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

Hey,

If this is your first time here, welcome! The Logical Box is a weekly newsletter for owners and leaders who want AI to reduce real work, not add new work. Each issue focuses on one idea: see where work breaks down, fix the clarity first, then add AI where it actually helps.

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.

They die from unanswered questions that nobody wanted to ask out loud.

Who owns this? What does success look like? How do we know when it is done?

If your team has been "testing AI" for a while now and you cannot point to a real change in how work gets done, you are probably stuck in what researchers have started calling pilot purgatory. And you are not alone.

According to McKinsey, 88% of organizations now use AI in at least one business function. But most of them are not getting measurable returns. The technology is running. The results are not coming.

That number is striking because it means the adoption problem has largely been solved. Leaders bought the tools. Teams are using them. And yet the work has not changed in any meaningful way.

The problem was never the tool.

It usually starts with genuine momentum. Someone tries a new AI tool, gets excited, demos it in a meeting. Leadership approves a small budget. A few people start using it.

Then six months pass.

The tool is still running. People still use it sometimes. But if you asked the team lead to show you what changed in their workflow, they would pause. If you asked who owns the results, you would get a name that changes depending on who you ask. If you asked what "done" looks like for this initiative, the answer would be some version of "we are still figuring that out."

That pause is pilot purgatory.

Work is moving, but no one can tell you where it is going or how they will know when it arrives.

Pilots fail to land for one consistent reason: the definition of success comes after the tool, not before it.

A team decides to use AI to speed up client reporting. Nobody asks what "faster" means in numbers. Nobody assigns an owner for the output. Nobody writes down what the finished report should include. The AI runs. Reports get produced. But the manager still reviews everything, the client still requests changes, and the time saved on drafts gets eaten up in corrections.

The tool did its job. The work around the tool was never defined.

This is the same problem as every other clarity failure we have talked about this month. Unclear ownership creates stalls. Undefined "done" creates endless loops. AI does not fix either of those things. It just moves faster inside them.

There is a budget cost, which is obvious. But the less visible cost is organizational fatigue.

When a pilot runs without a clear outcome, the people involved stop believing in the initiative. They keep using the tool because it is expected, not because it is helping. The skeptics on the team feel like they were right all along. Leadership starts questioning whether AI is worth the investment at all.

The real damage is not the wasted subscription fee. It is the erosion of trust in what is possible.

When the next initiative comes around, your best people will engage with less energy than they did the first time. That pattern compounds.

Pick one AI pilot that has been running in your business or your team for more than 60 days.

Ask three questions about it:

One. What does success look like in specific terms? Not "it saves time." How many hours, on what task, measured how often?

Two. Who owns the outcome? Not who uses the tool. Who is accountable for whether the pilot is working?

Three. What does done look like? What is the condition under which you would say this is working and ready to scale, or not working and ready to stop?

If you cannot answer all three, the pilot has not started yet. You have been running the setup, not the experiment.

Write the answers down this week. A paragraph is enough. Then share it with whoever owns the initiative.

That one document will do more for your AI results than any new tool you could add.

AI should not be running in any workflow where the team cannot explain in plain language what the output is supposed to be and who reviews it before it moves forward.

If those two things are not clear, you do not have an AI problem. You have a workflow clarity problem. Solve that first.

If someone asked your team to prove that your current AI pilot is working, what would they show?

If the answer requires more than a minute to pull together, you do not have a clear enough definition of success yet.

This is the last issue in February's theme on ownership and done. Next month, we move into repeat work and the hidden busywork that drains teams without anyone noticing.

If you want help mapping out one of your stalled pilots and turning it into a system with a real owner and a real finish line, that is exactly the kind of work we do inside AI Clarity Hub. You can find us on the link below.

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.

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

Andrew Keener helps leaders see where work breaks down so AI creates value instead of confusion. He works full time at NASA in knowledge management and workflow systems and leads internal AI training. His consulting work targeting all AI is outside of the NASA space is through his company Keen Alliance Consulting.