Most hotels are doing AI backwards

Hotels keep starting with the technology and hunting for problems it might solve; the ones winning with AI in 2026 will do the opposite. Start from the friction, pick one workflow, and let the numbers decide what comes next.

They start with the technology, then hunt for problems it might solve. A vendor pitches an AI agent, someone forwards a LinkedIn thread and suddenly there are three pilots running across reservations, revenue, and marketing, none of them tied to a number anyone can defend. Six months later, you end up with a graveyard of pilots and no impact on the P&L. 

The reason is simple: AI got treated as a strategy instead of a tool. And tools don’t matter until you’ve named the job.

Start from the friction instead. Pick the work that bleeds money or hours every week, the spreadsheet someone rebuilds every Monday, the report that swallows a full afternoon, the decision that keeps slipping because the data lives in five different systems. That’s where AI earns its keep or proves it isn’t the right answer. 

The five steps below are how I’d actually run that exercise inside a hotel or hotel group. The goal isn’t to deploy AI everywhere. It’s to find the one workflow where AI removes real, measurable friction, prove it pays for itself, and only then move to the next. You’ll pick the right problem, pressure-test whether AI can solve it, commit to one workflow, run a measurable 30-day trial, and let the numbers decide what comes next.

Here’s how I’d run it:

Step 1: Map your weekly pain in euros and hours.

Sit down with your commercial, ops, and finance leads, and list the recurring tasks that:

• Take more than 2 hours a week
• Require pulling data from 3+ systems
• End in a decision someone postpones because it’s tedious

Pricing reviews. Group quote turnaround. Month-end reporting. Review responses. Corporate account follow-ups. Forecast updates. Then pick the top 3 by hours × frequency × decision impact.

Step 2: For each one, ask three questions.

• Is the bottleneck information or judgment? AI handles information well. Judgment still needs your team.
• Does the task have a clear input and a clear output? If you can’t describe both in one sentence, it’s not ready for AI yet.
• What does “good” look like? If you can’t define a quality bar, you can’t tell when AI gets it right.

If a task fails any of these, park it. Move to the next.

Step 3: Pick one. Just one.

The mistake is starting with five. Pick the task with the highest hours-saved-per-week and the clearest quality bar.

Step 4: Run a 30-day measurable trial.

Before you start:

• Baseline the current time and cost
• Define the quality threshold (e.g. “75% of outputs need zero edits”)
• Pick one person who owns it

After 30 days, you should have a real number: hours saved, revenue captured, errors reduced.

Step 5: Only then expand.

Once one workflow is paying for itself, add the next.

The hotels winning with AI in 2026 won’t be the ones with the most tools. They’ll be the ones who killed the most weekly meetings and manual processes.

This is roughly how we built HotelGPT at Juyo.

We didn’t start with the model. We started with the questions 1,500 hotels were already asking, week after week. Why is RevPAR off? Where did pickup come from? Which segments are underperforming versus last year? What should I price the group asking for 40 rooms in October?

The point isn’t that AI is magic. It’s that the right AI, pointed at the right friction, gives your commercial team their week back.

That’s the bar we built HotelGPT against, and it’s the bar we’d recommend you hold every AI pilot to.

If you want to see what that looks like in practice, join the HotelGPT waiting list here:

Written by Vassilis Syropoulos

Founder at Juyo