Machine learning, the latest incarnation of artificial intelligence (AI), works by detecting complex patterns in past data and using them to predict future data. Since almost all business decisions ultimately rely on predictions (about profits, employee performance, costs, regulation, etc.), it would seem obvious that machine learning (ML) could be useful whenever “big” data are available to support business decisions. But that isn’t quite right.

The reality in most organizations is that data may be captured but they are stored haphazardly. Their quality is uneven, and integrating them is problematic because they sit in disparate locations and jurisdictions. But even when data are cleaned up and stored properly, they’re not always appropriate for the questions or decisions that management has in mind. So, how do you know whether applying predictive analytics through AI techniques to a particular business problem is worthwhile? Although every organization and context is different, here are five general principles that should be useful in answering that question.

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