Hotel chains have broadly adopted artificial intelligence, yet most lack the enterprise readiness to scale it beyond isolated pilots, according to a new study from h2c, the hospitality consulting and analyst firm.

The study, published via Hospitality Net, examines how chain-affiliated hotel companies are deploying AI across operations, distribution, and guest-facing functions. Its central finding is a gap between adoption and infrastructure: AI tools are already widespread across hotel chains, but the underlying data architecture, governance, and organizational capabilities needed to support them at scale remain limited.

That gap carries direct cost implications for operators. AI systems layered onto fragmented property management, central reservation, and customer data platforms tend to deliver marginal gains at best, because the models depend on clean, unified data to produce actionable output. Where readiness is weak, chains pay for tooling twice — once for the AI licenses themselves and again for the integration work required to make the systems functional.

For hotel groups, the readiness question also determines who bears the cost of deployment. Company-operated portfolios can centralize AI investment at the corporate level, while franchised systems face the harder problem of pushing adoption out to independent owners who must fund implementation at the property level. The study's findings suggest that chains with heavy franchise mix will move slower on enterprise-grade AI unless corporate funds the infrastructure directly.

The pattern mirrors what has played out in restaurant technology over the past several years: rapid adoption of point solutions — chatbots, dynamic pricing engines, revenue management assistants — followed by a harder, slower phase of consolidation onto integrated platforms. Hotel operators now appear to be entering that second phase, where the differentiator is not whether a chain uses AI but whether its data and systems can support it consistently across the estate.

h2c positions the research as a benchmark for hotel groups assessing their own AI maturity. The findings imply that operators should treat AI investment decisions as infrastructure decisions first — evaluating data quality, system integration, and staffing before adding new tools — rather than as standalone technology purchases.

The study signals that the next competitive phase in hotel AI will be decided less by adoption rates, which are already high, and more by which chains build the enterprise foundations to turn widespread experimentation into measurable returns.