Pattern-matching is the strength. Anything that has been done many times before, in formats that exist in training data, is something an LLM does well. Anything that requires fresh facts, precise arithmetic, or judgment beyond what a written record contains, an LLM does badly. The trick is knowing which side of that line your use case falls on before you commit budget to a deployment.
Good at
Bad at
The honest mid-zone
Sentiment analysis on reviews, conversational guest queries that need a real answer, recommendation engines for upsells — these are areas where LLMs are very good but not perfect. About 85-92% accuracy in production. That is great for assistance but not enough for full automation. Most successful hotel AI deployments in 2026 use LLMs as a draft layer that a human reviews before sending, not as a fully autonomous agent.