Applied AI
A Third of People Don't Check What AI Gives Them. In Events, That Ends Up in a Proposal.
A US survey out this week found over a third of workers rarely check AI output before using it. Events is a copy-paste industry under deadline, which makes this a proposal problem, not an abstract one.
The number
A survey published this week, Resume Now's "AI Oversight Gap", found that 35% of US workers rarely or only occasionally review AI-generated output before they use it. That is not a fringe habit. That is roughly a third of the people using these tools, sending or filing or presenting whatever the model handed back, largely unread.
I would not treat a US consumer survey as gospel for the UK events industry specifically. But it matches something I hear in almost every Discovery Lab: people know, in the abstract, that they should check AI output. Under a Thursday afternoon deadline, with three other things also due, that knowledge does not always survive contact with the actual week.
The cautionary tale, and why it is more useful than it looks
Back in February, a Nebraska attorney filed a divorce appeal brief with 63 case citations. Fifty seven of them were defective. Twenty were entirely hallucinated, cases that do not exist, invented by an AI tool and never checked before they went into a legal filing. The attorney has since been suspended pending a disciplinary investigation.
It is tempting to read that story and think "well, that is law, the stakes are obviously higher, that would never happen here." I would push back gently on that. The mechanism is identical to ours. Someone under deadline pressure, working with a tool that sounds confident regardless of whether it is right, trusted the output because checking it felt like it would cost time they did not have. The domain is different. The habit is the same one a third of workers admit to.
Why events is a particularly copy-paste industry
Most events work is assembled fast, under deadline, from parts. A proposal draws on a template, a past quote, a supplier's spec sheet, a venue's capacity chart, and increasingly a paragraph an AI tool wrote to fill a gap because nobody had time to write it themselves. Sponsor decks do the same. So do delegate FAQs, supplier comparison documents, and the summary paragraph at the top of a report that nobody reads closely because it is "just the intro."
An unchecked AI paragraph dropped into a proposal is the modern version of the wrong client name still sitting in the template from the last job. It used to be a find-and-replace slip. Now it can be a venue capacity that is subtly wrong, a supplier claim the AI generated with confidence rather than sourced, or a number that sounds plausible and is not close to accurate. The failure mode has not changed. What has changed is how confident the wrong version sounds, and how much of the document it can now touch at once.
A forty-person agency in the Midlands had exactly this happen earlier this year. A venue capacity generated as a placeholder in a first draft, never replaced with the real figure, went out in a proposal to a client who happened to know the venue well. The client caught it in about four seconds. The agency spent considerably longer explaining it.
The three things worth actually checking
Nobody has time to re-verify an entire document line by line under deadline, and telling people to do that is how the advice gets ignored. A shorter, honest list survives contact with a Thursday afternoon better than a long one does.
- Numbers. Capacities, prices, dates, headcounts, percentages. Anything with a digit in it should be traceable to a source you can point to, not just "that sounded about right."
- Names. Client names, venue names, supplier names, job titles. These are the easiest things for an AI tool to blend or misremember from context, and the fastest thing a client will notice if wrong.
- Anything the client could check in ten seconds. A claim about their own industry, their own previous event, or a fact about their own sector. If they could disprove it with one search, assume they might.
That is a three-line habit, not a process. It fits on a sticky note. It is also, not coincidentally, close to what a careful proofreader would have checked before AI tools existed at all. The tool changed how fast a paragraph gets written. It did not change what is worth checking in it.
Who signs off what
The other gap I see is not the checking habit itself, it is that nobody owns it. If everyone is "supposed to check," in practice the person under the most deadline pressure is the one least likely to, and the document goes out anyway because someone assumed someone else already looked at it.
A cheap fix: name who signs off numbers and who signs off names, per document type, so it is not a diffuse responsibility that quietly belongs to nobody. A proposal does not need a committee. It needs one named person whose job it is to read the numbers before it leaves the building, even when, especially when, they did not write the paragraph themselves.
The sentence to watch for
"We always check that" is the sentence people say right before they describe the one time they didn't. It is not said dishonestly. It is said because checking used to be the default, back when writing was slow enough that reading it back happened almost by accident. AI tools removed that accidental read-through. If checking is going to keep happening, it now has to be a deliberate step, not a side effect of how slowly the document used to get written.
This week
Pick one document type your business sends out often, a standard proposal template is a good place to start, and write down, in one line, who checks the numbers and who checks the names before it goes out. Not a policy document. One line, pinned somewhere the team will actually see it. If you want help finding where else in the business this kind of unchecked step is already sitting, that is the sort of thing a Discovery Lab is built to find.