“Should we use AI for this?” is a question we get asked about almost everything now, and the honest answer is often no — not because AI doesn’t work, but because the problem doesn’t need it. Here’s a practical way to think about it.
Machine learning is a good fit when…
- You have a repeated decision or prediction that happens often enough to be worth automating (fraud flags, demand forecasts, support ticket routing)
- You already have historical data that reflects the outcome you’re trying to predict
- The cost of an occasional wrong prediction is acceptable, or there’s a human review step for high-stakes cases
It’s usually the wrong tool when…
- The rule is actually simple and deterministic — a plain if/else or a lookup table solves it more reliably and more cheaply
- You don’t have enough historical data, and collecting it isn’t feasible in a reasonable timeframe
- The business needs a fully explainable decision every time (some regulated contexts genuinely require this)
A quick gut-check
If you can describe the exact logic a smart employee would follow to make the decision, in a few sentences, you probably don’t need a model — you need software that encodes that logic. Machine learning earns its cost when the pattern is real but too complex or too variable to write down as a fixed rule.
We’d rather tell a client honestly that they don’t need a custom model than sell them one that doesn’t move the metric they actually care about. If you’re weighing this decision for a specific use case, talk to us about it.