What Full-Text Search Already Taught Us About AI

We have been here before.

In the enterprise, there have always been two kinds of searches. The first looks for the exact answer you get from a query (deterministic). The second looks for the closest answer you can find through full-text search (probabilistic).

We knew the difference, and we learned how to use each kind.

Full-text search gave us the approximate answers. It returned a ranked list of the most relevant results — useful when you are browsing, less so when you need the emergency shutdown procedure. But it presented its results as what they were: ranked, approximate, and left to your judgment.

AI does not.

It returns its most probable answer as though it were the answer, with no ranking and no indication of confidence. Some of what it returns is accurate, some is close, and some is invented outright — presented together, with nothing to tell them apart.

And AI doesn't tell you which is which.

Ask for the emergency shutdown procedure and there is a correct answer and an incorrect one. The procedure AI returns may be close to the one you need, or synthetic — created in real time. Either way it is presented with the same confidence, and the two are difficult to tell apart. However, using the wrong procedure may result in serious consequences.

For AI to be reliable in the enterprise, it has to do what full-text search never had to: handle both kinds of answers at once. It has to draw the exact answer from structured records and the approximate one from documents, and distinguish between them — returning facts as facts and estimates as estimates.

That is the difficult part. It is doable. However, it is not yet being done.