Predictive vs Generative AI: How They Work and When to Use Each
Description
Watch: full video.This IBM Technology explainer compares predictive AI, which uses historical data to estimate what is likely to happen, with generative AI, which learns patterns in data to create new content such as text, images, or code. The useful distinction is not simply “old AI versus new AI,” but whether the task requires a forecast or classification versus generating something new. For knowledge workers, the practical lesson is to match the type of AI to the job rather than treating generative AI as the answer to every problem.
Official video embed
Martin Keen, IBM Technology · 2026-05-11
Martin Keen, IBM Technology · 2026-05-11
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Open original sourceKey Takeaways
- Predictive AI asks what is likely to happen next: it uses patterns in historical data to make forecasts, classifications, scores, or estimates about future or unknown outcomes.
- Generative AI asks what could be created: models such as LLMs and diffusion systems learn patterns in their training data and use them to produce new text, images, code, and other content.
- The practical choice depends on the job: use predictive AI when you need to estimate an outcome or probability, and generative AI when you need to create, transform, or synthesize content.
- The two approaches are complementary rather than competing. A real workflow can use predictive models to identify what is likely to happen and generative models to help interpret, communicate, or act on that information.