Key Takeaway
AI features need explicit experiment and evaluation phases that traditional feature development skips, and these phases must have defined exit criteria before production rollout begins. This framework defines seven lifecycle stages with stage gates, from ideation through sunset, designed to integrate with agile development workflows.
Prerequisites
- A product development process with sprint planning and review cadences
- Feature flagging infrastructure for gradual rollouts
- Model evaluation capabilities (automated test suites, golden datasets)
- Monitoring infrastructure for tracking feature-level quality metrics
- Defined success metrics for AI features (quality, engagement, business impact)
Why AI Features Need a Different Lifecycle
Traditional software features follow a linear path: design, build, test, deploy, maintain. AI features require a fundamentally different lifecycle because their behavior is probabilistic, their quality can degrade over time without any code changes, and their failure modes are often subtle rather than binary. A traditional feature either works or it does not. An AI feature works well, works poorly, works differently for different users, and its quality drifts over time as the world changes relative to the training data. This uncertainty demands additional lifecycle stages for experimentation, gradual rollout, and ongoing evaluation.
The Seven Lifecycle Stages
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