AI Project Management: Strategy, Delivery, and Lifecycle

As artificial intelligence transforms industries, organizations need leaders who can bridge the gap between technical AI capabilities and business strategy. Managing these initiatives requires a unique set of skills distinct from traditional software project management. This course prepares you to confidently guide AI projects from initial conception through to deployment and monitoring. You will learn how to align stakeholders, estimate resources, mitigate data risks, and deliver scalable AI solutions that drive real business value. What you'll learn: - Understand the core foundations of AI and machine learning lifecycles, including data collection, training, and deployment phases. - Plan project resources, timelines, and budgets while accounting for the experimental nature of AI development. - Evaluate and mitigate ethical risks, data biases, and compliance requirements in modern AI applications. - Design integration strategies for advanced technologies, including generative AI models and retrieval-augmented generation (RAG) workflows. - Implement basic MLOps principles to ensure continuous monitoring, model governance, and long-term system performance. - Collaborate effectively between data science teams, engineering departments, and business stakeholders. You will start by mastering foundational AI concepts and terminology before exploring the step-by-step phases of the project lifecycle. Through clear written explanations and practical business scenarios, you will progress from initial strategic scoping to deployment and post-launch maintenance. This course is designed for aspiring project managers, product managers, business analysts, and team leaders who are new to AI projects and want to build a solid foundation without needing a programming background. Prepare yourself to lead the next generation of technology initiatives by mastering the essentials of AI project management today.