The Clarity Gap: Why AI Feels Like Extra Work

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For leaders who want AI to help, not add more work.

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If your business only works because you are in the middle of everything, this newsletter helps you build systems so it does not have to.

Now, on to this week.

The complaint I keep hearing

There is a phrase I hear from leaders more than almost any other when the topic of AI comes up.

"It just feels like more work."

They have tried the tools. They have sat through the demos. They have watched the videos promising hours saved. And somewhere between the promise and the practice, something went sideways.

The output was not quite right. The revision took longer than starting from scratch. The time spent prompting felt like time that could have been spent doing.

So they step back. Not because they do not believe AI can help. But because it did not.

Here is what I want to name today: that frustration is valid. But the cause is almost never the tool.

What creates the gap

When AI feels like extra work, there is almost always a gap underneath.

Not a skill gap. Not a tool gap.

A clarity gap.

The clarity gap is the space between what you asked AI to do and what AI actually needed to know to do it well.

Here is how it shows up in real work:

You ask AI to write a client follow up email. You get something generic and slightly off. Why? AI does not know that this client prefers direct language. AI does not know you already had a tense call last week. AI does not know the goal is to save the relationship, not close the sale.

You ask AI to draft a job description. You get something that reads like every other job post online. Why? AI does not know your team culture. AI does not know you need someone who can work independently because you do not have time to manage closely. AI does not know the last hire failed because they needed too much direction.

You ask AI to summarize a meeting. You get a list of topics discussed. Why? AI does not know that only one decision actually mattered. AI does not know who was supposed to own the next step. AI does not know that half the meeting was noise.

In each case, the tool did exactly what it was asked. The problem is what it was not told.

The five types of missing clarity

After watching this pattern repeat across dozens of teams, I have found the clarity gap usually falls into five categories. Knowing which one you are missing makes fixing it much faster.

1. Missing outcome definition

The task has no clear picture of "done." You will know this is the gap when AI produces something technically correct but not useful. The fix is to describe what success looks like before you describe the task itself.

2. Missing context

The task assumes knowledge that only lives in your head. You will know this is the gap when AI gets the tone, audience, or situation wrong. The fix is to share the backstory, even if it feels obvious to you.

3. Missing constraints

The task has unspoken rules you have never written down. You will know this is the gap when AI gives you something that breaks a rule you did not realize you had. The fix is to name what you do not want, not just what you do want.

4. Missing examples

The task has a standard you can recognize but cannot describe. You will know this is the gap when you keep saying "not quite" but cannot explain why. The fix is to show AI a sample of what good looks like, even a rough one.

5. Missing purpose

The task has a reason behind it that changes how it should be done. You will know this is the gap when AI completes the task but misses the point. The fix is to explain why this matters, not just what needs to happen.

Most failed AI attempts are missing at least two of these. Some are missing all five.

A before and after example

Here is what this looks like in practice.

Before (the request that creates rework):

"Write a follow up email to the client about the project delay."

After (the request that gets close on the first try):

"Write a follow up email to John at Riverside Corp about the two week delay on their website project. John is direct and appreciates honesty. He was already frustrated in our last call, so the tone should be calm, confident, and solution focused. The goal is to keep his trust, not just explain the delay. Keep it under 150 words. Do not apologize more than once."

This is not a full prompt engineering lesson. It is a small adjustment that produces a better outcome.

The second request takes 60 seconds longer to write. But it saves 15 minutes of revision and produces something you can actually send.

Taking it further

Once you know what context makes a task work, you do not have to type it every time.

You can save that context inside a Claude Project or a custom GPT in ChatGPT. Now the AI already knows how you write client emails, what tone you prefer, and what mistakes to avoid. The next time you need a follow up email, you give it the situation and it handles the rest.

That is how 60 seconds of clarity becomes permanent leverage.

That is the clarity gap, closed.

The diagnostic you can run this week

Pick one task you have tried to hand to AI that did not go well. Before you try again, run through this checklist.

Ask yourself:

Did I define what "done" looks like? If not, add one sentence describing the outcome.

Did I share the context AI would need? If not, add the backstory, the audience, or the situation.

Did I name what I do not want? If not, add one or two constraints.

Did I show what good looks like? If not, paste in an example, even a rough one.

Did I explain why this matters? If not, add one sentence about the purpose behind the task.

Then give AI the updated request.

You will not get a perfect result. But you will get closer. And more importantly, you will see exactly where the gap was hiding. That feedback is worth more than any prompting trick.

Why this matters beyond AI

Here is the part that surprises people.

When you close the clarity gap for AI, you also close it for your team.

The same missing information that makes AI struggle is the same missing information that causes your team to guess, ask questions, or deliver work that needs correction.

The five categories I listed above apply to every handoff, every delegation, every SOP that does not get followed.

AI just makes the gap visible faster.

So every time you clarify a task for AI, you are also building the system your team can follow. That is how one small fix becomes leverage.

A resource to help

I built a one-page worksheet called the Clarity Gap Diagnostic that walks you through the five categories for any task. You can use it yourself or hand it to your team before they bring AI into a workflow.

Download it below as a bonus with this issue.

We just officially launched January!

Where I am building this live

This month I launched AI Clarity Hub, a private space for owners and leaders who want AI to reduce real work, not add new work.

Inside the hub, we work through exactly what I described today: finding the clarity gaps, building simple systems, and only then adding AI where it actually helps. Members get access to live training sessions, ready to use templates, and a library of AI assistants built for real business workflows.

Thanks for reading,

Andrew Keener
Founder of Keen Alliance & Your Guide at The Logical Box