53% of Hotels Use AI to Price Rooms, but Only 10% Trust It Fully

Fifty-three percent of hotels now use artificial intelligence to help set room prices, yet only 10% would let it make most pricing decisions on its own. Those two figures come from RoomPriceGenie's 2026 State of Revenue Management Report, a survey of 450 senior revenue decision-makers across the UK, US, Canada, Mexico, DACH, Spain, Australia and New Zealand. The gap between the numbers is the story. Hotels are adopting the technology faster than they trust it.

The report, published by the revenue management system vendor, surveyed operators at both ends of the technology spectrum. Adoption is broad. Beyond the 53% already using AI, 16% use it extensively. Another 35% are piloting or considering it. Just 11% are not considering it at all.

Trust lags well behind. While 77% of hoteliers say they trust AI to at least some degree, most still check its recommendations by hand before acting. The report frames the next challenge plainly: AI in revenue management must explain itself well enough that operators feel confident acting on it. A vendor survey naturally favors the case for buying a system. The trust numbers suggest buyers are not yet convinced.

What sits behind the pricing at hotels without AI?

Forty-three percent of surveyed hotels have no revenue management system at all. That figure splits two ways. Fourteen percent price rooms manually, on instinct or fixed seasonal rates. Twenty-nine percent use spreadsheets with limited automation. Any AI pricing pitch to this segment replaces human guesswork or semi-automated spreadsheets — not a competitor's algorithm.

Information access compounds the problem. Demand forecasts rank as the most influential pricing input, with 52% calling them very influential, and competitor rates follow at 51%. Yet only 30% of hoteliers say gathering pricing information is quick or easy. Among hotels with an established revenue management system and strategy, that figure rises to 47% — still under half.

Who actually sets the price?

Eighty-five percent of hoteliers agree revenue management is essential to profitability. A similar share say a dedicated revenue management role is at least somewhat essential, including 58% who call it very or absolutely essential. Responsibility tells a different story. Only 24% give a dedicated revenue manager primary responsibility for pricing. General managers and owners still hold the lever at most properties, alongside their wider duties.

Property size drives the hire. Hotels become significantly more likely to employ dedicated revenue expertise once they pass 50 rooms. The median hotel makes that hire at 146 rooms.

Is the talent even available?

Nearly half of hoteliers, 47%, say they struggle to find qualified candidates for revenue roles. That tension runs hardest in DACH, where 66% report recruitment difficulties, and Canada, at 56%. Meanwhile, 70% believe revenue management expertise is growing across the industry. Both claims can be true. A maturing field does not guarantee a maturing pipeline in every market.

RoomPriceGenie CEO Chas Scarantino said: "Margins are tighter than they've been in years, and guests compare dozens of options before they book, so a wrong price costs real money, fast. Our research shows that hoteliers see the value of better revenue management, but many are still trying to do more with limited time and expertise. AI has huge potential to help, but adoption alone isn't enough. Hoteliers need to understand why a price is being recommended, make sure it fits their strategy, and feel confident acting on it."

Scarantino's pitch — "sophisticated revenue management without requiring a specialist behind every pricing decision" — targets exactly the 43% without a system and the 47% who cannot hire one. The survey does not report revenue uplift from AI adoption, only attitudes. Hotels weighing the investment will want margin evidence, not adoption rates, before the 10% trust figure moves.