AI in iGaming: Jaime Ocampo on Turning AI into Better Decisions

AI has quickly become one of the most discussed technologies in iGaming. The more important question for operators, however, is how it can be applied in practice to create measurable commercial value.

In a recent iGaming News industry feature, AI in iGaming: How iGaming Leaders Are Actually Using AI, Golden Whale Managing Director, Asia, Jaime Ocampo shares his perspective on how artificial intelligence is evolving from an industry talking point into a practical decision-making capability.

For Jaime, one of the most significant developments has been the shift from using AI primarily to analyse historical information towards making intelligent recommendations in real time. This enables operators to evaluate complex player behaviour and determine the next best action at an individual level, rather than relying solely on predefined segments, fixed rules and campaign-driven CRM.

AI-Powered Decision Intelligence Across the Player Lifecycle

At Golden Whale, AI sits at the core of how we help operators improve decision-making across player engagement, retention and incentive strategies.

Through FOUNDATION™, player behaviour can be continuously analysed to predict lifetime value, identify churn risk and support next-best-action decisioning across the player lifecycle.

Importantly, this intelligence does not require operators to replace their existing CRM and operational technology. Golden Whale integrates with existing systems to enhance the decisions they make, helping teams optimise engagement and incentive allocation while reducing reliance on manual, rule-based processes.

The objective is not automation for its own sake. It is to enable better and more consistent decisions at a scale that would be difficult to achieve manually.

Continuous Optimisation with Human Oversight

Looking ahead, Jaime expects AI to become a standard component of the operator technology stack over the next three to five years.

As adoption increases, the conversation is likely to shift from whether operators should use AI towards how effectively it supports commercial decision-making.

Continuous optimisation will play an important role in that evolution. Rather than reacting to player behaviour after the fact, operators will increasingly be able to use predictive intelligence to inform decisions across acquisition, engagement, retention and player value.

Human expertise, however, remains essential. AI should support experienced teams with greater precision and intelligence, while operators retain oversight and control over how those decisions are applied.