AI PMs face unique challenges that traditional PM frameworks do not address. Technical literacy needed: understand model capabilities and limitations, know the difference between deterministic and probabilistic outputs, understand training data requirements and biases, grasp basic MLOps concepts (model versioning, drift, retraining). AI-specific metrics: beyond standard product metrics (DAU, retention), track model performance metrics (accuracy, latency, fallback rate), cost per query, human override rate, and model confidence calibration. Evaluation frameworks: define success criteria before training, build evaluation sets that represent real user distribution, establish minimum performance thresholds and guardrails. Managing AI development: expect iteration - first model is never production ready, budget for data collection and labeling, plan for longer development cycles than traditional software. Roadmap considerations: model improvement is not linear, data quality improvements often beat model architecture changes, monitoring and retraining are ongoing costs. User trust: transparency about AI involvement, graceful degradation when confidence is low, easy human override paths. Key tools: familiarity with Weights and Biases (experiment tracking), basic understanding of prompt engineering, ability to interpret confusion matrices.