It’s probably the most common piece of AI advice a founder gives their team: “Go play around with ChatGPT, see what you can do with it.” It sounds reasonable. It costs nothing, it puts no pressure on anyone, and it treats the team like capable adults who can figure things out.
It also doesn’t work, at least not for most of the team. A few people will genuinely go try it, get curious, and start finding real uses. Everyone else will open it once, type something vague, get a mediocre result, and quietly close the tab. Three months later, “we told everyone to try ChatGPT” has produced almost exactly the same one or two AI users the company already had.
Why “Just Try It” Feels Like Good Advice
It’s worth understanding why this advice is so common before explaining why it falls short. It feels like good advice for a few reasons.
It’s easy to give. There’s no plan to build, no workflow to design, no time investment from leadership. Saying “go try ChatGPT” takes ten seconds.
It feels empowering. It treats the team like they don’t need to be told exactly what to do, which is usually the right instinct for capable employees.
It worked for the person giving the advice. The founder or team lead saying this usually did exactly that themselves: opened ChatGPT, experimented, and found something useful. It’s natural to assume the same approach will work for everyone else.
The problem is that the people giving this advice are almost always the same people who were already curious enough to experiment without being told to. That’s not a coincidence. It’s survivorship bias. The advice worked for the kind of person who didn’t really need the advice in the first place.
What “Just Try It” Actually Asks Someone to Do
Open a blank ChatGPT window and tell someone to “try it” and you’re handing them three separate problems at once, without realizing it.
They have to figure out what task is even worth trying it on. They have to figure out how to describe that task well enough to get a useful answer. And they have to figure out whether the answer they got back is actually good, or just good-sounding.
For someone who’s busy, not particularly technical, and not all that curious about AI for its own sake, that’s a real amount of friction for an outcome that isn’t guaranteed to be worth it. Most people, reasonably, decide it’s not worth the time and go back to doing the task the way they already know how to do it.
This isn’t a character flaw. It’s what happens whenever you hand someone an open-ended tool and a vague instruction instead of a specific, low-effort way to get a specific result.
What Happens Instead, in Practice
Tell a team of fifteen people to “try ChatGPT” and here’s roughly what happens. One or two people, the ones already inclined toward this kind of thing, actually dig in and find something useful. A few more open it once, don’t get much out of their first attempt, and don’t try again. The rest never open it at all, because there was never a clear reason to start.
Three months later, AI usage at the company looks exactly like it did before the advice was given: a couple of people using it regularly, everyone else not using it at all. The advice didn’t fail because the team lacked ambition. It failed because “go try it” was never actually a plan.
What Works Instead
The fix isn’t more encouragement. It’s giving people something specific to try, instead of asking them to invent their own starting point.
Instead of “go try ChatGPT,” it’s “next time you write a follow-up email to a lead, use this to get your first draft.” Instead of “see what AI can do,” it’s “use this to summarize your next call instead of writing notes from scratch.” The task is already chosen. The first attempt is already set up to work. All the person has to do is show up and use it, the same way they’d use any other tool that’s already built for the job they’re doing.
That single shift, from an open invitation to a specific, low-effort first task, is usually the difference between AI adoption that sticks and AI adoption that quietly fades after the first week.
The Real Lesson
“Just try it” puts the entire burden of figuring out AI on each individual person. It asks them to find the use case, write the prompt, and judge the result, all on their own time, with no guarantee it’ll be worth the effort.
What actually gets a whole team using AI is the opposite: removing that burden entirely, so trying it isn’t an open-ended experiment but a specific, ready-to-go first win. That’s what WorkLLM is built to provide. Instead of handing your team a blank prompt box and a vague suggestion, WorkLLM gives them ready-made AI Agents for the tasks they already do, so the first time someone uses AI at work, it’s already set up to go well.
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Product-focused founder with deep experience in AI, enterprise software, and data platforms. Passionate about turning complex workplace problems into simple, scalable products.