Chapter 38 Further Reading

On Organizational AI Adoption

"The State of AI in the Enterprise" (McKinsey Global Institute, annual) McKinsey's annual survey-based research tracks enterprise AI adoption patterns, barriers, and outcomes. The findings consistently highlight change management and talent development as more important than technology selection. Available at mckinsey.com.

"AI Adoption in the Workplace" (MIT Sloan Management Review) MIT SMR's ongoing coverage of AI in organizational contexts is among the most research-grounded in the business press. Their work on the "AI adoption gap" — the distance between AI availability and effective use — directly addresses the individual-to-organizational challenge described in this chapter.

"Prediction Machines: The Simple Economics of Artificial Intelligence" by Agrawal, Gans, and Goldfarb While primarily focused on economic analysis, this book provides a useful framework for thinking about which decisions to delegate to AI and which to retain for humans — directly applicable to use case taxonomy development.

"Working with AI: Real Stories of Human-Machine Collaboration" (MIT Work of the Future) A collection of case studies from MIT's research program on AI in the workplace. More grounded in actual organizational experience than most academic AI research.

On Change Management for Technology Adoption

"Leading Change" by John Kotter The classic framework for organizational change management. While not AI-specific, Kotter's eight-stage model applies directly to AI adoption challenges, particularly the importance of creating urgency, building guiding coalitions, and generating short-term wins.

"The Lean Startup" by Eric Ries The build-measure-learn loop Ries describes applies directly to AI policy development: publish a working policy, observe how it performs in practice, learn from problems, update. The perfect policy that takes six months to develop is worse than the good-enough policy that launches now.

"Switch: How to Change Things When Change Is Hard" by Chip Heath and Dan Heath The Heath brothers' framework for change management — directing the rider (rational), motivating the elephant (emotional), shaping the path (environmental) — maps directly onto the challenge of AI adoption where rational arguments for AI are often insufficient to overcome emotional resistance and environmental friction.

On AI Policy and Governance

"Governing AI in the Enterprise" (Deloitte Insights) Deloitte's practice area on enterprise AI governance publishes practical frameworks for AI policy development, risk assessment, and governance structures. More practitioner-oriented than academic.

"AI Ethics Guidelines Global Inventory" (AlgorithmWatch) A catalog of AI ethics guidelines from organizations worldwide — useful for benchmarking your team's policy against what peer organizations have established. Available at algorithmwatch.org.

"Responsible AI in Practice" (Microsoft) Microsoft publishes extensive guidance on responsible AI deployment, including governance frameworks and policy templates. While enterprise-oriented, many elements are applicable to smaller teams. Available at microsoft.com/ai/responsible-ai.

On AI Skills Development and Training

"The AI-Powered Organization" by Brass and Shook (MIT Technology Review Insights) Research on what distinguishes organizations that have successfully scaled AI capabilities from those that haven't. The talent and training investment findings are particularly relevant.

"Developing AI Literacy in the Workforce" (World Economic Forum) WEF's research on AI literacy frameworks — what skills workers need, how to assess gaps, and what training approaches are most effective. Available at weforum.org.

On Team Dynamics and Performance Standards

"Team of Teams" by General Stanley McChrystal McChrystal's account of transforming military command structures for a complex environment offers relevant lessons for AI adoption: the importance of shared understanding, information flow, and distributed decision-making authority. The chapter on "shared consciousness" is particularly applicable.

"Measure What Matters" by John Doerr The OKR framework Doerr describes provides a useful structure for establishing measurable success criteria for AI adoption — directly applicable to the "measuring effectiveness" challenge addressed in Chapter 39.

On Equity and AI

"Atlas of AI" by Kate Crawford Crawford's critical examination of AI's social and labor implications provides important context for the equity dimensions of workplace AI adoption. While broader than organizational deployment, it informs the equity considerations every team leader should be thinking about.

"The Alignment Problem" by Brian Christian Christian's examination of how AI systems reflect the values of their designers and trainers is relevant background for understanding why AI tools may perform differently for different types of users and different types of tasks — with implications for equity in organizational adoption.

Practical Templates and Frameworks

NIST AI Risk Management Framework (NIST) The U.S. National Institute of Standards and Technology has published an AI Risk Management Framework that provides a structured approach to AI governance at any scale. While designed for enterprise and government contexts, the principles scale down effectively. Available at nist.gov/artificial-intelligence.

"AI Use Policy Templates" (Future of Life Institute) The Future of Life Institute has published model AI use policies that teams can adapt. A useful starting point for policy drafting. Available at futureoflife.org.