AI matchmaking

The practical difference between AI matchmaking and older rules-based systems is that AI systems learn from behaviour rather than relying only on what people typed into a form. Someone might tag themselves as interested in "logistics" but spend all their time viewing packaging exhibitors; a learning system picks that up and adjusts. For organizers, this matters because self-reported profiles are often thin, rushed, or aspirational, and behaviour tends to be more honest. In use, AI matchmaking works best when there's enough activity to learn from: a large event, an engaged audience, or several editions of historical data. On a first-year event with 300 registrants and sparse profiles, an AI layer has little signal to work with and may perform no better than sensible rules. That's the honest nuance vendors rarely volunteer. The common buying mistake is asking "does it use AI?" instead of "what data does it learn from, and how much of that data will my event actually generate?" A well-tuned rules engine with good registration data beats a starved model. Ask vendors to show recommendation quality on an event of your size and profile, not on their flagship customer's numbers.