Most asked
What does "diagnose before you prescribe" actually mean?
Short answer
It means no responsible party recommends a treatment before understanding the condition. In business terms: no tool recommendation, no build, no platform decision until someone has examined your goals, operations, team, and governance. It is the second opinion you get before surgery.
Every pitch for AI, whatever is being sold, rests on the same assumption. The problem is in the process. Fix the operation, add the AI, get results. It is a logical sequence and it holds up in a board presentation. It is also exactly where most AI investments go sideways.
That sequence is actually three assumptions stacked on each other. First, that you have correctly identified which process is the problem. Second, that the process is the real constraint rather than a symptom of something upstream. Third, that adding AI to a corrected process will produce the outcome you are targeting. Most organizations do not validate a single one of those before committing budget.
What gets purchased is a solution to a symptom. The real constraint tends to sit upstream: a goal that was never defined precisely enough to measure, a data layer that cannot support the output, or a governance gap that surfaces six months after deployment when something goes wrong and nobody owns it.
The investment lands. The results do not. Not because AI does not work, but because the diagnosis happened after the purchase.