Predictive AI looks at past patterns to guess what will happen next, helping users make better decisions. Good prediction opportunities have these traits: enough historical data exists, patterns stay mostly stable over time, predictions help users take action, and some uncertainty is okay. Common uses include predicting:

• Demand
• When equipment needs maintenance
• How long tasks will take
• What users might do next

Think carefully about time frames: predicting next week's sales works better than next year's. Ask whether users can actually do something with the prediction. There's no point predicting things users can't change or influence. Check how accurate predictions need to be. Movie recommendations can be wrong sometimes and still be useful. Medical predictions need to be much more certain. Show confidence levels and alternatives when AI isn't sure.

Don't use predictions for high-risk situations unless accuracy is extremely high. Focus on predictions that help with decisions users are already trying to make. They just need better information to decide. Remember that predictions are only valuable if they arrive on time and users can act on them.