AI Diagnostics

Before diagnosing anything, build a complete picture of the situation. Skipping this step means you risk running a root-cause analysis on the wrong symptom entirely. Run through all six questions below before moving to root-cause analysis. WHO — Who is affected? Which users or teams are experiencing the problem? Is it everyone, or specific groups only? Who reported it first and when? Who owns the system — technically and operationally? Who are the downstream recipients of the wrong outputs? WHAT — What exactly is happening? ...

' The first answer is almost never the real cause. Most organizations stop at Why 2 or Why 3 and fix the symptom instead of the source. Run this drill separately for each distinct symptom you identified during problem scoping. Your Root Cause Drill Symptom: _________________________________ Why Question Answer Why 1 Why is this happening? Why 2 Why is that happening? Why 3 Why is that happening? Why 4 Why is that happening? Why 5 Why is that happening? True root cause: _________________________________ ...

Before approving budget or developer time to fix a failing AI model, score the proposed solution across these four operational dimensions. Rate each category from 1 (Severe Friction / High Risk) to 5 (Seamless / High Return). 1. Desirability — Trust Quotient Adoption risk: Users will embrace this fix — previous model instability has not burned trust beyond repair. If trust is damaged, map a change-management step before scoring higher than 2. ...

Diagnose before you retrain. The most expensive fix in AI is the one that solves the wrong problem. Use this framework every time someone says “the model is wrong” — before agreeing to any fix. How to use this framework Run the three layers in sequence. Do not skip ahead. Layer 1 — Horizontal scan: Map the full problem landscape before going deep. Layer 2 — 5 Whys drill: Find the true root cause for each distinct symptom. Layer 3 — Design Thinking check: Confirm your fix will actually be adopted. Critical rule: Complete all three layers before recommending any fix. The most expensive mistake in AI diagnostics is solving the wrong problem — and the wrong problem almost always looks like the right problem at Layer 1. ...

“In enterprise AI the model is rarely the problem. The system around it almost always is.” A customer rep promised a discount. It was the right discount. The model said so. The problem — the policy had changed three days earlier. The model didn’t know. The customer was already engaged. The promise was already made. The rep couldn’t honor it. The customer was furious. When I was brought in, the team’s instinct was to retrain the model. Expensive. Time-consuming. And in this case — completely wrong diagnosis. ...