I’ve probably been asked about a hundred times in the last two years to list the “main” use cases of AI in our industry. And while I understand what drives this request (we’re all just trying to wrap our heads around AI while drinking from the firehose of unlimited possibilities it presents), I’m not a big fan of boxing people in with a static list of examples.

I’ve done that a few times publicly, publishing white papers and articles (for example, in HFTP’s Tech+Fin Hospitality magazine) based on in-depth research. But what I’ve noticed is this: things change so fast, and the number and types of use cases expand so dramatically, that it’s a bit like trying to count stars in the sky. By the time you finish, a thousand new ones appear.

Here’s what I’ve learned over the past decade consulting hotel companies: in our industry, there is no one-size-fits-all. Every company is unique: the way they run their operations, the tech stack they build, the add-ons they plug into their PMS, even the organizational structure itself (which dictates who the end users of technology are). Naturally, each company will end up with its own set of AI use cases, defined by what data their systems generate and what business problems they’re trying to solve.

So, when we talk about “what connections are required to build clean datasets for useful AI actions,” I’d argue that the answer will always be: it depends. There’s no universal recipe card. What’s essential for one company may be irrelevant for another. The bigger opportunity here is to stop chasing the perfect, finite list of AI use cases and instead focus on building flexible, connected, clean data foundations that allow each business to define and evolve its own use cases as the technology (and their needs) keep evolving.

Because in the end, AI in hospitality isn’t about narrowing down the possibilities, it’s about laying the right pipes that naturally flow from how your company operates.