AI Decision Making: A Guide for Leaders and Managers In 2026

There is a pile of files nobody reads any more.

They are the applications the model scored as low risk. They arrive already sorted, already ranked, already carrying a number that looks like an answer. And at some point, without anybody deciding it, the analysts stopped opening them.

Nobody signed off on that. There was no meeting. It happened the way these things happen, through a system that was slightly faster than reading, and a queue that was slightly longer than the day.

That is what this course is about.

A model produces a prediction. A prediction is not a decision. Between the two sits a judgement about what matters, what it costs to be wrong in each direction, and who is answerable. A machine can make the first part almost free. It cannot make the second part at all.

You do not need a technical background. No code appears anywhere in this course. There is no maths beyond arithmetic. Every term is defined the moment it is first used, and every lesson stands alone, so you can watch them in any order.

Twenty six short lessons take you through it. What a prediction actually is, and what it is not. The four parts of a decision, and which of them a model can touch. What AI cannot decide, no matter how good it gets. Why every metric encodes somebody's value judgement. Why reversibility matters more than accuracy when you are choosing what to automate.

Then the part most courses skip: how to read the output. Why confidence is not correctness. Why base rates decide everything, and how a ninety percent accurate model produces mostly wrong answers when the thing it looks for is rare. Why the same question gives different answers. How a chain of ten steps, each ninety five percent reliable, ends up around sixty percent. Ten questions to ask before you trust a model with anything.

Then two biases, chosen because they are the two that actually cost money. Then oversight that works rather than oversight that exists on paper. Then three cases worked end to end.

What you keep. Sixteen files. Six workbooks that do the arithmetic for you, including a base rate calculator, a calibration check and a chain reliability calculator. Ten documents, including a decision rights grid, an override policy, an oversight pattern selector, an automation bias countermeasure list, and a decision record template.

On honesty. This course does not sell you a tool and it does not name one. It will tell you plainly which decisions to keep away from a model, and it spends a whole section on that before it spends a minute on anything else.