AI is reshaping industries at speed, yet many sportsbook operators have yet to deploy it in any meaningful way. The challenge is not technology readiness; it is about execution focus. The most effective starting point is not to labour over an expansive strategy, but rather focus on a single, high-value use case that delivers measurable results quickly and lays the foundation for broader adoption.
Begin where impact is immediate
When it comes to AI, many operators feel constrained by today’s operational pressures, legacy systems or leadership uncertainty. In practice, this leads to a comfortable focus on sustaining current momentum rather than building for future growth, a position that risks seismic competitive erosion in an industry ripe for intelligent automation.
Successful AI deployment does not demand large budgets or grand strategies. Given the pace at which AI capabilities evolve, strategies will likely become outdated before they are even finalised or approved. The priority is to select one use case that drives both commercial impact and demonstrates what AI can achieve across the organisation.
Starting with a targeted approach also gives internal teams a critical runway to adapt. A focused use case allows operators to build the infrastructure, governance and cross-functional processes required for AI adoption, without the complexity and distraction of trying to scale too early. Teams gain hands-on experience with data pipelines, model monitoring, compliance requirements and operational integration. Once additional agents are introduced, internal capability has developed from experimental proof-of-concept work into an organisation ready for production-level deployment at scale.
Case example: In-play engagement at scale
In-play sports betting represents billions of dollars in annual revenue globally, yet the tools to engage players dynamically during live matches remain basic, hindering enormous growth potential. AI agents address this gap, they identify pivotal in-game moments, generate context-rich game insight and narratives and recommend bets tailored to each moment. They scale execution autonomously across every game, region and language, while retaining full operator oversight. This isn’t theoretic, it’s deploying today.
Typical deployment of such agents would be four to six weeks. Within that timeframe, the operator moves from limited manual engagement to a scalable system autonomously delivering real-time, in-play engagement, which is personalised at individual player level, materially increasing both player interaction and net gaming revenue (NGR) contribution without disrupting ongoing operations.
Once a single agent proves its value, expansion is straightforward. Additional AI agents can be integrated to drive compounding returns, such agents include:
• Autonomous content creation for pre-match, in-play and post-match marketing, enabling real-time, scalable player engagement.
• 1:1 personalisation delivering insight and propositions to match a player’s unique DNA, enhancing engagement and transactions.
• Responsible gaming through agents predicting and managing player behaviour in real time.
• Embedded player and match insights that provide the consumer with confidence and inspiration when making their betting decisions.
• Adaptive machine learning that continuously iterates and optimises content and user experience to maximise player engagement and revenues.
While each agent delivers significant standalone value, a far larger transformative impact lies in their ability to work as a connected, context-aware ecosystem. This agentic architecture enables data and intelligence to flow seamlessly between agents, scaling insight, automating decisions and unlocking compounding commercial advantage across the entire operator value chain.
Act, learn, scale
AI will drive change across the gaming industry faster than traditional strategic cycles. Success will come from strategy and execution at pace: plan, deploy, learn and accelerate.
A single, targeted use case that delivers immediate value, will in parallel build critical internal capability, and prepare management and teams to transition from proof-of-concept to full production readiness. This approach reduces risk, compresses time-to-value, and positions operators to leverage AI for enormous competitive advantage.
Adam Lewis is the co-founder and CEO of AxiumAI, a company pioneering agentic AI solutions that deliver intelligent, autonomous player engagement and unlock scalable growth across the full operator value chain.
With deep industry expertise, the former Entain MD is reshaping how operators compete, perform, and lead in a new era of AI-driven transformation.
The post The playbook for AI success first appeared on EGR Intel.
Former Entain MD and now CEO of AxiumAI, Adam Lewis simplifies how operators should implement and engage with AI to unlock scalable growth
The post The playbook for AI success first appeared on EGR Intel.