Why Hotels Are Stuck in AI “Pilot Purgatory”

The useful part

It is developing enough confidence in the technology, data, and governance behind it to allow AI to influence real commercial decisions at scale. The trust gap becomes particularly visible in revenue management, where changes to pricing, inventory, or forecasting can have an immediate financial impact. McKinsey's State of AI 2025 found that fewer than one-third of organizations have successfully scaled AI beyond the pilot stage.

How it works

• Revenue management systems often reach conclusions that appear counterintuitive when viewed through a single data point, such as current occupancy.
• Hotels typically manage commercial data across a range of systems, including the PMS, CRS, RMS, CRM and distribution platforms.
• The quality of any automated recommendation depends heavily on how consistently those systems exchange information.
• Disconnected data can limit what AI is able to achieve, while inconsistent reporting and definitions can cause different departments to make decisions using different versions of demand.
• Applying AI to fragmented data does not simply risk producing a poor recommendation.

What to take from it

When the wrong type of AI is paired with incomplete or disconnected information, organizations risk creating bad recommendations at scale. The question matters because not all AI-enabled systems are designed in the same way. When the wrong type of AI is asked to solve the wrong problem, organizations risk creating confidence in outputs without improving the quality of the underlying decision.

Example or evidence

• Generative AI can help users interpret and communicate recommendations, while agentic capabilities can support execution by carrying out approved actions within defined parameters.
• September 29, 2026 Operations PIP Flexibility: 5 Ways Hoteliers Can Build a Stronger Case to...
• It is whether organizations trust it enough to become part of everyday decision-making.
• A revenue manager may be comfortable reviewing a recommendation generated by a system.

Details worth keeping

The challenge facing many hotel groups today is no longer access to AI. Advertisement For hotels, the challenge is often not whether AI works. Allowing that same system to automatically increase a rate, restrict lower-rated inventory, or decline a piece of business requires a higher level of confidence in both the data and the decision-making process behind the recommendation.

Related coverage

• Hospitality Net: Drawing on Google Cloud research of 2,400+ executives, the author argues hotels are deploying AI at the edges rather than embedding it in core value-creating processes, limiting returns.
• Hospitality Net: Using Cathay Pacific's Google AI contrail-avoidance trial as a framework, this piece argues hotels must define decision envelopes, run shadow-mode pilots, and track AI override rates before granting...
• Hawaiibusiness: UH-West O‘ahu's Create(x) lab and community partners help restore endangered ecosystems using ancestral knowledge.