By adopting a structured execution strategy, organizations can transform AI projects into products with life cycles focused on continuous improvement and measurable outcomes. The article highlights the use of frameworks like MCI (moat, cost, and innovation) and VFS (viable, feasible, and scalable) for prioritization and the importance of cross-functional collaboration. A real-world use case of a retail company improving inventory management through AI-driven forecasting illustrates the benefits of this approach. Ultimately, integrating PM principles can help organizations fully leverage AI's potential for innovation and growth.
Key takeaways:
- AI projects often fail due to misalignment with organizational goals, poor execution frameworks, and inadequate stakeholder buy-in.
- Product management principles, such as customer-centricity and iterative development, can help navigate challenges in AI-driven digital transformations.
- Viewing AI initiatives as products with life cycles encourages continuous improvement and measurable outcomes.
- Cross-functional collaboration and structured execution are crucial for scalable success in AI projects.