You have been working on this for weeks. Maybe months. You fix one thing and another version of the same problem shows up somewhere else. The team is frustrated. You are frustrated. And somewhere in the back of your mind, a quiet question keeps surfacing: are we even solving the right thing?

That question is worth listening to. Because in most cases, the answer is no.

The problem you are working on is usually a symptom. The real problem is one or two levels deeper — and it is almost impossible to see clearly when you are inside it. This is not a failure of intelligence. It is a structural problem with how human beings process complexity under pressure. When something is urgent and visible, we move toward it. We solve what is in front of us. And we do it again and again until we notice that nothing is actually getting better.

What Problem Framing Actually Is

Problem framing is the discipline of defining the problem correctly before you try to solve it. It sounds obvious. Almost no one does it consistently. The reason is simple: it requires slowing down when everything in your environment is telling you to speed up.

AI changes this equation. Not because AI is smarter than you — it is not. But because AI has no urgency. It has no stake in the outcome. It has no ego attached to the current solution. When you describe a problem to AI and ask it to help you frame it before you try to fix it, you get something that is almost impossible to get from a human colleague in the middle of a live situation: genuine, disinterested perspective.

"The most expensive mistake a leader can make is solving the wrong problem with excellence."

The Four-Step Play

This is Play 01 from Capacity, UnLocked. Here is how to run it.

  1. 1
    Describe the situation — what you are seeing, not your interpretation of it. The facts. The behaviors. The patterns. Not the story you have built around them.
  2. 2
    Let AI surface the root causes, hidden assumptions, and related problems. Ask it to give you five possible root causes beneath what you described. Ask it to name the assumptions you might be making.
  3. 3
    Reframe the problem statement based on what you learn. This is the step most people skip. Write a new version of the problem — one sentence — that reflects the root cause, not the symptom.
  4. 4
    Apply your judgment to the right problem. Now you are solving something real. The solution space looks completely different from here.

The Prompt That Changes Everything

Here is the exact prompt from the playbook. Copy it, paste it into ChatGPT, Claude, or Gemini, and fill in the brackets.

Root Cause Excavation Prompt

"Here is the problem I'm trying to solve: [describe it]. Before I try to fix it, I want to make sure I'm solving the right thing. Help me: identify the five most likely root causes beneath what I described, surface any hidden assumptions I might be making, and reframe the problem in a way that might be more accurate or more solvable. Then tell me which version of this problem would be most worth my time to actually work on."

Run this before your next problem-solving session. Before the next team meeting where you are about to align on a solution. Before you write the proposal, the plan, the strategy. Spend ten minutes here first. The downstream time savings are not small.

The Pattern That Keeps Showing Up

When leaders run this play consistently, a pattern emerges. The problems that felt like communication issues turn out to be clarity issues. The performance problems turn out to be expectation-setting problems. The morale problems turn out to be workload design problems. The same symptom, over and over, pointing to a different root cause every time — and a completely different solution.

You cannot see that from inside the problem. AI gives you the outside view without requiring you to leave the room.

That is what problem framing is for. And that is why it is the first play in the playbook.