AI Was Faster. So Why Was It Wearing Her Out?

A plan for writing a process isn’t the process.

That sounds obvious. But it becomes frustrating when you’ve given an AI tool the context, asked it to do the work, and received a confident message saying it’s finished.

Then you look at the result.

It explained what it would do. You still don’t have what you asked for.

I recently talked with someone who had gone out on her own and was using AI to build business processes and work around CRM and data management. She knew her work well. The problem wasn’t a lack of experience.

She described spending a lot of time arguing with the tool and checking whether it had actually completed the task.

I don’t have the exact exchange recorded, but one example I remember involved asking it to write a process. After receiving plenty of context, it returned a plan instead of the completed process. Similar mismatches kept sending her back through the work.

Was it faster than doing everything herself? From her experience, yes.

Was it easier to manage? Not necessarily.

Faster output can create another kind of work

Every result still needs somewhere to go. Someone has to review it, catch what’s missing, and decide what happens next.

If every update pulls you away from what you’re doing, you can spend the day switching tasks instead of finishing them.

For me, switching feels like having to reload the task. What was I trying to accomplish? What had already been checked? Which exception was I working through?

It reminds me of walking into a room to get something and forgetting why I went there. That’s an analogy for the feeling, not a technical explanation of the brain.

The work may be moving faster while my attention is getting pulled apart.

I’ve had to put some boundaries around that. I choose two or three projects for the day and focus on one at a time. If an agent starts a longer task, I may move to the next project.

But when it finishes, I don’t automatically jump back.

The result is ready for review. That doesn’t mean it needs my attention that second.

Review the request, not just the answer

In my own work, I require a separate reviewer using a different model before the main agent can tell me it’s done. Since adding that requirement, I’ve seen a substantial drop in errors.

The reviewer needs the original request and the actual result.

Did we ask for a plan or a usable process? Were the required pieces included? If the agent says it updated something, what evidence shows that it did?

A polished answer can still be the wrong deliverable. Another model agreeing that it looks good isn’t enough.

The review should identify what was verified, what remains unfinished, and what still needs a person’s judgment.

Give the task a finish line

Before handing over a recurring task, get clear about what you expect back.

Name the deliverable. Explain what makes it useful. Give it an example when you can. Decide which decisions still belong to you.

Then give the review a place on your schedule.

The goal isn’t to add another layer of management to your day. It’s to get useful work back without having to pull it apart three times.

If AI is making your work faster but harder to carry, the next step may be improving that handoff.

Bring one recurring task to a 90-Minute Workflow Optimization Session. We’ll clarify what finished means, what the tool should prepare, and where your review belongs.

Getting time back should leave you with more room for judgment, not less.