At a time when artificial intelligence dominates the global tech agenda, the gaming industry is starting to look beyond the hype and focus on what actually delivers business value. The “State of AI in Gaming 2026” report, developed in the United States by the UNLV International Gaming Institute through AiR Hub, in collaboration with KPMG, is based on surveys and interviews with operators, suppliers, regulators and startups across multiple markets. It offers one of the clearest snapshots to date of how AI is really being used — and, more importantly, where it is still falling short.
Artificial intelligence in the gaming industry has firmly established itself as a strategic priority in 2026, although its impact on business performance remains somewhat limited. The report analyses levels of adoption, maturity and return, bringing together insights from across the value chain. The full study is available via AiR Hub, alongside institutional information from UNLV International Gaming Institute and KPMG.
One of the most notable findings is the level of adoption: more than 80% of companies are already using some form of AI, particularly generative tools. Even so, this widespread deployment has yet to translate into clearly measurable financial outcomes. The report places the average maturity level at around 45 out of 100, indicating that most organisations are still in early or intermediate stages. In practical terms, AI is present across operations, but not yet embedded in decision-making frameworks or directly tied to revenue generation.
Across functions, roughly half of all use cases are concentrated in technology operations, security and product development. In security, AI is already standard in fraud detection, transaction monitoring and anti-money laundering controls. On the operational side, it helps streamline internal processes and reduce costs, while in product it enables more responsive, data-driven user experiences. These are meaningful improvements, but still not enough to materially reshape revenue structures.
On the commercial side, AI is most visible in marketing and CRM, where it supports segmentation, campaign automation and conversion optimisation. However, these gains are not always properly tracked. Around 25% of companies lack clear performance metrics, making it difficult to assess impact or justify further investment. As a result, many initiatives remain tactical rather than strategic, limiting their long-term value.
This feeds directly into the core issue: return on investment (ROI). Only 20% of companies report meaningful returns within a two-year period, highlighting a persistent gap between expectations and execution. In many cases, projects remain stuck in pilot phases, without reaching full-scale deployment. AI is being adopted — but not yet fully integrated into the economic engine of the business.
The report also highlights a clear divide between online and land-based operators. Digital-first companies tend to show higher maturity, benefiting from infrastructures designed for data scalability and automation. Land-based operators, by contrast, are still constrained by legacy systems and more rigid processes. This gap directly affects personalisation, efficiency and player retention, all of which are becoming critical competitive factors.
From a regulatory standpoint, the study’s “regulatory pulse” reveals another layer of complexity. A significant share of regulators acknowledge they lack sufficient visibility into how AI is actually deployed. At the same time, fewer than half of jurisdictions have established — or are developing — specific frameworks for AI in gaming. This creates a level of regulatory uncertainty that, in practice, slows down more advanced implementations.
There is also growing scrutiny around player protection. In more mature markets, regulators are beginning to demand greater algorithmic transparency, traceability of automated decisions and demonstrable use of AI in harm prevention. This signals a shift: AI will increasingly be assessed not just for efficiency, but for its role in compliance and accountability.
The concept of “Responsible AI” remains underdeveloped. Nearly a third of companies lack formal policies, and only 2% report full integration of responsible AI principles. Fewer than 20% have dedicated governance structures in place. In a highly regulated industry, this represents a growing operational and reputational exposure, even if it is not always prioritised internally.
Meanwhile, innovation continues to accelerate. Patent filings related to AI in gaming have grown steadily, from 15 in 2010 to more than 100 in 2025, with a strong concentration in the United States. Investment in startups and academic research is also expanding. That said, this growth remains concentrated among larger players, those with the resources to invest and scale effectively.
More advanced developments, such as agentic AI, remain at an early stage. The main barriers are not technical, but regulatory and trust-related. Fully autonomous decision-making still raises concerns, both legally and reputationally, which continues to slow real-world deployment.
Ultimately, the conclusion is less about technology and more about execution. The industry does not face an adoption problem, but an execution gap. AI is already embedded across the business, yet it has not consistently translated into sustainable value creation.
In an increasingly competitive environment, the operators that manage to scale AI with a clear impact on revenue, efficiency and compliance will be the ones to gain ground. The rest risk remaining in a cycle of experimentation, with limited tangible impact on the business.
