Mews Survey Finds 98% of Hoteliers Use AI

Why It Matters

The findings confirm AI as a baseline operational tool in hospitality while highlighting the need for governance and human‑centric design to unlock revenue potential.

Key Takeaways

• 98% of hotels use AI in at least 11 core tasks
• 59% say front‑desk welcome and check‑in should stay human‑led
• Only 41% have formal AI governance; lack reduces trust
• Properties with AI policies report 92% strong trust versus 49% without
• Revenue growth is top AI goal for 52% of AI‑proficient hotels

Pulse Analysis

The Mews Hotelier Survey 2026 reveals that AI has moved from pilot projects to mainstream deployment across the hospitality sector. With 98% of surveyed properties reporting AI usage in at least 11 of the 19 most common tasks, hotels are leveraging machine learning for everything from dynamic pricing to housekeeping scheduling. This breadth mirrors adoption rates in retail and finance, but the hotel industry’s front‑of‑house focus adds a layer of guest‑experience nuance that technology must respect. The data underscores that AI is now a baseline operating tool rather than a differentiator.

Even with near‑universal adoption, hoteliers remain cautious about fully automating guest interactions. The survey shows 59% of respondents insist that the front‑desk welcome and check‑in stay human‑led, a sentiment strongest among properties that already use AI extensively. This paradox highlights a mature understanding: technology can boost efficiency, but the personal touch still drives loyalty. Governance, however, lags behind; 41% of hotels operate without a formal AI policy, and trust plummets to 49% in those environments. Formal guidelines appear to double confidence, with 92% of policy‑backed hotels expressing strong trust in AI.

Revenue generation has emerged as the next frontier for AI in hospitality. Among the most AI‑proficient hotels, 52% cite revenue growth as the primary outcome they expect, eclipsing pure cost‑cutting goals. To deliver that upside, Mews is developing a semantic layer that injects property‑specific knowledge into AI models, moving beyond generic data sets. By linking spreadsheets, staff expertise, and disparate systems, the layer promises context‑aware recommendations for pricing, upselling and guest personalization. If successful, this approach could set a new industry standard, turning AI from a support function into a strategic revenue engine.