Hotels and AI: New Search Tools, Smarter Call Centers, and Why Robots Aren't Taking Over
Senior leaders from IHG, Wyndham, Choice Hotels, Hilton, Hyatt, and Marriott's vacation rentals division met at the Destination AI forum in Washington, D.C., to compare notes on AI. The consensus: it's still early. Most efforts are framed as experiments, with cautious rollouts and tight scopes. Yet several use cases are already moving the needle.
Where AI Is Working: Contact Centers
Contact centers are the clearest proving ground. Hotels field a surprising volume of routine calls-like guests phoning just to confirm a reservation. Automating these high-frequency, low-complexity requests reduces wait times, trims cost per contact, and frees agents for edge cases.
- Start with the top 10 intents (confirm reservation, change dates, check amenities, loyalty balance, invoice copies).
- Use narrow, high-precision flows; route to human agents on ambiguity, emotion, or compliance triggers.
- Track containment rate, first-contact resolution, average handle time, and CSAT by intent, not in aggregate.
- Enable real-time knowledge lookups (property details, fees, policies) and keep content versioned and auditable.
- Record all AI decisions, redact PII, and support easy agent takeover to avoid dead ends.
Search Is Getting Smarter
Chains are testing improved search on brand sites and internal portals. The goal: faster answers in natural language-both for guests and for staff handling policies, amenities, fees, and loyalty rules. Better intent understanding and fresher content beat generic site search every time.
- Centralize property content (amenities, fees, accessibility features, local policies) and expose it via APIs.
- Pair vector search with retrieval-augmented generation so responses cite sources guests and agents can verify.
- Implement feedback loops: click-through, dwell time, and "was this helpful?" to continuously refine results.
Back-Office Automation: Quiet Wins
Behind the scenes, teams report gains in content QA, rate and inventory checks, financial reconciliations, and basic forecasting support. These are low-risk, repetitive tasks where AI assists rather than fully replaces workflows. The catch: few are publishing hard ROI yet.
- Target repetitive reviews (rate parity, content errors, policy mismatches) with human approval gates.
- Use AI to draft, humans to finalize: policy updates, partner emails, and knowledge-base entries.
- Automate exception detection first; automate resolution only after you have six to eight weeks of stable results.
Why Hotel Robots Aren't Common in the U.S.
Service robots show up in pilots, but wide U.S. adoption remains limited. Operators cite unclear payback, guest expectations, elevator integration, building layouts, and safety and accessibility requirements. Maintenance, theft or damage risks, and union considerations also add friction.
Where robots do appear, they're usually scoped to simple, high-density routes-like deliveries on a few floors during specific hours. Most brands are prioritizing software automation that scales across portfolios before making hardware commitments.
Data, Security, and Guardrails Are the Bottlenecks
Executives flagged data infrastructure and security as gating factors. Without clean, accessible property data-and clear rules on what models can access-quality stalls and risk rises. Treat model choice as a detail; governance is the foundation.
- Redact PII automatically in transcripts and logs; restrict access by role and purpose.
- Adopt an AI risk framework, document evaluations, and keep an incident register for model failures.
- Review third-party vendors for data retention, audit trails, and fine-tuning on proprietary data.
Helpful references: the NIST AI Risk Management Framework and PCI Security Standards for payment-related data.
What to Do Next
Operations
- Map your top call and chat intents and define clear escalation rules; no dead ends.
- Keep a single, approved knowledge source for fees, amenities, policies, and promos. Assign owners and SLAs.
- Pilot with one brand or region, then expand by intent, not by department.
IT and Development
- Invest in content pipelines and retrieval. Model swaps are easy; data quality is not.
- Implement adversarial testing, prompt logging, and per-intent analytics dashboards.
- Set spend guards: per-session token caps, cache frequent answers, and prefer retrieval over long prompts.
- Gate any customer-facing model behind policy filters and toxicity/safety checks.
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