Large language model (LLM)

LLMs are why AI in events stopped being a conference slide and started being a tool. For organizers, they show up in two ways. First, as general assistants: drafting exhibitor emails, summarizing a hundred pages of survey feedback, rewriting session descriptions. Second, and more interestingly, connected to your event data — where they let anyone on the team ask questions in plain language ("which registered visitors this edition work in cold-chain logistics and haven't booked any meetings?") and get answers that used to require a data analyst and a day. That second use is the important one, and it comes with the important caveat: an LLM on its own knows nothing about your show. It's trained on the public internet, not your registration list, so the value depends entirely on how well it's connected to your actual, unified data. LLMs also have a known failure mode — stating wrong things fluently and confidently, called hallucination — which is why well-built event tools ground every answer in your records and show where the answer came from. The common mistake is the opposite one: pasting attendee or exhibitor data into a public chatbot without checking your data-protection obligations first.