I do not think the event industry has an AI problem.
I think it has a briefing problem, and in many cases, a human review problem as well. AI can produce useful work for event and marketing teams, but it is not magic. If the objective is vague, the audience is unclear, the source material is thin, or no one checks the output properly, the result will usually reflect that weakness at speed.
I see this often because I work across marketing, event technology and practical implementation at Pink Caviar Events. AI in events can help with ideas, structure, research, content and workflow. I use it myself. But the better question is not “Can AI do this task?” It is “Have we given it enough context to do something useful, and does a human know enough to judge the result?”
AI Is Not the Worker
One of the clearest examples I have seen was not a failure of the tool. It was a failure of process.
Earlier this year, we had a new staff member working on content for an ongoing series. I provided the brief, brand guidelines and the context needed to produce the work to our standard. I also asked her to complete the first piece, send it to me for review, and then continue once we knew the direction was right.
At the end of the day, she came back and said she had finished everything. That surprised me, because the review point had been missed entirely. What had happened was simple. The brief had effectively been put into ChatGPT, and 20 pieces of content had been generated without the review step in between. The work had not been checked properly against the brief, the brand guidelines or the standard we needed.
The giveaway was immediate. The copy was in US English. It did not match the brief. It had been asked to “sound like a blog written by Pink Caviar Events”, which is not the same as understanding the structure, tone, audience or purpose. Even the word count was wrong, despite the output presenting itself as if it had followed the instructions.
The next day, we went through how to use AI more effectively. Not how to avoid it. How to brief it, test it and review it, so it could support the work rather than become an unchecked replacement for judgement. That distinction matters. AI can help with the work, but it should not become the worker without supervision.
The Brief Shapes the Output
The quality of the output usually starts before the prompt. A weak prompt is often just the visible part of a weaker brief. If the team has not agreed on the audience, purpose, tone, source material or approval process, AI will not quietly fix that gap. It will produce something that sounds complete, even when the thinking behind it is not.
That is why I think AI in events needs to be treated as a briefing discipline, not just a tool choice. “Write an event email” is not a useful brief. An email to a senior corporate attendee, a sponsor, an internal stakeholder and a speaker all need different logic. The same applies to a speaker bio, social post, run sheet, agenda summary or post-event report.
There is a big difference between “write an event email” and “write a 150-word email for senior corporate attendees in Australia, using the approved event details below, with a professional but conversational tone, a clear RSVP deadline and no claims beyond the supplied source material.” The second version is still not perfect, but it gives the tool a defined job, a reader, a tone, a format and boundaries a person can review against.
A useful AI brief for event or marketing work should make the important decisions clear before the output is created. At minimum, that usually means:
- Who the content is for
- What the event or task needs to achieve
- What source material must be used
- What tone and language rules apply
- What details must be verified before use
- What format, length or platform constraints matter
- Who needs to review the output before it is used
This is where a lot of AI work goes wrong. People ask for the finished thing before they have clarified what “good” looks like. Then they judge the tool for producing something generic, off-brand or incomplete. In many cases, the tool has simply exposed that the brief was not ready.
Polished Does Not Mean Accurate
One of the dangers with AI is that the output can look finished before it is correct. That is where the risk increases. A rough draft invites review. A polished draft can lower someone’s guard.
We have seen this with speaker bios. In one case, we entered a person’s name, and the tool pulled information from someone else with the same name. We clarified the company and location, but it still mixed details from the wrong person. The result sounded professional, but it was not accurate. For a public-facing event profile, that matters. A speaker bio is not just filler copy. It represents a person’s credibility, role and connection to the event.
We also tested AI to help turn several documents and emails into a run sheet for an event. In some ways, it did a useful job. Dates, times, duties and sequencing were presented clearly.
But the contact details were missing.
That was picked up and fixed before the run sheet was finalised, so it did not become an event-day issue. It did, however, reinforce a simple lesson. For a run sheet being handed to our team and suppliers, missing contact details is not a small omission. On the day of an event, people need to know who to contact, when to contact them and how to reach them quickly.
That is why accuracy is my first concern. Not because AI is useless, but because confident inaccuracy is dangerous. If something is wrong and no one checks it, everything built on top of it becomes weaker. The issue is not only the original mistake. It is the assumption that the output must be right because it reads well.
AI In Events Can Improve the Brief Before It Completes the Task
Used well, AI can do more than produce the first draft. It can help improve the thinking before the first draft exists.
That is where I find it most useful. Before asking AI to write an email, bio, agenda summary or event communication, I can ask it to challenge the brief. What is missing? What would a sponsor need that an attendee would not? What assumptions are we making about the audience? What details would a supplier need before this becomes operationally useful?
This changes the role of AI. Instead of treating it as a shortcut to finished content, it becomes a pressure test for the work. A simple loop can help: draft the brief, ask AI what is missing for the audience or use case, then have a person approve the direction before generating the final output. The human still decides what matters, but the tool can help reveal what has not been considered yet.
Human Review Is the Control Point, Not the Afterthought
This is the part people sometimes want to skip, because the review slows things down. But review is also where the value is protected.
It helps to be clear about what we are actually using. ChatGPT, Gemini, Claude and similar tools are large language models. They are AI, but they are not intelligence in the human sense. They do not understand an event brief the way a strategist, producer or marketer does. They generate likely language based on patterns, which is why the output can sound right even when it is wrong.
That distinction matters. Large language models are, at their core, prediction systems. In simple terms, they generate likely words or phrases based on training patterns and the context they are given. IBM describes this as large language models working like statistical prediction machines, repeatedly predicting the next word in a sequence. That is impressive technology, but it also explains why a speaker bio can sound polished while pulling from the wrong person, or why a run sheet can look organised while missing information the team needs on the day.
That is why human review is not a nice final polish. It is the control point. Someone still needs to check whether the facts are correct, whether the tone is appropriate, whether private or sensitive information has been handled properly, and whether the output is actually useful for the job it is meant to support.
Yes, review takes time. But so does fixing the wrong speaker bio, correcting a run sheet after suppliers have received it or rewriting 20 pieces of content that were never checked properly. In practical terms, AI in events only saves time when the output is still checked against the brief and the audience and the operational details.
This is not just a small-team concern. McKinsey’s 2025 work on AI adoption points to the same broader issue: organisations do not capture value from AI simply by giving people access to tools. Value comes when workflows, governance and risk controls are designed around how the tools are actually used, not when AI is treated as a shortcut around process.
Better AI Starts Before the Prompt
I often come back to a simple view: “Technology for the sake of technology is meaningless… unless it is understood and implemented correctly.” AI is no different. Used poorly, it can make weak thinking look polished. Used properly, it can help teams move faster, question assumptions and improve the work before it reaches an audience, supplier or stakeholder.
The future of AI in event work is not only about better tools. It is about better questions, better source material, clearer briefs and stronger human review. The event industry does not need to fear AI, but we do need to stop treating it as if it understands the job by default. Better output starts earlier than the prompt. It starts with the brief, and it only becomes useful when a person still knows how to judge it.
Explore AI in events further at the Future of Events Summit, where event professionals, marketers and business leaders will come together to examine how better briefs, clearer judgement and practical technology use can shape more meaningful, measurable and memorable live experiences. Book your ticket and continue the conversation.
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Dimitri Cassimatis works across marketing, event technology and practical implementation at Pink Caviar Events. He brings a grounded perspective on AI, event systems and the human judgment needed to turn technology into useful, accurate and audience-ready event outcomes.