Four questions before you approve an AI project

Every portfolio company has an AI proposal in front of it. Most are sincere, some are good, and a great many will produce a demonstration that impresses a board and then quietly stops being used.

The difference is rarely the model. It is almost always one of four things, and all four can be checked in a meeting.

One: what decision does this change?

Not what it predicts. What it changes. If a system forecasts demand more accurately and nobody reorders differently as a result, the forecast has cost money and saved none. Ask who acts on the output, what they do today, and what they would do instead. If nobody in the room can name the person and the action, the project is a science experiment with a budget.

Good answers sound specific and slightly boring. Boring is the point.

Two: where does the data live, and who can move it?

An AI project is a data project in better clothes. Most stall not on the model but on access.

• The numbers are in a system the vendor cannot reach.
• The export is manual.
• The only clean copy is a spreadsheet on somebody's machine.

All of this is knowable on day one and almost never asked on day one. So ask to see the data before you approve the project. Not a sample the vendor prepared. The real thing, pulled the way the system would have to pull it, by somebody who has to live with the answer.

Three: what happens when it is wrong?

It will be wrong. That is not a flaw in the technology, it is what a probabilistic system is. The question is whether being wrong is survivable and whether anybody will notice. A pricing suggestion that drifts gets caught by a human who thinks the number looks odd. A model quietly reclassifying invoices may not be caught for a quarter. If the answer involves nobody checking, the project needs a control before it needs a budget.

Four: who owns it in eighteen months?

Every one of these has to be maintained. Data changes shape. A supplier changes a file format. The person who built it takes another job.

Ask whose job this becomes once the project team has gone. If the honest answer is nobody's, you are buying an asset that turns into a liability on a schedule you cannot see.

None of these are technical questions

Any of them can be asked by somebody who has never trained a model, and asking them will stop a good number of the proposals in front of you before any money moves. That is not a reason to be gloomy about AI in a portfolio company. It is how the ones worth having get built.