Co-Intelligence

The review

Mollick explores the implications of generative AI for work, learning, and creative activity. The book encourages engagement with the technology while taking its limitations seriously. It is a useful counterpoint to discussions that focus only on distant possibilities or treat automation as a simple replacement for human contribution.

For an enterprise team, the productive question is which parts of a real task benefit from assistance and which still demand expert review. A demonstration can make a tool appear broadly competent; an operating workflow needs a clearer account of inputs, acceptable outputs, permissions, exceptions, and ownership. Experimentation becomes more useful when it produces evidence about those boundaries.

Consider an illustrative analyst who uses AI to draft a comparison and then spends longer checking invented details than drafting would have taken. Another task, such as generating alternative questions for a workshop, may benefit with far less correction. The lesson is to assess assistance at the task level rather than deciding that AI is either universally useful or useless. For personal learning, the same distinction matters: a tool that explains and challenges your reasoning can help understanding, while one that simply supplies the answer may conceal what you have not learned.

This book closes the January–September series by bringing the discussion back to practice. Specific tools and capabilities evolve quickly, so readers should validate examples against their current environment. The lasting value is a habit of structured exploration: involve the people doing the work, compare results honestly, and preserve accountability as capabilities change.