Managing AI Projects: Drive Innovation and Successfully Navigate the Full AI Project Lifecycle

Managing AI Projects: A Practical Guide to the Full AI Lifecycle 💡

Artificial intelligence projects rarely fail because the model cannot be built. They stumble when teams cannot scope the work, manage uncertainty, or carry a promising pilot into production. Managing AI Projects speaks directly to that gap. Malini Jain Runtasewee and Adrián González Sánchez combine project management discipline with the day-to-day realities of AI development, offering a structured way to lead initiatives from first idea to deployed solution.

The book is aimed at the people who have to make AI delivery happen: project managers, product leads, engineers, and technical professionals who need to connect business goals with iterative, experimental work. It treats AI project management as a discipline of its own, not simply standard project management with an AI label attached.

Inside the AI Project Lifecycle ⚙️

Across seven chapters, the authors build a complete view of the AI project management landscape:

• Introduction to AI project management — the core concepts and why AI initiatives demand a different approach.
• A deep dive into AI for project managers — the technical context PMs need without assuming they are data scientists.
• The role of the AI project manager — responsibilities, collaboration, and leadership across technical and business teams.
• Applied approach to AI project management — practical methods for planning, estimating, and structuring AI work.
• PM considerations during the technical AI lifecycle — what changes when projects move through data, model, and deployment stages.
• Tools for managing AI projects — instruments and practices that support delivery.
• Tales of AI project innovation — real-world perspectives on what AI project delivery looks like in practice.

Managing Uncertainty, Experimentation, and Change

AI work is rarely linear. Requirements shift as data becomes understood, experiments produce surprises, and technical feasibility is tested in cycles. This book addresses that reality head-on. It looks at how to support iterative experimentation, reduce risk, and keep stakeholders aligned when the path forward is not fully known at the start.

Just as important, it explores how to bridge technical and nontechnical teams. That translation work—helping business leaders understand AI constraints while helping engineers stay connected to organizational goals—is often where projects either gain momentum or lose it.

Who This Book Is For

If you are responsible for turning AI ambition into delivered outcomes, this guide is written with you in mind. It is especially relevant for:

• Project managers moving into AI, data, or cloud initiatives
• Product and program leads coordinating AI delivery
• Engineers and data professionals who need a project management framework
• Technical managers aligning AI work with business strategy
• Anyone building a repeatable playbook for AI projects rather than relying on one-off heroics

From Pilot to Production 🚀

Many AI efforts prove a concept and then stall. Managing AI Projects focuses on the full journey—from ideation through deployment—so that projects can move beyond prototypes and into systems that create lasting value. The authors write from experience across cloud, data, and AI delivery, and they frame project management as a practical craft: tools, decisions, communication patterns, and lessons learned.

For Digital Delights readers, this is a digital edition of a current O’Reilly guide to a fast-moving field. It offers both a conceptual map and a working handbook for the messy, iterative, rewarding work of managing AI projects.