AIProductManager

Deciding when to use AI versus a rules-based approach requires evaluating whether AI adds genuine value to the product feature. The framework aligns AI capabilities with real user needs to reduce misaligned features and overpromise.

The best way to select an AI model is to align the model choice with your product strategy and success metrics. The framework provides model-selection guidance and model cost analysis to ensure AI capabilities match product outcomes.

Set success metrics for AI features by defining evaluation rubrics and criteria during the product brief stage. The framework establishes an AI feature decision framework to measure whether model potential translates into real product outcomes.

Yes, you can use this to implement governance and risk controls for responsible AI. The framework applies responsible AI practices across the product lifecycle, setting safety controls and transparency signals for UX design.

To plan an AI-powered product feature, you need to provide a product brief including the problem, model approach, evaluation criteria, and success metrics. This input drives the strategy, UX, and governance outputs.