BlogData & AI
Explaining AI predictions to non-technical managers
By Micah Ondiwa, Senior Software Engineer 1 min read
Managers trust predictions they understand. Show the main factors, the confidence and how the model performed in the past.
A prediction that cannot be explained is hard to act on. Managers need to understand enough to trust — or question — what a model says.
Show the main drivers
For each prediction, show the few factors that contributed most: "flagged because of late payments in the last three months and a recent increase in order size".
Communicate uncertainty
Present ranges or confidence levels instead of single numbers. A forecast of 1,000 ± 50 means something different from 1,000 ± 400.
Show the track record
Share how accurate the model has been on recent periods, in plain terms.
Explain limitations
State what the model does not know: new products, unusual events, data it was not trained on.
Keep decisions human
Frame predictions as decision support and record when people override them, and why.
See our AI and analytics work.
About the author
Micah Ondiwa
Senior Software Engineer
Micah Ondiwa writes about software engineering — application architecture, APIs and integration, data and AI-assisted development, and reliable delivery.