AI products differ fundamentally from traditional software due to their non-deterministic behavior and the delicate balance between agency and control. Success requires starting with low-autonomy systems under strong human oversight, then iteratively increasing automation as reliability improves. Effective teams prioritize problem-first design, tight cross-functional collaboration, and CEO-level engagement with AI tools. The Continuous Calibration/Continuous Development (CC/CD) framework enables safer deployment by using real-world data to refine system behavior and uncover hidden edge cases. While evaluations (evals) are useful, they're insufficient without production monitoring and customer feedback. Overhyped trends like multi-agent systems often fail in practice, whereas coding agents remain underutilized but high-potential. Ultimately, long-term advantage comes not from technology alone, but from organizational persistence, deep user understanding, and the ability to continuously adapt as models and workflows evolve.