SESKStuff Everyone Should Know
SESK: Stuff Everyone Should Know · sesk.info/ai-when-not-to-use-ml

When should a team prefer a simple rule over machine learning?

When the rule already meets the goal with less cost and complexity.

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  1. Whenever data exists
  2. Only for tiny companies
  3. When a simpler solution works ✔

Why: ML creates training, evaluation, deployment, and maintenance costs. A non-ML baseline reveals whether added predictive quality is worth that extra system burden.

Huh, didn't know that: A strong heuristic is also a useful benchmark for deciding whether ML adds value.

The dad joke
The simple rule solved the problem before the model finished importing libraries.
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References

Answer: When a simpler solution works. ML creates training, evaluation, deployment, and maintenance costs. A non-ML baseline reveals whether added predictive quality is worth that extra system burden.

  1. Google Machine Learning Problem Framing developers.google.com/machine-learning/problem-framing/problem
    You don't want to implement a complex ML solution when a simpler non-ML solution will work.
    Checked 2026-10-10.

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