically the right technology for creating robust pricing models.

There is a real danger of building something that looks clever but is effectively just overfitting historical data.

Where I see much more immediate value is in observability.

An operator generates enormous streams of information around prices, liabilities, customer activity and exposures. AI agents can continuously watch that data, identify unusual patterns and flag them for an experienced trader.

For me, the trader remains central. AI can give them additional eyes across the platform 24 hours a day, flagging anything that may need investigation.

That is the biggest opportunity over the next few years. AI can give organisations scale, monitor vast amounts of information and explain what it finds. The human role remains essential because people bring taste and judgement and remain responsible for high-stakes decisions.

Max Tesla, CEO & Founder, Blask

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1. Please introduce yourself.

I'm Max Tesla — a husband, CEO, and the second founder of Blask, in that order, on purpose. Blask is an AI-native iGaming analytics platform: five years building proprietary datasets across 130-plus jurisdictions.

Where AI actually shows up in my day is less glamorous than people assume. I built and still personally maintain the technical infrastructure behind my own research setup — a small Postgres + embeddings stack wired into a handful of MCP servers in Cursor — because I'd rather understand a tool by building with it than by reading a vendor's pitch deck. Day to day that means I'm usually the one testing whether Claude Sonnet's extended-thinking mode or Claude actually saves someone on my team an hour, not delegating that judgment to a slide deck. AI isn't a department at Blask. It's a tool I personally still argue with.

2. AI has evolved incredibly quickly over the past few years. Has there been a moment, use case or capability that genuinely surprised you or changed the way you think about artificial intelligence in business?

Two moments, years apart. The first was realizing my own attitude had to change. I started coding at 12, and back then my ambition was blunt: build something efficient enough to make tens of thousands of jobs disappear. Ethics wasn't even in the conversation. Watching the job-replacement debate actually play out in the real world — not in a comment section — is what pushed me toward supervised learning as a personal principle: AI should let people do more with less friction, not remove them from the loop.

The second was smaller and more recent, and it taught me more about how I personally use the tool. I was drafting a tweet with an AI model, and it handed me a punchline that was factually fine but read exactly like AI wrote it — the flat, generic close you can spot from a mile away. My instinct was to rewrite it myself. Instead I sent the model back to do a live web search on the topic first. It came back with something concrete — a real-world comparison that actually landed. That's when it clicked: don't polish AI slop by rewording it, redirect the model to go find something true and specific instead. Small lesson, but it changed how I brief every AI tool I've used since.

3. How is your company using AI today? Which AI tools, platforms or technologies are you actively using, and what measurable benefits have they delivered?

I care less about the tool than the test I run on it. If something has "AI" bolted onto an interface and I can already do the job faster in Cursor, I don't touch it — I've said as much internally when a database client shipped an AI assistant nobody asked for. The stuff I'm actually a heavy user of, personally: Sonnet / Fable with extended thinking for research-heavy work like turning a cold LinkedIn profile into personalized outreach, web-search-enabled models for drafting my own public writing, and the MCP setup I built myself so any of us can pull live web data straight into an editor instead of tab-switching all day.

Inside the product, AI isn't a feature you can point at — it's the reason the data exists at all. Blask Brand Discovery combines search, translated industry keywords, and computer vision reading operator screenshots plus a human review pass to identify all brands across every market we cover.

Blask Index, our core demand metric, uses AI to clean and smooth noisy Google Trends and Keyword Planner data and separate real brand signal from generic search noise. On the games side, the same computer-vision layer scans thousands of casino lobbies to work out what's actually live, where, and how it ranks. Strip the AI out of that pipeline and there's no product left — just a spreadsheet someone filled in by hand, three weeks too late.

The discipline is the same whether it's my own workflow or the product: it doesn't matter if the job needs an API call to a proprietary LLM, a computer-vision model, or plain NLP — pick whichever is fastest, cheapest, and most reliable, and nothing ships without a human checking the output. AI isn't some magic wand that fixes everything; it's a messy black box filled with entropy and unpredictability. I don't trust a model I haven't personally tried to break.

4. Has AI influenced your company's product or business strategy? Can you share any AI-powered products, features or initiatives you've launched recently, or are planning to introduce?

Yes — and it's a conviction I hold personally before it's a company line. Bolting a couple of AI features onto an existing product isn't a strategy, it's decoration. The companies that actually win are the ones who rethink how the product works, how decisions get made, and how the team itself is built around that — not just where the chatbot icon sits on the page.

That belief is why we rebuilt Blask's core product around a conversational layer: an operator asks a question in plain language and gets an answer from the same data model we've spent five years building, instead of hunting a dashboard for it. It also meant restructuring how we staff the company — data engineering and ML stay central, dashboard-building work scales back — and opening an in-house consulting practice for operators who want a person alongside the data, not just an API response. If a strategy can't survive being rebuilt around what AI actually changes, it wasn't a strategy.

5. Looking ahead, what role do you see AI playing in the future of iGaming over the next three to five years, and where do you believe the biggest opportunities lie?

Predicting AI five years out is a fool's errand — I'd rather look at Gartner's Hype Cycle than pretend I own a crystal ball. Some applications are already commoditized, some will sit in research forever, a few will never leave academia. But here's what I'll commit to, because I said it out loud before it was a trend piece: the winners won't be the ones with the flashiest chatbot. They'll be the ones who put AI into the infrastructure, not the shop window — machine learning quietly doing lobby monitoring, anti-fraud, dynamic pricing, tied to real market indicators instead of one player's last click.

The trap I see the industry walking into is treating "AI agent" as a synonym for "autopilot." Without real data pipelines and hypothesis testing underneath it, a chat window that promises to run your marketing or product decisions for you is a nice demo, not a working tool — and iGaming is going to burn real budget finding that out the hard way over the next couple of years. The opportunity belongs to whoever builds the boring, unglamorous infrastructure layer first. That's where the actual value sits, not on the surface.

Jaime Ocampo, Managing Director, Asia at Golden Whale

1. Please introduce yourself.

I'm Jaime Ocampo, Managing Director, Asia at Golden Whale. My role is to lead our expansion across the Asia-Pacific region, working with operators to help them make better commercial decisions through AI-powered decision intelligence.

I've spent more than 20 years in gaming, digital technology and data-driven growth, with leadership roles across companies including Electronic Arts, DeNA and Kochava. Throughout my career, I've seen how player expectations and operational complexity have evolved, making data-driven decision-making more important than ever.

At Golden Whale, AI isn't an add-on. It's at the core of how we help operators optimise player engagement, retention and incentive strategies. My focus is on helping operators move beyond static, campaign-driven approaches and adopt continuous, real-time decision-making that delivers measurable business outcomes.

2. AI has evolved incredibly quickly over the past few years. Has there been a moment, use case or capability that genuinely surprised you or changed the way you think about artificial intelligence in business?

Having worked in gaming for many years, I've always believed data should drive better decisions. What has surprised me is how quickly AI has moved from analysing historical data to making intelligent recommendations in real time.

One of the biggest shifts is the ability to evaluate millions of player interactions simultaneously and determine the next best action for each individual player. That's fundamentally different from relying on predefined segments or campaign rules.

For me, AI's real value isn't automation for its own sake. It's improving decision quality at a scale that simply isn't possible manually. That allows teams to spend less time managing rules and more time focusing on strategy, creativity and delivering better player experiences.

3. How is your company using AI today?

At Golden Whale, AI is embedded throughout our decision intelligence platform. Our FOUNDATION™ platform continuously analyses player behaviour to predict lifetime value, identify churn risk and recommend the next best action across the player lifecycle.

Rather than replacing existing CRM or operational systems, we integrate with them to enhance the decisions they make. Operators can optimise bonus allocation, personalise player engagement and improve retention without introducing additional operational complexity.

The measurable benefits are significant. Operators can improve the precision of player engagement, reduce unnecessary promotional spend and increase operational efficiency by replacing manual, rule-based processes with continuously learning AI models. The result is better commercial performance through smarter, more consistent decision-making.

4. Has AI influenced your company's product or business strategy?

AI isn't just influencing our strategy. It defines it.

Our focus is helping operators move beyond campaign-driven CRM by introducing an AI Decision Layer that continuously evaluates player behaviour and recommends the most effective action in real time. Instead of relying on fixed rules or manual segmentation, operators can make dynamic decisions based on how players are behaving at any given moment.

This approach has shaped products such as FOUNDATION™, our machine learning operations platform, and BONUS PILOT, which helps operators optimise incentive strategies using predictive AI. Looking ahead, we'll continue expanding our decision intelligence capabilities to help operators automate increasingly complex decisions while maintaining transparency and control over how AI is applied.

5. Looking ahead, what role do you see AI playing in the future of iGaming?

Over the next three to five years, I believe AI will become a standard part of every operator's technology stack. The conversation will shift from whether to adopt AI to how effectively it's being used to support commercial decision-making.

The biggest opportunity lies in continuous optimisation. Instead of reacting to player behaviour after the fact, operators will increasingly use AI to predict outcomes and make proactive decisions across acquisition, engagement, retention and player value.

At the same time, human oversight will remain essential. AI should enhance expertise, not replace it. The operators that gain the greatest competitive advantage will be those that combine trusted AI models with experienced teams capable of interpreting insights and making informed strategic decisions. That's where we believe the future of AI in iGaming lies.

Volodymyr Zakhovaiko, Tech Lead, Blurify

1. Please introduce yourself.

I'm Volodymyr Zakhovaiko, Tech Lead at Blurify. I oversee the technical direction of projects across the company, ensuring our architecture, engineering standards and delivery processes support the products we build.

A significant part of my role focuses on integrating AI into our development workflows. That includes configuring AI agents and their instructions, designing agent workflows, implementing automated code reviews and introducing AI-assisted processes that improve the security, quality and consistency of our software. For us, AI isn't simply another development tool. It's becoming an integral part of how we build, review, test and deliver software.

2. AI has evolved incredibly quickly over the past few years. Has there been a moment, use case or capability that genuinely surprised you or changed the way you think about artificial intelligence in business?

What changed my perspective most was how quickly AI could turn ideas into working proof of concepts. It has significantly shortened the path from initial concept to an MVP, allowing us to validate technical approaches much earlier in the development process. That faster iteration benefits both technical and business teams, giving them a clearer understanding of the product, its objectives and actual requirements before significant time and resources have been invested.

In the past, evaluating new ideas often involved a considerable amount of manual work. Today, AI helps us test assumptions more efficiently, refine concepts sooner and make better-informed product decisions.

3. How is your company using AI today?

We actively use tools such as Claude, Codex and GitHub Copilot throughout our development workflows. AI assists with everything from code generation and implementation reviews to validating implementations, helping developers work more efficiently. We also deploy open-source LLMs such as Gemma 4 and GPT-OSS on clients' own infrastructure, ensuring they retain full control over their data while benefiting from AI-assisted development. It also supports our QA engineers by making it easier to carry out broader end-to-end testing.

One of the most interesting developments for us has been AI-assisted QA. We've configured QA agents that can navigate our products in a Chrome browser, test user journeys and identify bugs automatically. Those issues can then be passed directly to implementation agents, creating a more automated workflow between testing, issue detection and development.

Beyond engineering, AI also supports research, technical discovery, documentation, internal communication and early product planning. Alongside this, we use the n8n automation platform to connect applications and services, automating repetitive processes to accelerate workflows and reduce the risk of manual errors. It shortens the gap between an initial idea and meaningful validation, reducing repetitive tasks and allowing both business and technical teams to spend more time on strategy, architecture and solving complex problems.

4. How has AI influenced your company’s product or business strategy?

AI has had a significant influence on both our product strategy and the way we design software. That thinking is reflected in Openora, our newly launched open AI-native gaming framework, which helps iGaming operators build new capabilities, modernise existing platforms and migrate at their own pace. It was designed not only to accelerate development, but also to reflect the practical realities of modern software development.

As AI becomes more widely used in development, the challenge is no longer simply generating code. Teams also need to ensure AI agents understand the project context and don't make incorrect assumptions. That's why we've focused heavily on improving agent visibility within Openora by introducing multiple deterministic guard layers that give tools such as Claude and Codex a much clearer understanding of the project's structure, objectives and development context.

5. The result is a framework that doesn't just make AI-assisted development faster, but also more reliable, predictable and practical for engineering teams working in production environments.

Looking ahead, what role do you see AI playing in the future of iGaming?

I believe AI will become a core part of how iGaming products are built, tested, operated and continuously improved. Over the next three to five years, its influence will extend across product development, integrations, quality assurance, platform operations, compliance, fraud prevention, personalisation and customer support. But the biggest opportunity lies beyond simply adding AI-powered features to existing platforms.

The real shift will come from rethinking how gaming infrastructure is designed. Most platforms in use today weren't built for AI-native development. They were created for an era where developers were solely responsible for interpreting documentation, understanding architecture and implementing changes. Going forward, platforms will need to be more modular, transparent and understandable for both developers and AI agents.

For operators, that means the potential to innovate more quickly, maintain greater control over their technology stack and reduce reliance on closed systems or slow platform evolution. AI will undoubtedly accelerate software development, but platforms themselves need to be designed to support that future.

Liam Hoofe, Content Strategist for GameOn

1. Please introduce yourself

I’m Liam Hoofe, Content Strategist for GameOn and lead writer for GO Intel. In my current role at GameOn, I help oversee AI training and development in the company, running regular workshops to ensure everyone is up to speed with the latest updates and developments.

Thanks to GO Intel, AI plays a huge role in my work with GameOn. I am also a huge AI enthusiast who has championed its role within the company, and who is constantly looking for new and interesting ways to use the tools.

2. Has there been a moment, use case or capability that genuinely surprised you or changed the way you think about AI in business?

I think it’s more a series of ongoing discoveries than one particular lightbulb moment. AI is constantly changing, with there always being something new to get to grips with and new ways it can be used to enhance a business.

Over the last year, a month has not gone by without me being impressed by some sort of AI update and its capabilities, whether it’s the introduction of ChatGPT Agents, Gemini’s latest updates, or Claude Cowork. Every tool has its own way of helping a business, and I am constantly experimenting across a range of platforms and tools to find new ways to help GameOn.

I think in PR and marketing, there is huge potential for how AI can be used. Its research capabilities have played a key role in the development of GO Intel, and it has been fascinating to watch these develop over the last 12 months.

3. How is your company using AI today?

The biggest example of how we are using AI is GO Intel. With GO Intel, we use AI-assisted research to produce monthly market intelligence reports and deep-dive research projects on the biggest questions challenging the industry.

Alongside this, I run regular AI training sessions to ensure the team is up to date with all of the latest AI tools and developments. I encourage everyone at GameOn to identify use cases and discover ways AI can enhance their workflows and boost productivity.

As a result, we have built multiple tools and agents to automate and speed up daily tasks. This has not only boosted efficiency, but has also enabled us to offer a more complete service.

4. Has AI influenced your company's product or business strategy?

Yes. Go Intel itself is an excellent example of how AI has shaped our services and products. We have also utilised AI to help our clients identify gaps in their marketing strategies, and to keep track of how other companies in the business are operating, giving them a clearer competitive picture than what any sort of manual tracking was able to previously deliver.

As a company, we have sought to embrace AI and to really look for ways it can help the business. We don’t want to just use it to save time, we want to find ways that it can genuinely improve the quality and the depth of the services we deliver.

5. Looking ahead, what role do you see AI playing in the future of iGaming?

From a PR and marketing perspective, I suspect content will be where we see the biggest impact over the next couple of years. We have already seen a lot of companies using AI for their content output, whether it’s writing, graphic design, or video. This is only going to accelerate as the technology improves and it becomes increasingly difficult to tell the difference.

Of course, this will have a hugely negative impact on jobs and, in my opinion, output quality. You can expect to see more and more companies sounding and looking the same, especially as data delivers the same results to everyone.

On the player side, I expect AI to have a considerable impact on personalisation and responsible gambling. AI-driven behavioural monitoring has the potential to help operators detect early indicators of harm and improve the overall player experience from a safety perspective.

When it comes to personalisation, I expect to operators tailor everything from game recommendations to communications and bonus offers.

Finally, I suspect we are going to see some fairly intense debates heat up in the industry over AI use. Compliance always lags behind innovation, and given how tightly monitored the industry is as a whole, I can see a lot of companies becoming frustrated if they are being held back in any way.

Patrick Eriksen, Head of Marketing at Kiron Interactive

1. Please introduce yourself. Tell us about your role, your company, and how AI fits into your day-to-day responsibilities.

My name is Patrick Eriksen and I head up the marketing team at Kiron Interactive, a virtual sports provider. I manage our team that handles everything from brand, product marketing and PR to events, customer marketing and sales support.

Before joining Kiron, I worked for a data and AI firm and have a background in software development, so I’m comfortable using technology to solve unique business challenges. AI fits, to some degree at least, into every part of my role nowadays. Whether that’s analysing data, prototyping ideas, producing reports, there’s very little I’ll do without at least getting an AI’s opinion/insight on. I’m essentially a marketer with a technical background who refuses to accept that there isn’t a smarter way to work.

2. AI has evolved incredibly quickly over the past few years. Has there been a moment, use case or capability that genuinely surprised you or changed the way you think about artificial intelligence in business?

The biggest shift for us over the last year has been to start using AI to build the tools we need, rather than treating AI as the tool itself.

We’ve used AI to replace a lot of our existing marketing and sales tools, and to build some entirely new applications from scratch. It’s shifted the way we think about work in general. As a marketing team, we’ve learned not to accept mediocre processes and tools. We don’t waste time using tedious or badly designed processes/software anymore. If something is taking longer than it should to do, we rebuild it in a way that fits our preferred way of working.

If a marketing process becomes a bottleneck, we look to re-engineer it in a way that fits our preferred way of working.

3. How is your company using AI today? Which AI tools, platforms or technologies are you actively using, and what measurable benefits have they delivered for your business, customers or internal operations?

I’ll speak at a high level about some of the internal tooling we’ve developed.

We’ve built tools across the full span of what my team and the sales teams touch. We’ve built a proposal and contract generator, a regulatory radar that watches news across jurisdictions, a go-to-market kit builder, a PR and events planner and plenty more. Each one replaced something that used to be an inbox or a spreadsheet.

But the real power is when these tools start talking to each other.

Our Game Coverage tool tells us which games are performing where. Our Co-Marketing tool tells us what’s happening in-store (right down to which posters are on the wall). Our Certifications Tracker tells us where we’re licensed to offer what. And our Events Tracker tells us that the CEO of Company X is going to be at an industry event next month.

So, by the time we meet with that CEO, we know the angle, we know what’s performing, we know what we’re licensed to offer and we know exactly what evidence to put in front of them.

And the important bit is that this doesn’t just work for one target account. It lets us do that across 50+ prospective customers at a time.

4. Has AI influenced your company's product or business strategy? Can you share any AI-powered products, features or initiatives you've launched recently, or are planning to introduce over the coming months?

I’m excited to roll out a new onboarding and KYC process that moves us away from the one thing I hate most: email.

Onboarding is such an important touchpoint for marketing. It’s the first real experience a customer has of truly working with Kiron. I want that to feel easy from the first click.

Strategically, there’s a line from Tom Bedor that I think about often: “If you are requesting human attention, demonstrate human effort.”

That principle has influenced a lot of what we put out as a team. AI-generated stuff absolutely has a place. But if we’re asking someone (a customer, a prospect, a colleague, even you reading this) to spend their time reading something, the least we can do is make sure it feels like we spent time writing it.

5. Looking ahead, what role do you see AI playing in the future of iGaming? How do you expect artificial intelligence to transform the industry over the next three to five years, and where do you believe the biggest opportunities still lie?

I love what we’ve been able to do with AI, but I think the gaming industry would be naive to treat it as just another productivity tool. It’s going to shake the foundations of any business whose main advantage has simply been “being able to build the thing.”

I might get some heat for this… but I don’t think there’s anything fundamentally very complicated about building most casino games. 3 years in AI development time is an eternity. Three years ago, Claude had only just been launched, and now it’s visualising proteins. In 3 years time, AI’s going to be able to perfectly recreate just about any game you show it, in the time it takes you to read this article.

And I don’t think that’ll only apply to slots. Nothing’s safe. Not virtuals, not platforms, not marketing tools (obviously), not even live dealer. If your moat is just “we can build this and other people can’t”, that moat is going to get very shallow very quickly.

Certification, regulation and IP protection will still matter, of course. They’ll still stop some people from becoming casino suppliers overnight. But this industry already has no shortage of copycat games. AI is just going to make them even easier to produce, while also accelerating code annotation, compliance preparation and the novel maths and mechanics behind the games.

So for me, the biggest opportunity isn’t AI at all. The AI part is becoming table stakes. The real opportunity is everything AI can’t do.

AI might create a product. Help you build a better deck. Write a perfect email.

But a perfect email is still just an email. None of that beats the way humans make other humans feel.

That’s the part I think companies need to take seriously.

Invest in the tools, sure. But invest just as hard in the people around them.

The people who make dealing with your business feel lekker. The people who aren’t afraid to pick up the phone. The people who can have the awkward conversations. The people who understand that AI can help them do the work, but can’t be accountable for it.

Because when the tools become a given, the people become the difference.

Adam Lewis, Chief Executive Officer at AxiumAI

1. Please introduce yourself. Tell us about your role, your company, and how AI fits into your day-to-day responsibilities.**

I'm Adam Lewis, Co-Founder and CEO of AxiumAI.

At AxiumAI, we're building the proprietary AI Operating Layer for autonomous, real-time player engagement. Our platform continuously understands every sporting moment, every player and every commercial objective before autonomously deciding, creating and executing personalised interactions across every customer touchpoint.

After more than 25 years in the industry, I've come to believe that the biggest challenge facing operators isn't data, content or marketing, it's intelligence. Every game creates thousands of engagement opportunities, yet today's teams and technology simply can't respond at that speed or scale.

AI changes that. It transforms player engagement from a manual process with player clusters, into autonomous 1:1 engagement that continuously understands, decides, creates and acts in real time. The operators that embrace this shift won't just engage players more effectively, they'll build sustainable competitive advantage for the next decade.

2. AI has evolved incredibly quickly over the past few years. Has there been a moment, use case or capability that genuinely surprised you or changed the way you think about artificial intelligence in business?

The moment that changed my thinking wasn't when AI started writing, it was when AI became capable of making contextual decisions.

That changes AI from being a productivity tool into an operating capability. Instead of simply helping people create content or analyse data, AI can now continuously understand what's happening, decide what should happen next and execute those decisions autonomously.

I don't think the long-term impact of AI is automation alone. It's the emergence of intelligence as an operating capability. Every organisation already has data. Most are investing in automation. The next differentiator will be how effectively businesses can continuously understand, decide and act in real time.

3. How is your company using AI today? Which AI tools, platforms or technologies are you actively using, and what measurable benefits have they delivered for your business, customers or internal operations?

Within AxiumAI, we use AI across software engineering, product development, customer delivery, commercial strategy and day-to-day operations. It enables our team to move faster, iterate more quickly and focus more of our time on solving high-value problems rather than repetitive tasks.

More importantly, AI sits at the core of our platform. Rather than simply generating content, our technology continuously analyses live sporting events, player behaviour and commercial objectives before deciding what the next best interaction should be for every individual player. That allows operators to deliver millions of relevant, real-time experiences at a scale that simply isn't achievable through traditional operating models.

The measurable benefit isn't just greater efficiency, it's a fundamentally different way of operating. AI allows a relatively small team like ours to build enterprise-grade capabilities, while enabling our customers to engage players with a level of speed, relevance and personalisation that wasn't previously possible.

4. Has AI influenced your company's product or business strategy? Can you share any AI-powered products, features or initiatives you've launched recently, or are planning to introduce over the coming months?

AI hasn't changed our strategy. It is our strategy.

From day one, we've believed AI shouldn't be another feature bolted onto existing sportsbook technology. It should become the intelligence layer that continuously understands what's happening, decides what to do next and acts autonomously in real time.

That conviction has shaped every product decision we've made. Rather than building individual AI use cases, we're building a single AI Operating Layer that powers player engagement across sportsbook, casino and player acquisition. Every new capability strengthens that intelligence layer rather than existing in isolation.

The reason is simple. We believe AI fundamentally changes the economics of customer engagement. For the first time, operators can deliver intelligent, real-time, one-to-one experiences for millions of players simultaneously, something that has never been commercially viable through people, rules or traditional automation alone.

Over the coming months, you'll see that capability expand into new channels, products and markets. But the strategy remains the same: building intelligence as a core operating capability, not simply another application of AI.

5. Looking ahead, what role do you see AI playing in the future of iGaming? How do you expect artificial intelligence to transform the industry over the next three to five years, and where do you believe the biggest opportunities still lie?

I think we're still in the very early stages of AI adoption. Over the next three to five years, the biggest change won't be that operators have more AI features. It will be that AI becomes the intelligence layer behind every commercial decision and every customer interaction.

Today's engagement model is still largely campaign-led and manually orchestrated. That simply won't keep pace with modern consumer expectations. The future will be continuous, intelligent and adaptive, with AI constantly understanding context, deciding the next best action and responding in real time across every customer touchpoint.

More importantly, AI changes the economics of engagement. Delivering genuinely personalised experiences has traditionally required significant teams, complex workflows and substantial marketing spend. AI makes intelligent, one-to-one engagement commercially viable at a scale that simply wasn't possible before.

Ultimately, I think the industry's competitive advantage will shift. Over the last decade, data and spend became a strategic asset. Over the next decade, it will be intelligence: the ability to continuously understand, decide and act in real time. The operators that embed that capability into how they run their business will be the ones that define the next generation of iGaming.