How to use AI for event networking: three concrete use cases

Every event organiser I speak to right now is being pitched an AI tool. The decks are similar: a hero number, a vague promise about engagement, an annual contract on the last slide. Most of the products underneath that wrapping are doing one useful job badly. A few are doing one useful job well. None of them are the all-singing platform the brochure suggests.

I think the right way to cut through this is to forget the word "platform" for a minute and ask a simpler question: what jobs at an event genuinely benefit from AI, and which can I pilot on a single event without locking myself in for a year? My take, after working through this with organisers running everything from 200-person association meetings to 8,000-pax conferences, is that there are three real use cases - and one rule that decides whether any of them work.

What organisers actually want from AI at events

Most organisers do not want "AI". They want to spend less time chasing registration data, fewer evenings sweating over a seating plan and more confidence that what they put on stage matches what their audience came for. That is the bar.

It is also a higher bar than it sounds. 60% of event teams do not actively manage networking at their own events (Freeman, 2025)- which means buying a tool to "do AI networking" without first deciding whether networking matters to your event will produce a clean, expensive nothing. AI in events behaves like AI everywhere else: it is a force-multiplier on what you already do, not a substitute for caring.

Across the McKinsey 2025 State of AI sample, 78% of organisations now use AI in at least one function (McKinsey, 2025) - so the question is not whether to use it. It is which use cases reward you for piloting and which punish you for trusting the brochure. Three pass that test for events.

Use case 1: turning registration data into an audience brief

This is the most underrated AI use case at events, and the one I would start with if you have budget for a single pilot.

Every event collects registration data. Almost no event reads it back at the audience level. AI changes the maths. A tool that ingests free-text answers - what people came for, what they want to learn, what they can contribute - can produce a one-page audience brief in twenty minutes that used to take a smart analyst two days. Topic demand. Seniority mix. Sector clusters. Sponsor-relevant cohorts. Unmet needs the agenda has not addressed.

Why does that matter now? Because 49% of UK information and communication workers were hybrid in Q1 2025, up from around 10% in 2021 (UK ONS, 2025). The senior knowledge-worker cohort that pays for conference tickets now sees their colleagues less than they see industry events. They expect the room to know who is in it. A registration form that asks the right four questions - and an AI tool that can read the answers as a body of evidence rather than a spreadsheet - is the difference between an event that feels personalised and one that feels generic. The form does the heavy lifting; the AI just makes the heavy lifting fast. There is more on what your registration form is really doing if this is the lever you start with.

Use case 2: pre-event matches with explanations

This is the use case most organisers have heard of - and most do badly.

The good version: 24 to 48 hours before the event, every attendee receives three named introductions, each with a one-line explanation of why this person is worth meeting and a suggested conversation opener. An attendee who arrives with three named matches and a one-line reason for each does not need a second drink to start a conversation. That is the entire mechanism. It works because it removes the social tax of approaching a stranger from cold.

The bad version: an in-app directory of 400 attendees with a "request a meeting" button and a push notification on the morning of the event. That is a search problem, not a matching one, and it shifts the work to the attendee. Adoption collapses below 20% almost every time. If you only remember one thing about this use case, it is that the explanation is the product. Without "why this person, why now", a match is just a name. With it, the conversation has already half-started before the room opens.

For the mechanics underneath - how the match is generated, what signals it weighs and why directional explanations matter - here is how AI matchmaking actually works. The short answer is that the algorithm only knows what you asked at registration. The form is the moat.

Use case 3: post-event measurement in 24 hours

The third use case is the one organisers ask for and almost no one delivers cleanly.

After the event, the senior team wants a one-page answer to four questions: did this audience get what they came for, what did they ask for that we did not provide, who would they pay to meet next time and is this event worth running again at this format. AI is genuinely valuable here because the input is messy - free-text feedback, post-event survey responses, in-app reactions, sponsor debrief notes - and a model that can cluster and summarise this kind of qualitative data in hours rather than weeks turns a slow process into a same-week one.

The most valuable post-event AI output is not a personalised follow-up email - it is a one-page audience brief in the organiser's inbox by 9am the next morning. That brief is what your sponsors actually want and almost never get. It is also what justifies next year's pricing conversation. If the tool you are evaluating cannot produce something like this, what you are buying is a survey tool with extra branding - and there is more on the metrics that matter post-event before you sign anything.

How to pilot without locking in

Most AI event tools are sold on twelve-month contracts. Most of those contracts are signed before the buyer has seen the tool work in their context. 30% of generative AI projects are abandoned after proof-of-concept by the end of 2025 (Gartner, 2024) - usually because the pilot was structured around the tool, not the outcome. You can avoid being one of them with a five-step pilot.

One use case, not three. Pick the registration brief, the matching layer or the post-event summary. Make the vendor sell you one thing at a time. If they refuse, that is the data point.

Three numbers, agreed in writing. Adoption rate (the percentage of attendees who view their match content), match relevance (a one-question 0-10 score in the post-event survey) and organiser time saved (hours you did not have to spend on this job). Write these on the SOW before you sign.

One event, not a year. Pilot on a single event with at least 80 attendees. Smaller and a manual introduction round still wins; larger and the pilot teaches you about scale at the same time as the tool.

Test the export inside the pilot. Ask for a clean export of attendee profiles, registration responses, match data and feedback in CSV or JSON during the trial. Two hours of testing the export is the cheapest insurance against vendor lock-in you will ever buy. Most lock-in at events is not legal - it is logistical. Once your attendee history lives in a vendor's database, leaving costs more than staying.

Decide on evidence, not enthusiasm. After the event, look at the three numbers. If adoption was below 30% and the relevance score was below 7 out of 10, the tool did not earn the annual contract. Renew on results, not on the vendor's roadmap deck. The free version of this conversation - the networking gap calculator - will give you a baseline for adoption before you start, which is what makes the post-event comparison meaningful.

The human-before-tech rule

AI is the kitchen tool. The conversation is the meal.

Every use case in this post earns its keep by giving an attendee a better experience of the room - knowing who is there, knowing why they should meet a particular person, leaving with a sense the event understood why they came. None of that is about the model. It is about whether you, as the organiser, are willing to design the event around the human moments and treat AI as the thing that makes those moments scale.

That is also the test for any vendor pitch. If a tool can describe what your attendees will actually feel, in plain language, before it describes its model architecture, it is probably worth a pilot. If it can only describe its model architecture, you are not the customer - you are the case study they want to write next quarter. If you want to see what we have built into All Along - and how the three use cases above sit inside one matching platform without the lock-in - that is a good place to start.