Artificial intelligence has become nearly ubiquitous across the iGaming industry, yet most deployments remain concentrated in back-office functions, while uses tied to gross gaming revenue (GGR) is still largely on the roadmap.
According to a report compiled by The Playa and NEXT.io, 79% of iGaming companies now use AI or machine learning, with 77% rating the technology as critical or very important to competitive advantage over the next two to three years.
Despite that high level of adoption, the study, which is based on a survey of 151 senior industry decision-makers, suggests that operators are still in the early stages of implementing AI investment into areas that impact commercial gains.
“Most have deployed it in areas that don’t directly impact GGR,” the report said.
AI use is most mature in operational efficiency functions such as process automation, content generation and customer support, where benefits are easier to measure and implement.
Internal process automation is fully deployed at 43% of companies, followed by content generation at 35% and customer support and chatbots at 29%, the report found.
Customer support stands out as the most effective application so far, with 32% of operators citing it as their top-performing use case.
These areas provide quick returns because performance metrics—such as reduced response times or lower support volumes—are immediately visible, analysts said.
By contrast, more advanced applications linked directly to revenue, including predictive analytics, player segmentation and personalised bonuses, remain underdeveloped.
Use cases tied to player behaviour and monetisation show significantly lower deployment rates, often below 20%, highlighting what the report calls a “readiness gap” between capability and impact.
For example, only 11% of operators have fully deployed AI in player acquisition and 14% in segmentation, while bonus personalisation and predictive analytics also lag behind.
Even where AI models exist, execution challenges persist. In churn prediction, two-thirds of operators with models in place have yet to act on the insights generated.
Similarly, while lifetime value (LTV) prediction is widely adopted among users of AI in acquisition, many companies fail to measure the financial impact of related tools such as fraud detection, limiting their ability to justify investment.
A consistent theme across the report is the difficulty operators face in assessing AI’s impact.
More than half of respondents said it was too early to judge performance across most use cases. Where companies were able to measure results, satisfaction was generally high and dissatisfaction rare.
The discrepancy reflects differences in attribution. In areas where AI outputs are direct—such as chatbot responses—results can be quickly quantified. In more complex scenarios, such as personalised offers or lobby recommendations, isolating AI’s contribution is significantly harder.
Contrary to expectations, the main barrier to AI adoption is not technology or budget, but organisational prioritisation.
Competing business priorities were cited by 41% of respondents as the top obstacle, exceeding concerns about integration complexity, skills shortages or costs.
Other barriers include lack of expertise (31%), integration complexity (34%) and regulatory or infrastructure challenges, though only 5% reported a lack of quality vendors
The findings suggest that most companies accept the value of AI but struggle to allocate the sustained focus needed to implement it at scale.
Looking ahead, the industry is expected to pivot toward more sophisticated use cases tied to player retention and lifetime value.
About 76% of respondents said they plan to expand AI usage within the next 24 months, with segmentation, predictive analytics and personalised bonuses among the top priorities.
This signals a shift from efficiency-driven deployment toward revenue optimisation and player experience.
Early evidence shows potential gains. Operators using AI-powered lobby personalisation reported gross gaming revenue increases of 3% to 9% and retention improvements of up to six percentage points.
However, such results are not yet widespread, reflecting the complexity of implementation and the need for robust data infrastructure.
The report concludes that the iGaming sector has moved beyond experimenting with AI and is now entering a phase where execution quality will determine competitive advantage.
While adoption is near universal, only a small group of operators have built the organisational structures, data pipelines and workflows required to scale AI effectively.
“The debate is no longer whether to invest in AI,” the report said. “It is who can execute.”
As the next wave of deployment focuses on player intelligence and personalisation, the gap between early movers and the rest of the industry is expected to widen over the coming two years.
