Beyond the hype How OpenBet is building an AI-first operating model


As AI moves from experimentation to infrastructure, OpenBet CTO Maria Kollia explains how the company is embedding intelligent systems across trading, player protection and product development, while balancing automation, governance and responsible gambling at scale.


Where is AI delivering tangible value across OpenBet's products?


AI is already deeply embedded across several of our internal pillars, and the foundation underpinning all of it is structured, governed adoption. Early on, we made a deliberate move away from ad hoc experimentation toward a model where every AI initiative operates with defined duration, clear success criteria, and accountable teams. Tat discipline has allowed us to scale adoption at pace, across engineering, QA, support, product, commercial, and back-office functions, without sacrificing the governance required across all of our global hubs.


To give a sense of the scale, over 600 of our engineers are now working with AI tooling as part of their daily workflow, which alone represents a material shift in how we build and iterate. Beyond developer productivity, AI is delivering value in player protection, where our Neccton technology identifies at-risk behaviour in real time, and in precision trading, where AI sharpens the accuracy of our models to protect margins and enhance the player experience.


We are also using AI for operational automation: workflow processes that previously required human execution are now handled end-to-end, significantly accelerating our product development lifecycle and getting innovation into the hands of our partners faster.


OpenBet works with multiple hyperscalers including Anthropic, Google, Microsoft and AWS. What sits behind that strategy?


Our multi-provider approach is based on four very deliberate 90


factors. First, market volatility: anyone that follows the AI landscape even semi-closely can see how quickly it is fragmenting, not consolidating, and there is a new model emerging every few weeks. It is clear that no single tool will win everywhere. Second, problem diversity: different challenges across our product stack require different capabilities, and a single provider cannot optimally serve all of them. Tird, workforce diversity: our engineers have varied backgrounds and tooling preferences, and our stack reflects that.


Fourth, evaluation discipline: we assess tools at the ecosystem level, not in isolation. Te question we ask is not "is this good?" but "how does it fit our stack?" Together, these ensure that OpenBet and our partners always have access to the best available capabilities as the technology evolves, without being captive to any single vendor's roadmap.


How does that multi-partner approach help you stay flexible as the technology continues to evolve?


Our strategy six months ago looked different from today. Tat is by design; we built an approach that evolves with the market rather than one optimised for a single moment in time. We no longer evaluate tools in isolation; we think at the ecosystem level, which means we can integrate new capabilities into our core stack the moment they prove their value. We are also actively diversifying through open-source, which builds optionality and ensures we are never locked into a commercial vendor's roadmap. Te result is an infrastructure that functions as a living system, capable of pivoting quickly toward the most effective breakthroughs while maintaining the stability our partners require.


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