Artificial intelligence has quickly become one of the most widely discussed — yet often vaguely defined — technologies within the casino gaming sector.

Across the supplier ecosystem, AI is increasingly being positioned as a catalyst for change. From game development and personalised player experiences to operational optimisation and deeper player insights, the technology is frequently presented as a transformative force.

However, as interest in AI grows, so does the need to distinguish genuine innovation from marketing buzzwords. With insights from industry professionals, this discussion explores how AI is currently being used in practical, real-world scenarios, where it delivers measurable benefits for operators, and where expectations may still exceed reality.

Rather than focusing on speculative possibilities, the conversation centres on deployable solutions that are already in use today, the commercial and regulatory factors influencing adoption, and the responsibility suppliers have when communicating the capabilities of AI-driven technologies.

As operators become more selective when evaluating AI-based solutions, the discussion also looks ahead to the next 12–24 months, examining which AI applications may soon become essential for casino suppliers and what meaningful success with AI in gaming could realistically look like.

What Actually Counts as AI in Casino Gaming?

Artificial intelligence is now embedded throughout industry marketing, but the term is often applied broadly. In practical terms, what qualifies as a genuine AI application in casino gaming today — and where might the label be stretched too far?

According to Andreas Koeberl, CEO at BetGames, the industry still has considerable ground to cover.

He notes that while AI was frequently mentioned at industry events such as ICE, there were relatively few genuinely new developments presented. In comparison to other areas of gaming technology outside the iGaming sector, the casino industry remains behind the curve.

Koeberl points out that BetGames’ Casino Designer platform stood out as one of the few solutions showcasing meaningful AI-driven functionality. While some AI elements are already used during game development processes, broader innovation within the sector remains limited.

Sam Depoortere, Chief Product Officer at OpenBet, highlights that AI has already moved beyond theoretical discussions in sports betting and is now supporting real operational processes.

However, he also notes that the term is sometimes applied too loosely. Traditional machine learning models are often described as AI, even though true AI involves more advanced capabilities such as reasoning systems, autonomous task execution, and natural language processing — particularly through large language models and agent-based frameworks.

At OpenBet, this includes tools such as generative responsible gaming assistants and workflow automation systems that provide contextual insights and more intelligent decision-making support.

Meanwhile, Bjørnar Heggernes, Chief Commercial Officer at The Mill Adventure, believes AI provides the greatest value when it eliminates repetitive or time-consuming tasks.

By reducing procedural work, teams can focus on more meaningful activities such as creativity, strategic thinking, and deeper analysis. AI also enables operators to better interpret the large volumes of performance data they collect.

However, Heggernes cautions that AI is sometimes overstated when it comes to game design and content production. Producing more games faster does not necessarily translate into higher player engagement, and marketing efforts alone cannot guarantee growth.

Practical AI Tools Already Used by Operators

Many AI-driven solutions that deliver tangible benefits are already being used today, particularly in backend systems.

Koeberl explains that the most practical AI tools currently focus on CRM systems, player segmentation, behavioural analytics, and automated decision-making processes. These technologies help operators personalise player engagement, refine retention strategies, and streamline internal workflows.

While experimental front-end concepts such as AI-generated presenters have been explored, real-world testing has shown limited commercial impact so far.

Depoortere also points to a range of AI solutions already available to operators. For example, OpenBet’s Neccton platform uses machine learning to analyse player behaviour in real time and identify potential risk indicators related to responsible gaming.

The latest version incorporates generative AI features that enhance analysis capabilities and provide support teams with clearer, more actionable insights.

AI is also improving trading accuracy, strengthening pricing models, and enhancing risk management processes in sports betting environments.

Beyond player-facing applications, AI is increasingly used internally to improve reporting, accelerate product development, and simplify operational processes.

Heggernes adds that one of the most widely adopted AI applications today is actually content creation. Tools such as ChatGPT, Claude, and Copilot allow teams to generate ideas, structure content, and optimise messaging far more efficiently.

These tools have become integrated into everyday workflows across many organisations, though he stresses that proper training is necessary to ensure they are used effectively.

Additional areas seeing meaningful progress include AI-powered customer support tools, business intelligence platforms capable of generating reports via prompts, and AI-assisted design tools for creating websites and product concepts.

Where AI Is Delivering Real Value

When evaluating the value of AI, operators are increasingly focusing on measurable outcomes such as operational efficiency, scalability, player engagement, and content optimisation.

Heggernes highlights the example of SmartLobbies, a system that automates casino lobby management while delivering personalised game recommendations to players.

Instead of manually managing game placement each day, the system dynamically promotes relevant titles based on behavioural data and performance metrics.

This approach reduces operational workload for casino teams while helping players discover games that better match their preferences. The result is longer playing sessions, improved game visibility, and more efficient content management.

Depoortere notes that AI is also improving efficiency across multiple operational areas. Automation reduces the need for manual reporting and routine data analysis, allowing specialists to focus on strategic work.

AI technologies are also helping operators scale operations through shared APIs that integrate trading, risk management, and compliance processes.

Responsible gaming tools powered by AI can identify potentially harmful player behaviour earlier, improving regulatory outcomes while supporting long-term customer retention.

Koeberl adds that the clearest benefits of AI currently lie in operational optimisation. Machine learning models help analyse large datasets more quickly, enabling predictive insights and improved decision-making.

Automation can also reduce manual workloads across marketing, production, and back-office processes, allowing teams to increase output without expanding headcount.

However, he emphasises that successful AI adoption requires clear objectives. Many AI projects fail because organisations attempt to implement the technology without clearly defining the problems they want to solve.

Ensuring Responsible and Transparent AI Use

As AI enables greater automation and personalisation, maintaining transparency and regulatory compliance remains a critical priority.

Heggernes stresses that any AI solution must operate within strict regulatory frameworks. AI can support responsible gaming initiatives by identifying risk patterns, monitoring player behaviour, and detecting potential fraud.

However, automated systems should always complement — not replace — human oversight. Clear escalation processes, manual review procedures, and transparent reporting remain essential components of responsible AI deployment.

Depoortere agrees that governance and oversight are key. AI systems must operate within structured governance frameworks that ensure accountability and transparency.

OpenBet has established a multidisciplinary AI ethics committee responsible for reviewing model behaviour, compliance considerations, and potential operational risks.

Koeberl also notes that organisations must fully understand their data flows and operational safeguards before deploying AI technologies.

While AI models can sometimes operate as “black boxes,” human decision-makers must always retain ultimate control.

What Will Define the Next Stage of AI in Casino Gaming?

Looking ahead, several AI applications are likely to move from competitive advantages to baseline expectations across the industry.

Koeberl identifies two major areas of development: increased automation within game production processes and the emergence of entirely new game formats that rely on AI technologies.

Depoortere predicts that AI-driven player protection tools and automated trading adjustments will soon become standard features for casino suppliers.

Once these capabilities become widespread, the key differentiator will be the depth of integration within supplier ecosystems.

Rather than offering isolated AI tools, leading suppliers will create interconnected systems where trading, risk management, compliance, and product development operate within a unified AI framework.

Heggernes believes that the ability to translate complex data into clear insights will become increasingly important.

As AI-powered analytics become standard, suppliers who can turn large volumes of data into personalised, actionable recommendations for operators will ultimately stand out.