Tying model metrics to business KPIs upfront is paramount for ensuring alignment between ML and product teams. Investing all the way through the ML lifecycle is critical to ensuring AI ROI, as it requires planning for design aspects, human computer interaction, hypothesis development, and eventual retirement of models. Threading the needle with a centralized ML approach can be worth it, especially when blending product focus with broader technical breakthroughs. Assessing talent involves simulating real-world problems, such as giving candidates modeling tasks that tackle actual business needs, to understand how they think and approach challenges. By implementing these best practices, ML leaders can ensure a good foundation for future success in the rapidly evolving AI landscape.