AI Is Becoming the Hotel Industry’s New Manager

Labor accounts for 51.7% of hotel operating expenses and 32.4% of total revenue at U.S. hotels, according to CBRE Group’s “Trends in the Hotel Industry” report, which analyzed 2,456 properties using 2023 data. Hotels paid 11.9% more in total labor costs that year while operating with 5.9% fewer employees than in 2019, CBRE found. They are paying more for less capacity.

In North America, 65% of hotels reported staffing shortages in 2025, BCG reported, citing the American Hotel and Lodging Association (AHLA). The math is pushing hotel operators toward AI not as a guest-facing amenity but as an operational management layer.

Hotels generate thousands of operational decisions each day across departments that rarely share data in real time. A room behind schedule affects check-in availability. A maintenance issue in a room block affects housekeeping routing. A staffing gap in one shift affects service levels hours later. Each dependency is currently managed through phone calls, radio communication and manual tracking, Hospitality Net noted. AI platforms are beginning to replace that coordination layer by connecting occupancy forecasts, internet of things (IoT) sensor data and guest activity into a single operational system that adjusts continuously.

AI Housekeeping Cuts Room Turnaround Time and Labor Waste

Housekeeping is where the labor pressure is sharpest and where AI is showing the earliest measurable returns. The Ritz-Carlton San Francisco implemented an AI system that synchronizes room-cleaning schedules with checkout patterns, guest preferences and staff availability, cutting room preparation time by 20%, BCG reported. IHG Hotels & Resorts has deployed predictive housekeeping models that anticipate peak cleaning times and allocate resources accordingly, according to BCG. AI housekeeping platforms replace static room assignment lists with dynamic routing that updates in real time as checkouts occur, rebalancing workloads across staff and cutting wasted movement, Hospitality Net noted.

Predictive maintenance is the other high-return application. IoT sensors on HVAC systems, elevators and boilers feed AI models that monitor vibration, temperature and energy draw continuously. When a compressor begins behaving abnormally, the system flags it before it fails, generates a work order and queues the repair during a low-occupancy period. Predictive maintenance programs can reduce maintenance costs by 25% to 30% and cut equipment downtime by 35% to 45%, according to the U.S. Department of Energy’s Operations and Maintenance Best Practices Guide.

Hilton’s LightStay platform, which tracks energy, emissions, water, waste and social impact across its properties, has delivered more than $1 billion in cumulative savings in costs since 2009, Hilton reported.

The Coordination Layer AI Is Actually Replacing

The opportunity AI addresses in hotel operations is not the front-desk agent or the housekeeper. It is the management overhead between them: the time spent triaging information that already exists in separate systems and has not been connected. PYMNTS Intelligence found that 52% of hospitality customers expect AI to play a role in customer interactions. Agoda, a Booking Holdings subsidiary, found that AI-assisted automation drove a double-digit year-over-year reduction in customer service costs per booking, PYMNTS reported.

The pattern across all three is the same: AI handles information aggregation and decision routing that managers currently do manually, freeing staff for work that requires physical presence or guest judgment. A unified operational platform that connects housekeeping status, maintenance alerts and occupancy data gives a general manager a real-time picture that previously required walking the floor and calling department heads.

PYMNTS Intelligence found that 37% of Labor Economy workers, including those in hospitality, said their employer had introduced new automation or AI tools in the previous 12 months. Yet nearly 60% of workers affected by those tools said they received no training, suggesting deployment is moving faster than workforce readiness. The training gap, not the technology, will decide which hotels actually get this right.