Gambling Industry Shows AI Ambition but Weak Governance, UNLV Study Finds
LAS VEGAS – A new report from the UNLV International Gaming Institute finds the global gambling industry is moving quickly into artificial intelligence, but still sits in an early stage of maturity.
LISTEN TO THIS ARTICLE:The research puts the sector’s AI Maturity Index at 45 out of 100, a level that signals active adoption but also warns operators, suppliers, and regulators that governance, expertise, and oversight are not keeping pace.
The State of AI in Gaming 2026, produced by UNLV International Gaming Institute in collaboration with KPMG, is the first edition of what the report says will become an annual benchmark for tracking AI across gambling. It draws on original survey work, regulatory analysis, and innovation data covering adoption, regulation, research, patents, conference activity, and responsible AI.
The headline number is not just the overall score. UNLV’s index found strategy was the strongest dimension at 57, while governance trailed at 30, leaving a 27-point gap between ambition and safeguards. That matters because the sector is not standing still. More than 80% of surveyed companies reported using generative AI, yet only about one in five companies had dedicated AI governance roles, and only 2% described responsible AI as fully embedded across their organizations.
Related: UNLV Study Finds Rising Slot Hold Rates in Nevada
Where AI Is Growing Fast, and Where It Is Falling Short
According to the report, AI deployment is concentrated most heavily in Technology & Security and Product Development & Innovation, which together account for nearly half of all weighted activity across the sample. Customer-facing functions and business operations sit in the middle, while risk and compliance trail. The pattern is notable because it runs against the common assumption that gambling companies are primarily using AI for front-end personalization and marketing.
The UNLV research also found a sharp split between business models. Land-based operators trail online counterparts by a statistically significant margin across strategy, infrastructure, and expertise, with the report pointing to legacy systems, fragmented data, and operational complexity as likely reasons. Online operators, by contrast, scored much higher and appeared more likely to sit in the report’s “leaders” category.
Several of the most important findings sit in the details:
- 81.5% of respondents said they use generative AI, while 32.1% reported using AI agents or reasoning systems.
- 61.4% cited knowledge and training gaps as the biggest barrier to scaling AI, and 53.0% cited resource or budget constraints.
- 42.2% said they have no AI-specific hiring planned, even though 53.0% expect AI to reshape roles and require reskilling over the next three to five years.
The commercial picture is also mixed. Cost reduction emerged as the top driver of AI adoption, followed by customer experience and revenue growth. But only one in five companies said they had already achieved meaningful ROI, while another quarter said they had no structured process for evaluating AI performance at all. In other words, the industry is investing, but many firms still cannot say with much rigor whether those investments are paying off.
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Regulators Increase Scrutiny as Industry Alignment Gaps Persist
The report’s regulatory section may be the most consequential for the market. Based on a regulatory scan and a survey of 113 gambling regulators worldwide, UNLV found that AI-related gambling regulation remains heavily concentrated in North America and Western Europe, with no recorded activity in the report’s scan across Central and Eastern Europe, Asia-Pacific, or the Middle East and Africa. Most of the regulatory action so far centers on automated tools for detecting gambling harm.
Yet the report also found a clear gap between what regulators think is happening and what companies say they are actually doing. Regulators most often identified customer-facing functions as the main area of AI use, with 78.8% selecting that category. But the industry survey showed the heaviest use in technology, security, and product innovation. Fewer than one in four regulators agreed they were aware of current AI applications in licensee operations, and a majority rejected the idea that the industry is capable of responsibly self-regulating AI use.
That disconnect runs alongside low regulator confidence in current oversight capacity. The report says regulators showed limited awareness of responsible AI practices among licensees and broad agreement that cross-jurisdiction collaboration would improve oversight. It also found regulators tended to think current gambling rules are not equipped to handle AI’s risks and opportunities.
Research and Patents Signal Growing AI Momentum
For all the operational gaps, the wider AI ecosystem around gambling is moving in one direction: up. UNLV’s research section found AI-related gambling publications reached 112 in 2025, or 12.5% of the total AI-gambling research corpus examined, with sports betting and online problem gambling now leading the academic agenda. The report also shows that AI sessions at major gambling conferences rose from 3 in 2020 to 81 in 2025, and that annual AI-related gambling patent grants increased from 15 in 2010 to 100 by 2025. The United States accounted for 61.4% of all patent filings in the dataset.
UNLV itself appears inside that innovation map. In the report’s review of top publishing institutions from 2020 to 2025, the University of Nevada, Las Vegas is listed among the 10 most active institutions by publication count. That gives the study added relevance for operators and suppliers looking not just for a one-off headline, but for a research base that may shape how the sector measures progress year by year.
The report stops short of claiming the industry is failing. What it does say, quite clearly, is that gambling has entered the AI era without yet building the governance maturity to match its strategic enthusiasm. That leaves the sector in a familiar position: commercially interested, operationally active, and increasingly innovative, but still short of the structure needed to manage AI with confidence at scale.
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