WASHINGTON D.C.—A new benchmark study released earlier this week at Destination AI suggests hotel companies are moving beyond the initial excitement around artificial intelligence and into the more difficult work of implementation, integration and measuring returns.
Findings from the inaugural State of Hotel AI A Benchmark show that while hotel executives increasingly see AI improving productivity and operational efficiency, many still struggle to turn strategy into measurable business outcomes.
Presenting the research at Destination AI, Josiah Mackenzie, VP of marketing of Actabl and host and producer of Hospitality Daily, described the survey as "the most exhaustive study of what AI is really doing in hotels right now," noting that it included representation from more than 100 companies, including management companies, owners, investors, brands and on-property leaders.
According to the study, 90 percent of respondents said AI is helping routine work get done faster. Mackenzie said the findings also point to AI's growing impact on creativity and productivity among hospitality leaders.
"Ninety percent said routine work for them was getting faster," he said. "Nearly as many said that they were able to be more creative."
Operational efficiency emerged as the top area where respondents believe AI provides value, cited by 38 percent of participants, compared with 28 percent who identified revenue growth as AI's strongest contribution. Guest communications and finance tied as the operational areas generating the biggest results from AI.
The survey also revealed a notable divide between independent and branded hotels. According to Mackenzie, respondents from independent hotels were more likely to identify commercial performance as AI's biggest benefit, while branded hotel respondents pointed to operational efficiency.
While business leaders see meaningful gains from AI, the perceived benefits are concentrated among management teams rather than frontline employees. Eighty-four percent of respondents said AI has improved their own work, but only 15 percent said it has improved frontline work.
"So I think this is the challenge I would have for you," Mackenzie said. "How do we think about a world where the benefits of AI are recognized by the people on the front lines of hospitality? It seems like we're not there yet, and I think there is potential to get there."
Despite growing adoption, financial returns remain difficult to quantify. Only one in four respondents reported payback from AI investments, and many said it is still too early to measure results. Researchers found that claims AI "has paid for itself" often reflected a sense of productivity improvement rather than hard financial metrics.
"More often than not, this idea of AI paying for itself yet was more of a feeling than hard data on the financial return," Mackenzie said. "The tighter we can tie investments in AI to financial outcomes, the better."
Still, confidence in AI spending remains high. No respondents reported plans to reduce AI spending in their 2027 budgets. The leading investment objective remains reducing costs and improving productivity, followed by generating incremental revenue and improving the guest experience.
The biggest challenges cited by respondents were not technological limitations but implementation issues. Change management, data quality and integrations topped the list of barriers to adoption, while only 42 percent of respondents expressed confidence in their organization's AI data readiness.
"Implementation barriers were the number one thing holding back results from AI," Mackenzie said. "Only 8 percent said it was the limitations of the AI technology itself. So execution is a challenge."
Perhaps the most noteworthy finding was what respondents identified as the biggest driver of AI success. Experimentation ranked first, ahead of executive mandates or hiring dedicated AI talent.
"It was not top-down executive mandates," Mackenzie said. "It was experimentation. It was creating space for teams to show initiative and some bottoms-up innovation."
That finding suggests the next phase of hotel AI adoption may depend less on adopting new technology and more on empowering teams to rethink workflows, improve data quality and turn AI experimentation into measurable business results