Introduction

Unit

In Progress

You’ve built a high-performing machine learning (ML) model, but the project is stalled. Why? Because you didn’t engage your stakeholders.

Stakeholder engagement is one of the most overlooked yet critical skills for ML professionals. Projects succeed not only because of technical performance, but because stakeholders feel their needs are understood and addressed.

Why does this unit matter?

An ML project is never a solo mission — it’s a collaborative effort that spans teams, functions and leadership levels. Even the most accurate model won’t create value if the right people aren’t brought in or if their concerns aren’t addressed. The ability to identify, engage and manage stakeholders — from end users to executive sponsors — is what transforms a prototype into a deployed, value-generating product.

In this unit, you’ll explore frameworks for identifying stakeholders, strategies for building productive relationships and techniques for balancing competing demands.

Learning objectives

By the end of this unit, you will be able to:

• Identify and classify key stakeholders and their levels of influence and interest in an ML project.
• Develop and execute a comprehensive stakeholder engagement plan to foster buy-in and collaboration throughout the project life cycle.
• Create and disseminate a detailed report and presentation that effectively communicates model development and confirms stakeholder approval.
• Evaluate the effectiveness of your engagement strategies and documentation.

Action item: Pause and reflect

Before diving in, take a moment to connect this topic to your own work.

Questions & Reflections

1. Think about a time when an ML or data project you worked on faced resistance or delays. Was it due to technical challenges or stakeholder challenges?

2. How might improving your ability to engage and manage stakeholders change the success rate and impact of your future projects?