Managing AI Projects

Chapter 4. Applied Approach to AI Project Management

In this chapter, we shift our discussion from the high-level management of AI teams to the specific methodologies required to succeed in AI delivery.

Building on the human and organizational foundations you learned about in the previous chapter, we’ll explore applied methodologies that help AI project managers bring structure to uncertainty. The primary focus is on learning how to apply frameworks like EMED (exploration, mobilization, execution, and delivery). We’ll examine how to use this model to guide a project from initial definition to final completion, finding the right balance between Agile, Lean, and traditional Waterfall approaches (Figure 4-1).

Figure 4-1. EMED methodology for AI project management

Additionally, we’ll discuss how to design workflows that integrate responsible AI best practices into every stage of execution. By the end of this chapter, you’ll understand how to choose, adapt, and combine methodologies that make AI projects not only deliverable but repeatable and aligned with business and ethical objectives.

Remember, the importance of having a methodology mindset when managing uncertainty, iteration, and accountability in AI delivery is due to the intrinsic nature of these projects. While they are similar to regular technology and software initiatives, they also bring complexities and unknowns that are ...