Latina Lewis

Essential Skills for AI Project Managers Artificial intelligence is reshaping the kinds of projects organizations pursue, and the skills project managers need to deliver them successfully. Explore the seven essential skills that will set you up for success in AI project management. Source material: May blog by Ron Schmelzer & Kathlene Walch https://www.pmi.org/blog/skills-for-ai-project-managersPMI Blog The AI Revolution in Project Management 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 pursue4and the skills project managers need to deliver them successfully. Managing AI projects draws on the same core strengths4technical insight, strategic thinking, and adaptability4that define great project management. It also calls for additional fluency in data, AI concepts, and delivery models built for rapid iteration and change. Growing Demand As AI projects become more common and complex, the demand for AI-savvy project managers is growing fast. What Makes AI Projects Unique 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. Data-Centric Foundations Iterative Development Shifting Goals static rules or code. Data governance, evaluation, and updates. There's rarely a exploratory objectives that may not be AI initiatives are built around data4not quality, availability, and security are central to success. AI models require continual retraining, fixed endpoint. Many AI initiatives begin with fully defined from day one. More Unique Challenges Context-Sensitive Results AI systems often behave Sensitive to Change Over Time environment. A model might type, or quality can cause AI differently based on the input or perform well in one region but poorly in another. Trust as a Requirement AI can affect people in unintended ways. Building trustworthy AI means addressing ethical, responsible, transparent, governed, and explainable layers throughout the project lifecycle. Even subtle shifts in data volume, outputs to vary4sometimes unpredictably. Continuous monitoring is key. Skill # : 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. The better your grasp of the data, the better you can scope, prioritize, and de-risk your project. Key Competencies Knowing how data is sourced, labeled, and cleaned Understanding data quality and bias Collaborating effectively with data engineers and data scientists Skills # & # : Critical Thinking and Trustworthy AI Critical Thinking and Problem Solving Trustworthy AI Practices Project managers need to stay nimble and make decisions a key role in making sure ethical considerations are embedded AI initiatives operate in environments of constant change. quickly as new information emerges. Analyze evolving model results Make judgment calls when performance degrades Pivot quickly when data reveals new insights You're not just managing a plan4you're constantly reassessing what's possible and what's working. Trust and accountability are not optional. Project managers play throughout the project lifecycle. Spot ethical risks (e.g., bias, lack of transparency) Facilitate discussions on fairness and accountability Incorporate ethical review checkpoints Trust isn't a feature. It's a necessity. Skills # & # : Communication and Agile Delivery Communication Across Teams AI teams are often composed of specialists who speak different "languages"4data scientists, engineers, legal, product, and line of business. Project managers act as connectors and translators. Bridge communication between technical and business teams Set realistic expectations with stakeholders Ensure alignment across cross-functional contributors Agile and Iterative Delivery While not every AI project uses Scrum or Kanban, nearly all require short cycles, frequent testing, and continuous refinement. Managing evolving scope Prioritizing iterations based on learning Balancing experimentation with business timelines Skills # & # : AI Lifecycle and Tool Proficiency Understanding AI Technologies and Lifecycle Tool Proficiency and Hands-On Project Management do need to understand the typical development process and what's experiments, AI projects benefit from: AI project managers don't need to build models themselves4but they required at each stage: Problem definition Data collection and preparation Model training and evaluation Operationalization and monitoring The PMI Certified Professional in Managing AI (PMI-CPMAI#) certification methodology provides a structured approach. From managing datasets in collaboration tools to tracking Project management tools that support data workflows Basic understanding of version control and pipeline management Comfort with rapid documentation and tracking The Seven Essential Skills at a Glance Data Literacy Critical Thinking Trustworthy AI bias environments throughout Understanding data sourcing, quality, and Communication Bridging technical and business teams Tool Proficiency Managing workflows and documentation effectively Making quick decisions in changing Embedding ethical considerations Agile Delivery AI Lifecycle experimentation processes Managing iterative cycles and Understanding development stages and Your Path Forward in AI Project Management 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 skills4from data literacy to ethical leadership and more4you'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. Get Certified PMI-CPMAI# is the only certification purpose-built to guide professionals through the full AI project lifecycle.