By Ulises Gil, journalist at G&M News.
What are the biggest misconceptions the industry still has about AI? How can companies overcome fear or hesitation when it comes to adopting these technologies?
It depends who you speak to. Some people outside the industry believe that the gambling industry is very sophisticated in its use of data and analytical techniques, in how it targets consumers and generates revenues from them. Much of this was influenced by casino leaders in the early 2000’s, such as Caesars CEO Gary Loveman, a former Harvard Economics Professor, who was renowned for adopting sophisticated, data-driven techniques to maximize revenues from Caesars’ Rewards loyalty program. Others, who know the industry well, believe that the industry is far behind big tech when it comes to maximizing the use of data from techniques like AI. I would suggest the truth is somewhere in the middle, with very sophisticated AI-applications in areas like RegTech, but also the industry is still relying on traditional forms of acquisition and retention that are not technology sophisticated e.g., bonusing.
Artificial Intelligence Research Hub, AiR Hub, which has recently launched, has the potential to drive innovation in the gambling space. Can you share a project that you’re particularly excited about?
I’m very excited about our flagship report, The State of AI in Gaming. The report will offer a comprehensive overview of how AI is shaping the global gaming industry. It will survey key innovations, user cases, and regulatory developments across the gaming value chain, from B2B providers and sports betting to startups and RegTech platforms. The report will identify areas of value creation opportunities and emerging risks tied to evolving AI technologies and governance frameworks. A key feature of the report will be a novel benchmarking framework to systematically evaluate AI maturity and capability across industry stakeholders. This “index” will serve as the foundational reference point for measuring both innovation leadership and responsible AI adoption for the gambling sector. I strongly believe that this report will become the ‘go-to’ resource for anyone wishing to know how AI is being adopted in the gaming industry and which organizations are leading in this space.
How important are events that focus on AI, such as G&M Events Peru 2025, for the future of the gaming sector in Latin America? What role do these gatherings play in accelerating adoption and education?
Industry events are very important as they allow people across different sectors of gaming and the wider tech industry to come together and discuss and debate how AI is being adopted, the challenges and the opportunities. The industry is very relationship-driven, so if you are working in an area that has an AI focus and you want the industry to take a serious look at what you are doing, then you can’t do that from your office desk. You have to go out there and showcase your work and build those relationships. The industry is very generous. Operators openly talk about their challenges and opportunities, and will listen to you if you have solutions that can help them.
What’s your vision for the integration of AI in gaming over the next 3 to 5 years? What should industry stakeholders be ready for?
There is a lot of buzz around GenAI at the moment and I’m interested to see how quickly the industry generates efficiencies and value in a variety of use cases, from personalized content in sports betting, product development in slots, to some of the more operational use cases like HR, legal and IT. Customer support has clear potential, but there are some sensitive edge-cases that will need to be solved. AiR Hub‘s Sports Betting Education with LLMs project is looking to improve conversational AI exploring how LLMs respond to problem gambling and whether they can effectively support novice bettors. As the industry relies more and more on AI, regulatory scrutiny will also increase, so the industry needs frameworks to build trust with stakeholders and show how some AI applications are working (e.g., building trust in systems that detect at-risk play) and also to demonstrate AI is not causing harms. I’m also interested to see how Agentic AI evolves and whether it can turbo-charge some areas of AI that appear to have plateaued in productivity in the past few years, such as in using AI to detect at-risk play.