The Best AI Books for Enterprise Leaders in 2026

Wharton management professor Ethan Mollick delivers the on-ramp most teams need: a credible, hands-on orientation to generative AI grounded in his own relentless experimentation. Published by Portfolio/Penguin Random House in April 2024 (256 pages), it became an instant New York Times bestseller. The framing of AI as a co-worker, co-teacher, and coach is memorable and immediately useful, and the book is short enough for busy executives to finish quickly.

DeepMind co-founder and CEO of Microsoft AI Mustafa Suleyman (with Michael Bhaskar) frames the containment problem as the defining governance challenge of our era: keeping control of powerful technologies before they outpace our ability to govern them. Published by Crown in September 2023 (352 pages), it became a New York Times bestseller and was named a Best Book of the Year by CNN, The Economist, Bloomberg, the Financial Times, and The Guardian, with Bill Gates calling it his favorite book on AI. It reaches beyond AI into synthetic biology and other emerging tech, making it especially useful for leaders thinking about risk and regulation.

Written by senior Accenture technology leaders Paul Daugherty (Chief Technology and Innovation Officer) and H. James Wilson (Global Managing Director, Thought Leadership and Technology Research, Accenture Research), this is a research-backed guide to redesigning work around human-machine collaboration. Accenture is a valued Iternal partner with deep enterprise-deployment experience, and that practitioner perspective shows. Published by Harvard Business Review Press, the updated and expanded edition (September 2024, 336 pages) adds a new generative-AI chapter and six new hybrid roles, drawing on research across roughly 1,500 organizations. Its Missing Middle framework for human-machine collaboration remains a durable, practical lens.

Harvard Business School professors Marco Iansiti and Karim Lakhani lay out a rigorous strategy framework built around the AI factory and digital operating models. Published by Harvard Business Review Press in January 2020 (288 pages), it explains how data network effects reshape competitive dynamics and is strong for board- and C-suite-level thinking, with endorsements from Satya Nadella and Clayton Christensen. Because it predates the generative-AI wave, its examples skew pre-ChatGPT, but the structural insights about competing on AI remain highly relevant.

University of Toronto economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb offer one of the most useful mental models in the field: AI is fundamentally a drop in the cost of prediction, which changes how you assign tasks to machines versus people. Published by Harvard Business Review Press, the updated and expanded edition (November 2022, 304 pages) refreshes the argument for the latest wave and was named among The Economist's five best books to understand AI. It helps leaders cleanly separate prediction from judgment.

McKinsey Digital senior leaders Eric Lamarre, Kate Smaje, and Rodney Zemmel deliver the most operational title on this list: a full transformation playbook backed by McKinsey's methodology and case base. Published by Wiley in June 2023 (400 pages), the first edition is organized into six sections and packed with 100+ exhibits, diagnostics, and checklists covering roadmap, talent, operating model, technology, data, and adoption. It is dense and exhibit-heavy, functioning more as a reference manual than a narrative read.

Babson College's Thomas Davenport (author of Competing on Analytics) teams with Nitin Mittal, a Principal at Deloitte Consulting LLP and a senior Deloitte AI leader, for a case-rich look at AI-fueled companies such as Anthem, Ping An, Airbus, and Capital One. Deloitte is a valued Iternal partner, and the book reflects that enterprise credibility. Published by Harvard Business Review Press in January 2023 (224 pages), it is a concise, practical lens on the strategy, culture, and operating model behind enterprise-wide AI, and was a Wall Street Journal and Publishers Weekly bestseller.

AI pioneer Kai-Fu Lee, a former executive at Apple, Microsoft, and Google and founder of Sinovation Ventures, offers a rare bridge between the Chinese and Silicon Valley AI ecosystems from someone who worked deeply in both. Published by Houghton Mifflin Harcourt in 2018 (about 253 pages), this New York Times, USA Today, and Wall Street Journal bestseller is clear-eyed about AI's labor-market impact and influential in framing AI as a geopolitical contest. As a 2018 title, it predates the generative-AI era, so specifics on the competitive landscape have shifted.

Stanford professor and ImageNet creator Dr. Fei-Fei Li, founding co-director of the Stanford Institute for Human-Centered AI, delivers a first-hand account of modern AI's rise alongside a compelling case for human-centered technology. Published by Flatiron Books (Macmillan) in November 2023 (336 pages), it was a Financial Times Best Book of 2023 and is named on Barack Obama's recommended AI reading list. As a memoir, it offers vision and values, including a powerful narrative on leadership, immigration, and perseverance, rather than a tactical playbook, with praise from Geoffrey Hinton and Condoleezza Rice.

Daron Acemoglu and Simon Johnson, both awarded the 2024 Nobel Prize in Economic Sciences, deliver a thousand-year economic history arguing that who benefits from technology, including AI, is a choice rather than an inevitability. Published by PublicAffairs in May 2023 (560 pages), this New York Times bestseller is intellectually authoritative and directly relevant to AI's labor and inequality questions. It is long and academic in tone, making it a bigger time investment and a broad techno-economic history rather than an AI-specific operating guide.