AI Consulting

Where should a hotel actually start with AI (without wasting money)?

Start where the pain is repetitive and measurable, not where the technology is most exciting. The best first AI project pays for itself quickly and earns the confidence for the next one.

The hardest part of AI in a hotel is not the technology. It is choosing the first project, because the wrong one burns budget and goodwill, while the right one funds everything after it.

Teams often start with whatever sounds most impressive. That is usually the most expensive way to learn what you needed to know first. A better approach is almost boring on purpose.

Start with a repeated, measurable task

The best first project is something your team already does many times a day, where the time spent is easy to count: answering the same guest emails, looking up policies, preparing the same reports. Repetition means impact, and measurability means you can prove the result.

Avoid the expensive distractions

The flashy projects, a clever website chatbot or a grand data platform, tend to cost the most and prove the least early on. They are not wrong, they are just the wrong place to begin. Save them for once you have an internal win to build on.

Prove it, then expand

A focused pilot with a clear before and after lets you measure hours saved or errors avoided in weeks. That evidence unlocks budget and buy-in for the next step, and it turns AI from a gamble into a sequence of improvements that have already paid for themselves.

The right first AI project is boring, repetitive and easy to measure. That is exactly why it works.

Key takeaways

  • Start where the work is repetitive and the time cost is measurable.
  • The most exciting projects are usually the worst starting points.
  • A small, provable pilot funds and justifies the next step.
  • Sequence beats big-bang: one win at a time.

Frequently asked questions

How much should a first AI project cost?
Less than you might expect. A focused first deployment targets one workflow, so it stays small and fast, and it should pay back in saved time well before a large platform would even launch.
Do we need to fix all our data first?
Usually not. You start in one area where the data is good enough, prove the value there, and improve the rest as you expand.

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Bartomeu Gili Prohens
Founder & CEO, RaceMyDesk
Building private, enterprise-grade AI for travel and hospitality. LinkedIn
Private AI, AI strategy and AI visibility for travel and hospitality.
racemydesk.com