This article is a guest contribution by Kevin Cochrane, Chief Marketing Officer of Vultr cloud infrastructure company. The views expressed in the article are solely those of the author.

At the start of the year, which should usher in abusy travel season, the hospitality industry is simultaneously grappling with rising operating costs and increasingly unstable demand. Geopolitical tensions continue to impact airfare and fuel prices, food and beverage supply chains, and traveler safety, further complicating the operating environment.

Occupancy rates alone are no longer sufficient to ensure profitability.The hospitality industry is turning to technology, especially artificial intelligence, to extract higher returns from each guest and build a buffer against uncertainty. However, these systems rely on high-performance IT infrastructure to function, and many hotels lack the conditions needed to support their operation.

A joint report by McKinsey & Co. and Skift Research found thatthe hospitality industry lags behind other industries in AI adoption rate and maturity. Even so, industry leaders remain highly enthusiastic about its potential: 90% of travel industry executives are already using generative AI in their businesses, and 80% plan to expand automation applications with agentic AI within the next three to five years.

Facingunprecedented operational challenges, hotels cannot afford to wait five years to deploy advanced solutions—they need these capabilities now. By upgrading to more powerful, resource-efficient infrastructure, hotels can power AI initiatives and protect their profitability today.

Core Capabilities

Pursuing scale alone is not the answer. The key to hotels benefiting from AI lies in focusing on maximizing marginal value. With supply continuing to outpace demand, hotels must extract more value from every booking while reducing labor and operational costs per unit of occupancy.

The following use cases are gradually being implemented across major global hotel chains:

  • Improved demand forecasting:AI tools enable hotels to update forecasting and pricing models in real time based on current demand drivers and guest behavior patterns. AI can automatically trigger actions to improve data depth and quality, such as reminding guests to update their loyalty membership information.
  • Labor optimization:AI systems can incorporate more accurate demand forecasts into weekly and monthly scheduling, avoiding costly overstaffing while ensuring each shift has enough employees to deliver quality service.
  • Increased profit per room:AI-driven insights, such as scenario modeling, can lead to more precise and strategic room rate setting. These gains accumulate across multiple properties, ultimately forming substantial growth.
  • Streamlined guest services:AI-driven interfaces for routine guest inquiries allow staff to devote more time to deeper customer interactions.
  • Expanded personalized experiences:Advanced AI unlocks a higher degree of personalized stays. For example, a business traveler who frequently stays at multiple properties might arrive to find their preferred in-room dining order ready, or the room thermostat preset to their customary temperature.

Starting with Data

Hotel guest data is highly fragmented. Many guests obtain lower prices and package deals through third-party travel services, which can lead to discrepancies in booking records. Meanwhile, hotel loyalty programs containing guest data can also become outdated if not manually updated.According to CNBC, more consumers are using personal AI agents to plan trips, which may make it harder for hotels to capture traveler data.

Without adequate visibility into data, hotels may miss key patterns that could lead to better guest experiences and higher revenue, while also creating blind spots in demand forecasting and potential cancellation warnings.

Hotels need a more robust IT frameworkto integrate siloed and inconsistent data. However, traditional infrastructure cannot support the data demands of organization-wide AI applications. Single-cloud models lacking proper software stack support often suffer from insufficient storage, lack of cross-property visibility, and inadequate computing power to handle the data volume and workload intensity required to run AI models.

Infrastructure Modernization

Hotels need high-performance computing capabilities but cannot afford unpredictable costs or rigid vendor lock-in—especially during such a period of unstable demand. AI costs are also rising, further squeezing IT budgets. Many major model providers are raising prices or restricting access due to shortages of necessary hardware.

To remain competitive without eroding profit margins, companies must invest in flexible, efficient infrastructure. This likely means moving from the single-vendor cloud architectures common in legacy systems to multi-cloud, software-supported ecosystems. Through this path, hotels can build scalable, flexible computing frameworks that synchronize data and AI model management across all properties.

A multi-cloud strategy also enables hotels to bring IT operations closer to guests—particularly beneficial for chains operating internationally. Running AI at the edge reduces latency, ensuring faster insights and real-time responses to demand changes, while improving guest-facing service performance.

This strategy also helps ensure compliance. Hotels inherently collect guests' identification and financial data. As personalization expands, the amount of identifiable data contained in profiles also increases. Consequently, hotels may face new regulations concerning data privacy and digital sovereignty. A single vendor cannot always guarantee sovereign infrastructure or ensure that stored data will not be replicated to other jurisdictions without authorization. Hosting data domestically on compliant infrastructure can avoid hefty violation penalties while enhancing the quality and integrity of services.