Becoming an AI product manager requires a focus on practical skills and business acumen rather than in-depth technical knowledge of machine learning. The role involves understanding when AI is appropriate for business use, bridging the gap between data scientists and executives, and managing the unique challenges of probabilistic systems, such as model drift and data quality. Unlike traditional product management, AI product managers deal with continuous monitoring and refinement of systems, translating technical metrics into business value, and navigating uncertainty with stakeholders. Essential skills include technical fluency, ethical oversight, and effective communication across technical and business audiences. Successful AI product managers learn to prioritize issues affecting user trust over new features and to connect model performance with business outcomes. Practical experience and real-world projects are more valuable than theoretical knowledge, with industry certifications and community engagement supporting career growth. The AI product lifecycle requires constant vigilance, as models continually evolve with new data, necessitating ongoing experimentation, retraining, and monitoring to maintain business relevance.