With player expectations rising and product complexity increasing, traditional menu-driven experiences are starting to show their limits. AI promises a fundamental shift from static journeys to intelligent, responsive and personalised interactions.
In this latest analysis, Adam Lewis, Chief Executive Officer at AxiumAI, and Katja Ulbrich, Director of Digital Product at OpenBet, discuss where AI is already delivering tangible gains, what is holding back wider adoption, and how operators and suppliers can work together to make AI-led UX a core operational capability rather than a side experiment.
Where do you see AI delivering the most immediate improvements to sportsbook UX in 2026?
Katja Ulbrich: The most immediate UX gains are coming from speed, quality assurance and hyper-personalisation. AI is already acting as a co-creation partner within product and UX teams, accelerating research, prototyping and design structures. Tasks such as design system audits, Web Content Accessibility Guidelines (WCAG) compliance checks or structural reviews can now be handled by AI agents in real time, dramatically shortening delivery cycles.
For operators and their bettors, this translates into faster UX, more consistent interfaces and increasingly tailored journeys. Hyper-personalised content, bet suggestions and navigation paths will become more precise and dynamic.
Further into the future, ‘no UI’ interactions via platforms such as ChatGPT, Gemini and Claude may start to complement traditional interfaces to enhance the efficiency of making bets. This will be for a small subset of bettors, as we still expect that casual bettors may still prefer a traditional interface for their entertainment-driven experience.
Adam Lewis: The most immediate improvements will come from changing how players interact with sportsbooks. Today, most sportsbooks still rely on the same inert, menu-driven UX they have always used.
As more sports, markets, casino products, and games compete for attention, these interfaces have become increasingly confused, making the experience feel irrelevant, static and difficult to navigate.
AI is being used to change this completely, shifting from a passive experience to an interactive, two-way conversation, one that builds confidence, encourages exploration, expands spend, and creates a deeper sense of brand immersion.
Applied AI enables a far more dynamic, personalised, and compelling UX. Rather than forcing players to navigate static menus and raw statistics, the next generation of sportsbook UX will deliver relevance alongside insight and opportunity in real time, in a way modern audiences can naturally engage with.
Instead of passive browsing and guesswork, players will be able to interact with the sportsbook much like they would with a human, by asking for insight on live games, exploring the most likely outcomes and understanding why, discovering which promotions are live today, requesting bet builder leg suggestions, and uncovering opportunities based on context, preference, and live action.
The UX being developed today is transforming sportsbooks from static platforms into intelligent, conversational experiences, opening up an entirely new level of engagement for a modern, global audience.
The fastest gains in 2026 will not come from expanding into more markets or offering better menus, but from AI that understands intent, reacts to live context, and inspires players to transact with confidence and excitement. AI will make sportsbooks feel less like retail experiences and more like knowledgeable, real-time companions.
How do you expect the relationship between operators and suppliers to evolve as AI becomes embedded in live sportsbook operations?
AL: As AI becomes embedded in live sportsbook operations, the relationship between operators, suppliers, and end-consumers will rebalance around intelligence rather than tooling.
Operators will move from orchestrating static systems to owning intelligent, autonomous engagement loops that operate in real time. The focus will shift from campaign execution and manual optimisation to defining strategy, guardrails, and outcomes, while AI handles moment-to-moment engagement at scale.
Platform and game suppliers will evolve from feature providers into capability partners. The most valuable will not simply ship platforms or content, but embed AI that actively drives discovery, engagement, and monetisation in live environments, integrates cleanly into operator ecosystems, and improves continuously through player behaviour and performance feedback.
For end-consumers, the change will be obvious. Sportsbooks will feel less transactional and more interactive, moving away from passive menus and towards experiences that respond, explain, and guide in real time. Engagement will become contextual, personalised, and confidence-building, rather than generic and promotional.
The result is a tighter loop. Operators set intent, suppliers deliver applied intelligence, and consumers engage through more natural, responsive experiences. AI becomes the connective tissue that aligns all three around real-time value creation rather than simple distribution. So, in short, AI is what will transform the ecosystem from a linear supply chain into a living, learning engagement system.
KU: The operator-supplier relationship will continue shifting from a transactional feature delivery to outcome-driven collaboration. Genuine AI, particularly agentic systems and LLM-powered workflows, requires robust data pipelines, governance and domain expertise. It is unlikely operators will want to replicate that infrastructure independently when suppliers can provide integrated capabilities.
We position ourselves as a strategic partner and delivery engine embedded in our clients’ operations. Therefore, AI must be integrated across trading, risk, UX and responsible gaming as a core operational layer.
Our proposition is built around measurable outcomes: margin optimisation, player protection and operational efficiency. We provide scalable, compliant ecosystems and hold ourselves accountable to the KPIs that matter to our clients, deliberately moving away from the marketing-led, ‘AI-washed’ solutions.
What internal changes do operators need to make to move AI-led UX from pilot projects to everyday use?
KU: Both operators and suppliers must reassess whether existing processes are still fit for purpose in a faster, AI-augmented environment. This includes establishing clear governance, defined KPIs and safeguarding frameworks to ensure outputs are measurable and compliant.
Crucially, AI cannot operate in isolation. Operators need skilled experts who understand sportsbook mechanics, user behaviour and desired outcomes to guide and validate AI outputs. Human-in-the-loop models remain essential to maintain quality and accountability.
Investment in training, cross-functional collaboration between product, UX and trading, and a paradigm shift towards faster prototyping and iterative development will distinguish enterprise adoption from experimentation.
AL: To move AI-led UX from pilots into everyday use, Sportsbooks need to change how they operate, not just what they build.
AI must be treated as core product infrastructure, with clear ownership inside product and commercial teams and accountability tied to live outcomes, not experimentation. When AI sits on the edge of the organisation, it never meaningfully shapes the experience.
On the other hand, operators also need to move away from fixed journeys and static templates. AI works best when teams define outcomes and guardrails, then allow systems to adapt interactions in real time based on context and player behaviour.
Progress comes from deploying one high-impact use case into production, learning fast, and scaling from there. AI-led UX is never ‘finished’ – it improves continuously through live feedback, and operators that embrace this shift are the ones that move beyond pilots to achieving real-world value at scale.
What will differentiate operators who successfully evolve sportsbook UX with AI from those who do not?
KU: Successful operators will combine AI-driven speed with strong human-in-the-loop expertise and clear outcomes goals. AI dramatically increases production capacity, but without strategic direction, it can just as quickly scale poor experiences.
Operators who succeed will embed AI with a quality framework including clear objectives, measurable performance indicators and expert oversight. They will balance automation with entertainment value, recognising that sportsbook is fundamentally an experience and keeping the player journey at the centre of every decision. Those who treat AI as a long-term operating model, as opposed to a race to be first, will create more resilient UX ecosystems.
AL: By 2026, the operators that successfully evolve with AI will be differentiated by how they make decisions and execute, not by who has access to the best technology.
First, successful operators will demonstrate clear C-suite intent through action, not statements. AI initiatives will have named executive ownership, defined budgets and KPIs tied directly to live commercial outcomes, with AI embedded within core product, marketing or operations teams rather than treated as side projects or innovation lab experiments.
Second, they will act despite already having full technology priority lists. Winning operators will recognise that a busy roadmap cannot be an excuse for inaction. They will deliberately carve out space for one AI use case that can be deployed quickly, run in parallel and deliver measurable value without waiting for major platform changes.
Third, they will be able to start quickly. Rather than waiting for perfect data, platforms or long-term strategies, they will select one high-impact use case and deploy it into live operations within weeks. Progress will be measured through real customer behaviour and revenue impact, not internal milestones.
Fourth, they will actively retire legacy ways of working. By reducing or removing manual processes, heavy approval chains and campaign-led execution, operators will allow AI systems to operate with speed and autonomy inside clear guardrails, with teams shifting from execution to supervision, tuning and scale.
The operators that win will operationalise AI, not experiment with it. They will measure it relentlessly, scale what works, shut down what does not, and move on quickly from legacy processes once AI proves it can deliver better outcomes.
