Hotels Are Racing to Adopt AI, but They’re Running in Blind
Artificial intelligence is rapidly becoming the hospitality industry’s latest obsession. New tools appear almost weekly, and hotel leaders are eager to demonstrate they are embracing the future. Yet the excitement masks a potential problem.
As hotels rush to adopt AI, many are still refining their understanding of what AI truly encompasses. By taking time to distinguish between different forms of AI and how they align to specific business goals, hoteliers can turn early enthusiasm into long-term strategic success.
Adoption without understanding
The latest H2C AI and Automation in Hospitality Report shows that nearly eight out of ten hotel chains already use AI in some form, and almost nine out of ten expect to expand its use within the next two years. On paper, this looks like a confident and innovative move by the sector. But in practice only a small proportion of hotels have a defined AI strategy led by senior management. Confidence in AI-driven pricing scored barely above six out of ten, while integration challenges and fragmented data continue to be major barriers.
These figures reveal that many hotels are adopting AI tactically, essentially adding tools to solve isolated problems, rather than strategically, with a clear view of how different AI types align to distinct business outcomes. True transformation begins when hotel leaders understand what kind of AI they’re using, what problem it’s solving, and how it fits into a broader commercial strategy.
At the root of this confusion is a widespread assumption that AI is a single technology. It is not. The term covers several distinct forms, each with its own purpose and limitations. When these differences are ignored, technology is often applied in ways that add little value or even create new inefficiencies.
Three forms of AI every hotel should know
The first is what we at IDeaS call ‘mathematical AI’, the science behind modern revenue management. These are the proven algorithms that forecast demand, determine availability, and recommend the right price at the right time. This form of AI has quietly delivered reliable revenue optimisation for years and should be the cornerstone of a hotel’s commercial AI strategy.
The second is generative AI. It can write, summarise, or analyse text, images, and data, helping teams save time and communicate more effectively. Used properly, it can improve productivity across marketing, operations, and reporting. But it is not built to make precise pricing or forecasting recommendations, and lacks the specialized logic needed for accurate performance analysis. Generative AI typically works best as a support layer that enhances human output, rather than replaces it. Applying this type of AI to the wrong use case risks costly mistakes.
The third is agentic AI, an emerging category that can autonomously act on information rather than simply present it. Often agentic AI fuses together elements of generative AI and more specialised applications like mathematical AI. In a hospitality context, this could mean adjusting pricing and rate restriction guidelines when forecasted demand changes or triggering a marketing campaign to fill shoulder nights. Agentic AI has the potential to connect departments and create a truly adaptive commercial engine, though few hotels are using it today. Agentic AI should only be introduced once a hotel has strong data foundations and clear governance frameworks in place.
Why distinction matters
Recognising the differences between these types of AI and how they’re best applied in hospitality is more than academic. It is fundamental to implementing an effective AI strategy. Hotels that rely on flashy and not fit-for-purpose generative AI tools to make analytical decisions will find their strategy flawed and performance weakened. At the same time, those who implement the right types of AI tools on top of a poor data foundation are likely to replace manual inefficiency with automated error. The result is frustration, mistrust, and wasted investment.
The H2C research also confirms that the barriers to AI success aren’t solely technological, they are organisational as well. More than half of respondents cited a lack of AI expertise, and many reported limited data sharing between revenue, marketing, and distribution teams. Without collaboration and consistent data structures, even the most advanced systems cannot deliver on their promise.
This underscores the importance of taking a thoughtful approach to AI implementation and partnering with providers who can not only deliver the right tool for the job today but help build a strong data foundation for future success.
Clarity over complexity
The hotels that succeed with AI share one crucial trait. They start with clarity. They know exactly what they are trying to achieve, and they choose the right type of AI to meet that goal. They strengthen rather than replace human decision-making, and they connect their data so that all commercial functions move together. Machines handle the heavy analysis and tedious taskwork. People provide the direction, strategy, and oversight.
For hotel leaders, the next step is to formalise this understanding into their AI roadmaps. Map which business functions are best served by each AI type, define data requirements, and establish governance that blends machine precision with human oversight. In doing so, hotels can move from experimenting with AI to embedding it as a strategic enabler across their commercial ecosystem.
The future will not belong to the hotels that have the most AI, but to those that understand how to apply it best. The winners will use each form of AI where it truly adds value, and they will maintain human oversight in every decision that defines their brand and guest experience.
AI will continue to shape hospitality in profound ways. But real progress depends on understanding, not hype. When hotels apply AI with purpose and precision, it becomes more than a trend. It becomes a tool for lasting performance and smarter, more confident decision-making.
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