The most valuable advice I give clients: sometimes you don't need AI.

Real examples where I recommended against ML:

A company wanted AI to categorize support tickets into 5 categories. Their ticket format was structured, and a simple keyword matching rule handled 92% of cases correctly. We added ML for the remaining 8%.

A startup wanted a recommendation engine. They had 200 users and 50 products. A curated "staff picks" list performed better than any algorithm because there wasn't enough data for personalization to work.

A factory wanted predictive maintenance. Their equipment failure was caused by one specific part wearing out on a predictable schedule. A calendar reminder to replace the part was more reliable than an ML model.

When NOT to use ML:

The problem has clear rules that cover most cases. Use rules. Add ML for the edge cases only.

You have too little data. ML needs patterns. Small datasets don't have enough.

The cost of being wrong is higher than the cost of a manual process. Some decisions shouldn't be automated.

A simpler solution works well enough. "Well enough" is a valid engineering target.

Knowing when NOT to use AI is the most senior AI engineering skill. It saves time, money, and complexity for both you and your clients.

Reserve ML for problems where it genuinely adds value. Use simpler tools everywhere else.