As AI adoption accelerates, Thomas Kolbabek explores why trust in machine learning depends on human oversight, measurable outcomes, and clearly defined decision boundaries.
As AI adoption accelerates across iGaming, more operators are evaluating whether to build machine learning capabilities in-house or partner with specialist providers. While modern tools have made AI more accessible than ever, accessibility alone does not guarantee better commercial outcomes.
In a recent editorial with GBC Time, Thomas Aigner, SVP of Business Development at Golden Whale, explores why the industry’s build-versus-buy debate is asking the wrong question.
Rather than focusing on who owns the technology, Thomas argues that operators should evaluate AI based on one simple principle: can it consistently deliver better decisions and stronger commercial outcomes than the alternatives?
The editorial examines why prediction alone does not create value, where internal AI initiatives often encounter operational challenges, and why successful optimisation depends on connecting models with the right interventions, workflows, and continuous learning processes.
Thomas also highlights the importance of external benchmarking, arguing that without a point of comparison, operators may struggle to understand whether internally developed models are truly competitive or whether opportunities for improved performance remain untapped.
Ultimately, the strongest AI strategies are rarely defined by build or buy alone. Instead, they combine internal expertise, strategic ownership, and specialist optimisation intelligence to improve retention, player value, incentive efficiency, and long-term commercial performance.
Read the full editorial on GBC Time.
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