The hotel industry’s AI. Can we catch up by asking the right questions?
Last week, I listened to a revenue manager explain why their AI system recommended a 30% rate increase for next Thursday. The explanation took 6 minutes and involved three different dashboards. I still don’t get the reasoning behind the decision.
In my eyes this captures where revenue management in hospitality stands with AI today. We’re embracing it. Yet we seem to be ages behind on other industries, already.
Because we’re still figuring out how to make it actually useful.
A different reality: While other industries have been perfecting AI for years, hotels are just now moving beyond basic automation. Companies like Runnr.ai did make significant strides forward managing a multitude of guest requests and communication.
Besides that, hotels often do struggle with implementations. And frankly, that might be a blessing in disguise.
While other industries were busy teaching AI to drive cars and diagnose diseases, we were still figuring out why the PMS froze each time whenever a corporate booker tried to enter a company name longer than 35 characters.
Jokes aside, here’s what I’m seeing across properties that are getting AI right in areas of revenue management:
Transparency: The best systems don’t just tell you what to do—they explain why in language your GM would understand. “Your pickup is accelerating because there’s a medical conference downtown and your nearest competitor just closed bookings” beats “algorithm suggests 15% increase” every time.
Relevance: Smart hotels are learning that more data doesn’t equal better decisions. The most effective systems focus ruthlessly on the handful of external factors that actually influence their specific property. A beach resort doesn’t need cryptocurrency trends; a business hotel doesn’t need surf reports.
Human override options: Here’s the part that surprises people—the hotels seeing the biggest improvements aren’t the ones that follow AI recommendations blindly. They’re the ones whose systems learn from when revenue managers say “no.”
The most sophisticated AI isn’t about replacing human judgment. It’s about amplifying it.
What excites me most is watching systems that remember when they’re wrong. A system that learns “this revenue manager always overrides my weekend suggestions because they know about the local wedding venue” becomes far more valuable than one that keeps making the same mistake.
The hotel industry might be late to the AI party, but we’re arriving with something other industries lost along the way: the wisdom to question our tools rather than blindly accept them.
The real question isn’t whether your hotel needs AI—it’s whether your AI understands your hotel.
How transparent is your current system about its reasoning? And when you disagree with its recommendations, does it learn from that?
#Xotels #revenuemanagement #hotelmanagement #artificialintelligence #AI #hotelindustry #HotelStrategy
