New research shows 53% of hotel revenue leaders employ artificial intelligence for room-rate setting, yet only 10% are prepared to let the technology decide most pricing choices.
Survey scope and key adoption figures
The study, released on October 20, surveyed 450 senior revenue decision-makers across the United States, United Kingdom, Canada, Mexico, German-speaking Europe, Spain, Australia and New Zealand. It gathered insights from a broad mix of independent boutique properties and large international chains, providing a snapshot of global attitudes toward AI in hospitality.
Among respondents, 16% reported extensive AI use, while another 35% are piloting or planning adoption. A small 11% said they are not considering AI at all, indicating that a minority remain completely disengaged from the technology.
Trust versus delegation
Although 77% expressed some degree of trust in AI, only a small fraction would allow it to make most pricing decisions, according to the report.
Most hoteliers still examine AI-generated rate suggestions before implementation, indicating the technology serves as a decision-support tool rather than a replacement. Operators typically review the output to ensure it aligns with brand standards, local market conditions, and strategic objectives.
AI systems usually propose rates based on demand forecasts, competitor pricing and other market data; the final choice must align with each hotel’s commercial strategy, which often incorporates factors such as loyalty program considerations and seasonal promotions.
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Chas Scarantino, chief executive of RoomPriceGenie, said hotels need clearer insight into how recommendations are derived. “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,” he explained.
The findings highlight a challenge for revenue-management software providers: delivering recommendations that are transparent enough for operators to act on without hesitation, while still leveraging sophisticated predictive models.
Revenue-management technology gaps
Even as AI gains traction, 43% of surveyed properties lack a dedicated revenue-management system. Fourteen percent rely on manual pricing, and 29% use spreadsheets with limited automation, suggesting that many establishments still depend on low-tech solutions.
Demand forecasts were rated very influential by 52% of respondents, while 51% cited competitor rates as equally important, showing that both internal predictions and external benchmarks drive pricing strategies.
Only 30% said gathering pricing information is quick or easy. That share rises to 47% for hotels that already have a revenue-management system, a 17-point difference that shows the efficiency gains from dedicated software.
While the data suggest better-equipped hotels face fewer information hurdles, the survey does not prove that technology alone resolves the issue, as human judgment remains a critical component.
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A strong majority—85%—agree that effective revenue management is essential for hotel profitability, reinforcing the link between pricing discipline and financial performance.
Staffing shortages compound the issue
Fifty-eight percent consider a dedicated revenue-management role very important, yet just 24% have a specialist primarily responsible for pricing, indicating a gap between perceived need and actual staffing.
In many cases, general managers or owners retain pricing authority alongside their broader duties, which can stretch their capacity to focus on detailed rate optimization.
Properties tend to add dedicated revenue staff after surpassing 50 rooms; the median size for hiring such expertise is 146 rooms, reflecting a threshold where revenue complexity justifies a specialist.
Recruiting qualified revenue managers remains difficult, with nearly half of respondents (47%) reporting challenges finding suitable candidates. The recruitment challenge is particularly pronounced in German-speaking European markets, where 66% reported difficulties finding qualified professionals, and in Canada, where 56% reported similar difficulties.