Build the skills to lead AI projects with confidence

In many industries, artificial intelligence is becoming a key driver of innovation. From intelligent automation to customer-facing applications, AI initiatives are reshaping the kinds of projects organizations pursue—and the skills project managers need to deliver them successfully. As these projects become more common and complex, the demand for AI-savvy project managers is growing fast.

Managing AI projects draws on the same core strengths—technical insight, strategic thinking, and adaptability—that define great project management. It also calls for additional fluency in data, AI concepts, and delivery models built for rapid iteration and change.

This article outlines the top skills for AI project managers, including essential skills for artificial intelligence success in a leadership role. Whether you’re already managing AI projects or looking to grow into the role, these capabilities are essential to your success.

The unique nature of AI projects

Before exploring the skills, it’s important to understand what makes AI projects different. These differences explain why even experienced project professionals often encounter new challenges—and why successful delivery of AI projects benefits from building on core project management strengths while developing new skills tailored to this space. Here's what makes these projects unique:

• Data-centric foundations: Unlike traditional software projects, AI initiatives are built around data—not static rules or code. This makes data governance—including quality, availability, and security—central to success.
• Iterative development cycles: AI models require continual retraining, evaluation, and updates. There's rarely a fixed endpoint, which means PMs must lead projects that evolve as insights emerge.
• Unclear or shifting goals: Many AI initiatives begin with exploratory objectives. Project managers need to lead teams toward outcomes that may not be fully defined from day one.
• Context-sensitive results: AI systems often behave differently based on the input or environment. For example, a model might perform well in one region but poorly in another.
• Sensitive to change over time: Even subtle shifts in data volume, type, or quality can cause AI outputs to vary—sometimes unpredictably. Continuous monitoring is key.
• Trust as a requirement: AI can affect people in unintended ways. Building trustworthy AI means addressing all its key layers—ethical, responsible, transparent, governed, and explainable—throughout the project lifecycle.

These characteristics elevate the importance of specialized skills for AI project managers.

The top artificial intelligence skills for project managers

Mastering AI project management starts with developing the right mix of technical fluency, communication savvy, and ethical foresight. These seven skills will help you lead complex, fast-moving AI initiatives with confidence.

1. Data literacy and awareness

AI project managers don’t need to be data scientists, but they do need a solid understanding of how data works. This includes:

• Knowing how data is sourced, labeled, and cleaned
• Understanding data quality and bias
• Collaborating effectively with data engineers and data scientists

The better your grasp of the data, the better you can scope, prioritize, and de-risk your project.

2. Critical thinking and problem solving

AI initiatives operate in environments of constant change. Project managers need to stay nimble and make decisions quickly as new information emerges including being able to:

• Analyze evolving model results
• Make judgment calls when performance degrades
• Pivot quickly when data reveals new insights

You’re not just managing a plan—you’re constantly reassessing what’s possible and what’s working.

3. Trustworthy AI practices

Trust and accountability are not optional. Project managers play a key role in making sure ethical considerations are embedded throughout the project lifecycle:

• Spot ethical risks (e.g., bias, lack of transparency)
• Facilitate discussions on fairness and accountability
• Incorporate ethical review checkpoints in the project lifecycle

In short: trust isn’t a feature. It’s a necessity.

4. Communication across technical and business teams

AI teams are often composed of specialists who speak different “languages”—data scientists, engineers, legal, product, and line of business. Project managers should act as connectors and translators between these groups to promote shared understanding and alignment:

• Bridge communication between technical and business teams
• Set realistic expectations with stakeholders
• Ensure alignment across cross-functional contributors

5. Agile and iterative delivery for AI projects

While not every AI project uses Scrum or Kanban, nearly all require short cycles, frequent testing, and continuous refinement. AI PMs should be comfortable with:

• Managing evolving scope
• Prioritizing iterations based on learning
• Balancing experimentation with business timelines

6. Understanding AI technologies and lifecycle

AI project managers don’t need to build models themselves—but they do need to understand the typical development process and what’s required at each stage:

• Problem definition
• Data collection and preparation
• Model training and evaluation

Operationalization and monitoring

The Cognitive Project Management in AI (CPMAI^™) methodology and certification provides a structured approach.

7. Tool proficiency and hands-on project management

• From managing datasets in collaboration tools to tracking experiments, AI projects benefit from:
• Project management tools that support data workflows
• Basic understanding of version control and pipeline management

Comfort with rapid documentation and tracking

Conclusion

AI projects challenge familiar ways of working—but they also offer an exciting opportunity for project professionals to expand their expertise.

By building the right skills—from data literacy to ethical leadership and more—you’ll be better prepared to guide your teams through the unique demands of artificial intelligence projects and deliver results that are trustworthy, valuable, and aligned with business needs.