Choose Machine Learning for the Right Reason

Not every difficult workflow needs machine learning. That is good news. A product team should be willing to improve a process with better data entry, a rule, a search experience, or a clearer operating procedure when one of those options solves the problem more reliably.

This is an original companion to Part 2 of the AI & PM Insights ML strategy series. It is about choosing ML because it is justified, not because it is available.

Look for a learnable pattern

ML becomes compelling when a task requires recognizing patterns across many examples and the relationship is too variable to encode with stable rules. There should be a plausible input, an outcome to estimate or classify, and feedback that can reveal whether the output was useful.

A useful question is whether an experienced person can explain how they reach a decision but cannot consistently reduce that explanation to a small set of rules. That often signals a pattern worth investigating.

Compare the alternatives honestly

Before building a model, compare the candidate approach with reasonable alternatives. Consider accuracy, latency, interpretability, maintenance cost, and what happens when the system encounters an unfamiliar case.

The comparison should include the full workflow. A highly accurate model may still be the wrong answer if it cannot be integrated in time, does not have enough representative data, or creates too much work for the people expected to use it.

Use a small decision memo

A short memo can make the choice more rigorous. It should state the problem, the expected benefit, the non-ML options considered, the evidence that a learnable pattern exists, and the next low-cost test.

That discipline gives teams permission to say no early. It also makes the eventual yes more credible because everyone can see what the investment is intended to change.