I keep running into the same problem with AI.

The output is not useless. That would be easier.

The harder problem is when it is close.

It has the right shape. It uses the right words. It sounds reasonable. It may even be mostly accurate.

But I still know I would not use it.

That is the tough part, isn’t it?

At what point is good enough actually good enough? Is it saying what I would say, or did I let AI take over too much of the thinking?

That is the part I keep running into. I will read something that sounds good, maybe even better than the rough version I started with, and still have to stop myself and say, no, I would not really say that.

Not because it is bad.

Because it is not mine.

That is where the real work is.

Close is harder than wrong

When AI is wrong, I know what to do with it.

I throw it out. I correct it. I ask again. I change the source. I change the prompt. Whatever.

The close answer is harder.

It can look finished. It can sound better than the messy thought I started with. It can give me enough structure to feel like the work is mostly done.

But then I read it again and something is off.

Maybe it is too broad. Maybe it is too polished. Maybe it answered the topic, but not the actual point. Maybe it turned something practical into something that sounds like every other article on the internet.

That happens a lot with writing.

AI can take a rough idea and make it cleaner. Sometimes that is exactly what I need. Other times, the cleaning is the problem.

The sentence gets better, but the point gets weaker.

The article gets smoother, but it loses the part that made me want to write it.

That is not a small issue. If the output is close enough to pass at first glance, I need to know what standard it failed.

“Make it better” is not enough

I still care about prompts.

A clearer prompt helps. Better context helps. Better examples help. A better model can help too.

But I am running into a different problem now.

I cannot just tell AI to make something better if I have not defined what better means.

Better for who?

Better in what way?

More direct? More useful? More accurate? More specific? More like me? More helpful to the reader? More grounded in actual work?

Those are not the same instruction.

If I say “make this stronger,” AI may make it more polished.

But stronger does not always mean polished.

Sometimes stronger means shorter.

Sometimes it means more specific.

Sometimes it means less careful.

Sometimes it means taking out the sentence that sounds smart but does not add anything.

Sometimes it means leaving in the rougher line because that is the line with the actual point.

That is the part AI keeps forcing me to confront.

A lot of my standards are still living in my head.

I know when something sounds wrong. I know when something is too generic. I know when a recommendation is technically fine but not useful. I know when a sentence sounds like something I would never say.

But knowing it in my head is not the same as giving AI a standard it can work against.

AI is making me define what I reject

This is where the work is changing for me.

I am getting more specific about what I reject.

For writing, I reject copy that sounds like it is trying too hard to be thought leadership. I reject padded openings. I reject sentences that feel like they were written to sound important instead of saying something clearly. I reject articles that explain a category but never make a point.

For research, I reject summaries that collect information but do not tell me what matters. I reject source piles. I reject answers that treat every point like it has the same weight. I reject anything that sounds certain when the evidence is thin.

For strategy work, I reject recommendations that are reasonable but not actionable. I reject “you should consider” language when the work needs a decision. I reject plans that sound good until you imagine a real team trying to do them.

For competitive work, I reject feature comparison theater. I do not need a table that says everyone has everything. I need to know what a buyer would actually care about, where the pressure is, and what claim we can defend.

That is what I mean by defining the standard.

It is not just telling AI what to create.

It is telling AI what not to get away with.

The closer the output gets, the more the standard matters

Bad output does not teach me much.

Close output does.

Close output forces the question.

What exactly is wrong here?

That question is annoying, but it is useful.

If I say the writing is too generic, I need to know what generic means in that case.

Is it using words nobody would say in a normal conversation?

Is it making a point everyone already agrees with?

Is it hiding behind big language?

Is it avoiding the actual opinion?

Is it writing about the topic instead of writing to the reader?

Is it smoothing out the part that made the idea worth saying?

The more specific I get, the better the next pass gets.

Not perfect. Not magic. But better.

That is the part I think gets missed in a lot of AI conversations. The prompt is not just a command. It is a transfer of judgment.

If the judgment is vague, the output will be vague.

If the standard is generic, the output will be generic.

AI does not always need more instruction. Sometimes it needs a clearer definition of what good work is supposed to feel like.

This changes how I use AI agents

This is also changing how I use multiple agents.

At first, it is easy to think more agents means better work.

Sometimes it does.

But more agents can also create more close-but-not-quite output. More angles. More comments. More valid suggestions. More things to sort through.

So I am getting more intentional about the jobs I give them.

I do not just want an agent to review the work.

I want it to review against a standard.

Tell me where this sounds generic.

Tell me where this loses the audience.

Tell me where this is accurate but not useful.

Tell me where I am making a point that is too safe.

Tell me where this sounds like AI cleaned the life out of it.

That kind of review is more useful because it is not just feedback. It is feedback against something I care about.

And even then, I do not take every suggestion.

The agents are not in charge.

They help me see the work from more than one angle. I still decide what survives.

This is not just a writing problem

Writing makes this easy to see, but I do not think this is only about writing.

This shows up anywhere AI is used for work that requires judgment.

A sales team using AI to draft account research still needs a standard for what matters.

A product team using AI to summarize customer feedback still needs a standard for what counts as signal.

A security team using AI to review risk still needs a standard for what deserves escalation.

A leader using AI for strategy still needs a standard for what makes a recommendation useful enough to act on.

Without that, AI can produce a lot of work that looks good but does not move anything forward.

That is one of the risks I see with AI adoption right now.

Access is spreading faster than standards.

People have the tools. They have the licenses. They have the assistants inside the software they already use.

But the harder question is still there.

What does good look like?

Who decides?

What gets rejected?

What needs human judgment?

What is good enough for a draft, and what is good enough to make a decision?

Those questions are not as exciting as a new model release, but they matter more in the work I actually care about.

Where I am right now

I am still working through this.

I do not have a perfect system. Some prompts are still bad. Some review steps are too much. Some agents sound smart and add nothing. Some outputs still miss the point completely.

But I am clearer on what I am trying to improve.

I am not just trying to get faster output.

I am trying to get clearer about the work.

What I value. What I reject. What I trust. What I would actually use. What I am willing to put my name on.

That is where AI is becoming useful to me in a way that feels more durable.

It is not just helping me produce more.

It is forcing me to be more specific.

And honestly, that may be the more important part.