Hotels Are Treating ‘AI’ Like a Buzzword Instead of a Practical Strategy

At a recent Hotel Data Conference, panels mentioned AI constantly but rarely defined what they meant. The term was used interchangeably for systems that do very different things. That creates confusion for hoteliers deciding what to buy, build, or change.

Why this matters: a parallel with the cloud

The author draws a comparison to the earlier "cloud" hype cycle. A useful concept became a catch-all marketing term, which led to sloppy adoption, security shortcuts, and mismatched expectations. The same pattern is starting with AI: rapid adoption before clear definitions or operational guardrails.

What should come first: the problem, not the tool

Most conversations flip the order: they begin with "we have AI, what can we do?" The recommended sequence is the reverse. Identify a real operational problem first, then evaluate whether predictive, generative, or autonomous capabilities—or none of them—are the right tool.

An audience member described a narrow, real problem: conference attendees had an 8 AM session while hotel checkout for other guests was 11 AM, forcing attendees to check luggage and creating friction. The proposed fix was concrete: link the conference room block to extended checkout for that segment and reorder housekeeping to prioritize remaining rooms. That solution-focused example received little attention compared with abstract AI discussions.

A recommended tactic: internal hackathons

The article proposes running internal AI hackathons. Give staff time to pick a real operational issue and prototype a fix. Benefits: some projects solve long-standing issues, and the exercise quickly reveals internal capability versus marketing talk. Start small, iterate, and expand based on what works.

How one vendor approached the problem

LodgIQ's platform illustrates the recommended approach. They began by identifying pain points that cost commercial teams time—assembling data across systems and receiving unexplained recommendations that users override. Predictive, generative, and agentic layers were added later, behind a conversational interface that keeps a human asking and directing work. This order preserved trust and increased adoption.

The author stresses that technical sophistication alone does not create profit. Value comes when recommendations are used at scale. Time saved and decisions adopted are the measurable outcomes that translate into revenue or margin.

• Define a small, concrete operational problem that causes friction or wasted time.
• Run a focused internal experiment or hackathon to prototype solutions.
• Distinguish which AI capability (predictive, generative, agentic) is appropriate—and only use agentic systems with clear human oversight.

AI can help—but only when it's applied to real problems, with clear distinctions between capabilities and with humans kept in control. Starting with technology instead of operations leads to mismatched tools, wasted investment, and eroded trust.