Operations Note 02 · using AI to find the real cause, not the nearest excuse
Don’t Fix the Symptom
“Proposals go out too slowly” is an observation. It tells you nothing about what to change. Here’s how to use AI to get from a symptom to a cause, and how to keep it honest along the way.
The idea in one line: AI is good at generating an explanation that sounds right. That’s not the same as generating one that is right. Treat every cause it gives you as a hypothesis to test, not an answer to act on.
An observation isn’t a diagnosis
Once Note 01 gets you to a real constraint, the instinct is to fix it immediately. Resist that for one more step. “Proposals take three days to reach the customer” describes what’s happening. It doesn’t say why, and fixing what’s happening without knowing why usually just moves the delay somewhere else.
The tool for closing that gap is old and simple: keep asking why. It doesn’t need a name or a framework to be useful. It just needs discipline. AI turns out to be a strong partner for this, not just because it won’t get tired or defensive four questions in, but because it can hold the emails, the CRM, the invoices, and the meeting notes in view at once and check whether they’re actually telling the same story.
Keep asking until the answer changes shape
Keep going until the explanation moves past the immediate task, to the rule, decision, ownership gap, or capacity limit actually causing it. That’s usually the point worth acting on.
The problem stopped being “proposals are slow” three questions in. It became “the business has no rule for when a pricing decision needs the owner, so every decision defaults to needing the owner.” That’s a fixable thing. “Be faster at proposals” isn’t.
Don’t trust the first answer
AI will give you a root cause on request. That’s exactly the problem: it will give you one whether or not the evidence supports it. If you ask “why is this happening” without pushing further, you get a plausible story, not a verified one. Push on every answer the same way you’d push on a person’s guess.
Questions that test it
- “What evidence actually supports that?”
- “What would we expect to see if this were true?”
- “What else could explain the same pattern?”
- “What’s missing that would help us be sure?”
Questions that just confirm it
- “Does that sound right to you?”
- “Can you explain that more?”
- “What else should I know?”
- Accepting the first answer because it sounds specific
A good diagnosis separates three things clearly: what the evidence actually shows, what you’re inferring from it, and what you still don’t know. If AI collapses those into one confident paragraph, ask it to pull them back apart.
Run it yourself
- “Here’s the problem we found: [describe it]. Ask ‘why’ repeatedly, using only the evidence I give you at each step, until the explanation moves from a task to a rule, a decision, an ownership gap, or a real capacity limit.”
- “At each step, separate what the evidence shows, what you’re inferring, and what’s still unknown.”
- “Don’t call anything a root cause unless you can point to the evidence for it. If you can’t, say so.”
Finding the real cause changes what “fixing it” even means. Note 03 covers the part most AI conversations skip entirely: choosing what kind of fix actually fits, before choosing a tool.
Catalyst Studio · Operations Notes