Why Most People Use AI Wrong (And the Fix Is Simpler Than You Think)

The most common complaint about AI tools from executives who have tried them is some version of: the outputs are generic, it does not really understand my situation, and it is not saving me as much time as I expected. If that sounds familiar, the problem is almost certainly not the tool. It is the pattern of use.

The Core Mistake: Treating AI Like a Search Engine

Most people approach AI the way they approach Google. They type a question, read the answer, and move on. That produces Google-quality output: general, surface-level, and not specific to your situation.

AI language models are not search engines. They do not retrieve information from a database. They generate responses based on what you give them. The more context you provide, the more specific and useful the response. If you ask a vague question, you get a vague answer. That is not a flaw in the model. It is a predictable result of how you used it.

What Good Use Actually Looks Like

Compare these two prompts:

• Bad: 'Write a memo about our new remote work policy.'
• Good: 'I am the COO of a 200-person professional services firm. We are moving from fully remote to a three-day-in-office requirement starting in June. There is meaningful resistance from senior individual contributors who joined during the pandemic. Write a memo that acknowledges that tension directly, explains the business rationale without being defensive, and gives people a clear path to raise concerns. Tone should be direct but not corporate.'

The second prompt takes 45 extra seconds to write. The output is not in the same category as what the first prompt produces. This is the entire game: giving Claude enough context to actually understand your situation.

The Three Things That Almost Always Fix Mediocre Output

More context about the situation

Who is involved, what is the history, what constraints exist, what has already been tried. Most people give Claude the headline and wonder why the response does not go deep enough.

A description of the audience

Who is this for? What do they already know? What do they care about? What would make them skeptical? The more specifically you describe the reader or listener, the more targeted the output.

An example of what good looks like

If you have a previous memo, email, or document that hit the right tone, paste it in. Tell Claude: this is the kind of thing I am going for. It will calibrate to your style far more accurately than any description of voice.

The Iteration Habit

The second most common mistake is treating the first response as the final response. Almost no one who gets good results from AI accepts the first draft. They push back. They ask for changes. They say: this paragraph is too formal, make the opening stronger, remove the bullet points and write it as prose.

Think of the first response as a starting point, not a deliverable. The real value of AI is in the iteration. Each round gets closer to what you actually need, and the total time is still a fraction of doing it yourself from scratch.

How Long Before This Feels Natural

Most executives we work with feel a noticeable difference within the first two weeks of deliberate practice. Not because they have learned some complex system, but because they have stopped asking vague questions and started giving Claude enough to work with.

The ceiling on what you can get from these tools is much higher than most people discover on their own. The floor, if you use them like a search engine, is exactly as unimpressive as you have probably experienced.

The output quality of AI is almost entirely determined by input quality. Invest 60 more seconds in the prompt. The return is not proportional.

Work with Maya

Ready to build real AI fluency?

Book a free 20-minute intro call with Maya. No prep needed — just a conversation about where you are and where you want to be.