The Biggest Mistakes People Make When Getting Started with AI

The most common thing I hear from people who’ve been “trying to get into AI” is some version of: “I’ve opened it a few times, played around, didn’t get much out of it, and kind of gave up.”

They’re not behind. They didn’t fail. They just ran into the same walls almost everyone hits when getting started with AI. And nobody warned them.

I’ve been in some form of education for more than a decade, and watching professionals navigate AI adoption has been one of the most fascinating (and occasionally heartbreaking) things I’ve observed. The technical part? Not the problem. The psychological and behavioral patterns? Those are what derail people, consistently and predictably.

Mistake 1: Waiting until you feel “ready”

You don’t have to feel ready, just start. That’s the first thing I want you to hear.

Most mid-career professionals are used to preparing before they act. You study, you take the training, you get certified, and then you do the thing. That pattern works for most professional development. It does not work for AI.

AI is a do-first, understand-later tool. The understanding literally cannot come through preparation. It comes through use. Every coaching conversation I’ve had confirms this: the people who waited until they understood AI well enough to start are still waiting. They can keep up with the latest buzzwords as they rotate through LinkedIn, but they still haven’t become AI-first practitioners. The people who just opened Claude or ChatGPT and tried something with real work came back the next week with actual questions.

Give yourself permission to be a beginner. You don’t have to have it all figured out. You know who DOES know about what AI can do? The AI itself! Just keep asking while you work, at any fricton point, just ask the LLM itself what’s going on.

Mistake 2: Starting with your most important work

This one is counterintuitive, but bear with me.

When people finally commit to trying AI, they tend to bring it their biggest, most high-stakes task. A client proposal. A presentation for the executive team. A performance review for someone they’re about to put on a PIP. Then the output isn’t quite right (because it never is on the first try), and they conclude that AI doesn’t work for their kind of work.

Start with something that doesn’t matter much. An internal update nobody will scrutinize. A quick recap email to a team member. A brainstorm you’d throw away half of anyway.

I started applying it at work with articles written for a Knowledge Base that hadn’t even been released to customers yet. We were translating our outdated 80-page “how-to” guide with a more searchable, updated knowledge base site filled with articles addressing specific questions. This was, in hindsight, a perfect project to get better at using AI: low stakes, plenty of articles to write, plenty of opportunities to improve the process.

The goal in week one is not to get better output. The goal is to get comfortable with the back-and-forth. Because AI tools like Claude and ChatGPT almost never nail it on the first try. That’s not a bug. The first output is a starting point. The real work (and the real skill) is in the conversation that follows, where you shape and redirect and push back until you get something useful.

Low-stakes practice builds the iteration muscle. Build that first.

Mistake 3: Treating the first response like a finished product

This is probably the single most common reason people decide AI “doesn’t work.”

They type something in, read what comes back, go “that’s not right” or “that sounds like AI wrote it,” and close the tab. What they missed: the first response was never supposed to be the final answer.

When you get output that’s almost right, that’s the moment to lean in, not give up. Try: “That’s close, but the tone is too formal. Make it more direct.” Or: “Good structure, but I need you to cut the second paragraph and sharpen the opening.” Each round gets you closer. Most professionals find that by the third exchange, they have something genuinely useful.

Claude and ChatGPT are not vending machines. They’re more like a new colleague who needs direction. You give direction. They try. You refine the direction. They get better.

Mistake 4: Giving no context (and then being surprised by the generic output)

“Write me a status update that the new project is rolling out next week.”

I understand the instinct: you want AI to just handle it so you don’t have to think about it. The problem is that Claude or ChatGPT has no idea who you are, who you’re writing to, what the project is, what’s actually going on, or what “a status update” means in your organization.

The output you get back reflects exactly how much context you gave: almost none.

Think about it this way. If you were onboarding a smart new hire and you handed them a task, you’d give them background. Who the audience is. What you’re trying to achieve. What tone is appropriate. What to leave out. AI needs the same thing. The more specific and grounded you get with context, the more useful the result.

A better version of that status update prompt might look like: “I’m a project manager at a mid-size consulting firm. I need to write a Friday status update for my VP. The project is two weeks behind due to a data migration issue that wasn’t in scope. My VP doesn’t like excuses, just wants to know what’s happening and what’s next. Can you draft this for me in a direct, professional tone that’s under 200 words?”

That’s not harder. It just requires you to think for 90 seconds before you type. The output difference is significant.

Context hack: ask it to ask you

Here’s the even better version of giving context: don’t try to front-load it all yourself. Have Claude pull it out of you.

State your goal, then tell the LLM to ask you clarifying questions before it writes anything. “I need a Friday status update for my VP. Before you draft it, ask me 3–5 questions you need answered to write it well.” Now Claude names what it doesn’t know. You answer. Then it drafts, grounded in context it actively collected rather than context you tried to predict.

This works because the LLM knows what’s missing better than you do. It’s spent millions of tokens drafting status updates. It knows what makes one land or flop. Let it use that judgment to interview you.

Don’t rush to output. Have a conversation first. The draft gets dramatically better.

Mistake 5: Going it alone

This one is more subtle than the others, but I think it might be the most important.

When you’re getting started with AI on your own, you have no reference point for whether what you’re experiencing is normal. Is this output good? Is it taking me too long? Is everyone else getting better results than this? Am I doing something wrong?

That uncertainty compounds over time. And without community or feedback, most people quietly stop. Not because AI failed them, but because the isolation made the friction feel like personal failure. The most useful advice I keep giving to new users is painfully obvious, but they all need to hear it a few more times: “Just ask Claude.”

Getting that reminder, and that reassurance that there is no secret power move the more experienced users are doing, they are also just asking Claude along the way, is a powerful unblocker. Without it, you might feel “bad at it” or that the tool just isn’t mature enough to get the job done. And it’s easy to walk away from it at that point.

The professionals who are getting the most from AI right now are not doing it alone. They’re showing each other what works. They’re normalizing the friction. They’re bouncing prompts off each other and stealing approaches that would never have occurred to them solo.

You don’t have to figure this out by yourself.

The underlying pattern

These five mistakes are all variations on the same thing: AI adoption is not a knowledge problem. It’s a behavior and psychology problem.

You don’t need a course. You need low-stakes practice with real work, realistic expectations about the first response, a habit of giving context, and people to figure it out alongside.

Start there. You don’t have to be perfect about it.

Where to practice this alongside other professionals

The patterns above are what I see week after week in our community sessions. And watching people break through them in real time, with each other’s support, is one of my favorite things about this work.

If you want to practice getting started with AI alongside other professionals who are navigating the same questions, that’s exactly what we do at MVP Club. No pressure, no performance. Just real work, real iteration, and people who get it.