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When is machine learning worth it for a small or mid-sized company?
Machine learning is worth it when you have a repeated, well-defined prediction problem, several months or years of relevant clean data, and a process that can act on the output. Demand forecasting, churn prediction and anomaly detection are common fits. If basic reporting is still unreliable, fix that first.
Questions to test readiness:
- Is the outcome measurable and recorded consistently?
- Is there enough history for the model to learn from?
- Would a simpler rule or trend line do almost as well?
- Who will use the prediction, and what will they do differently?
Simple baselines should always be tried first. If a model barely beats them, the added complexity and maintenance are rarely justified.
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