Chapter 35 Further Reading: Change Management for AI
Change Management Foundations
1. Kotter, J. P. (2012). Leading Change. Harvard Business Review Press. (Updated edition; originally published 1996.) The foundational text for organizational change management. Kotter's 8-step model, developed from a study of over 100 companies attempting large-scale transformation, remains the most widely used framework for leading change at the organizational level. The updated edition includes reflections on how digital transformation has altered the change landscape. Essential reading for anyone leading an AI transformation, not because AI change is identical to prior transformations, but because the organizational dynamics — urgency, coalition, vision, communication, short-term wins — are universal.
2. Hiatt, J. (2006). ADKAR: A Model for Change in Business, Government, and Our Community. Prosci Learning Center. The definitive guide to the ADKAR model. Hiatt's individual-focused framework complements Kotter's organizational approach by providing a diagnostic tool for understanding why specific people are not adopting a change. The book includes assessment instruments and case studies. Particularly valuable for AI practitioners who need to diagnose adoption bottlenecks at the team or individual level.
3. Bridges, W. (2009). Managing Transitions: Making the Most of Change. 3rd ed. Da Capo Press. Bridges distinguishes between "change" (the external event) and "transition" (the internal psychological process people go through). His three-phase model — endings, the neutral zone, and new beginnings — provides an emotional map of the change experience that supplements the more procedural ADKAR and Kotter frameworks. Relevant for understanding why technically successful AI deployments trigger emotional resistance.
AI-Specific Change Management
4. Fountaine, T., McCarthy, B., & Saleh, T. (2019). "Building the AI-Powered Organization." Harvard Business Review, 97(4), 62-73. A McKinsey-authored guide to the organizational challenges of scaling AI. The authors identify the most common failure modes — including the change management gaps that cause technically successful AI projects to fail in practice. Their finding that organizational and cultural barriers matter more than technical ones directly supports this chapter's central argument.
5. Davenport, T. H., & Mittal, N. (2022). All In on AI: How Smart Companies Win Big with Artificial Intelligence. Harvard Business Review Press. Examines companies that have successfully embedded AI across their organizations — not as pilot projects but as core capabilities. Davenport and Mittal's analysis of "all in" companies like Ping An, Capital One, and Anthem reveals the change management strategies that enabled organization-wide adoption. The emphasis on leadership commitment, cultural transformation, and sustained investment connects directly to Kotter's steps 7 and 8.
6. Brynjolfsson, E., & McAfee, A. (2017). "The Business of Artificial Intelligence." Harvard Business Review, July 2017. An influential article arguing that AI's most important impacts are not technical but managerial. Brynjolfsson and McAfee's framing of AI as a "general-purpose technology" — one that requires complementary organizational innovations to deliver value — provides the economic logic behind this chapter's emphasis on change management as the primary value driver.
7. Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2017). "Reshaping Business with Artificial Intelligence." MIT Sloan Management Review and Boston Consulting Group. A global survey-based study that identifies the "AI adoption gap" — the difference between AI enthusiasm and AI results. The study's finding that organizational alignment, not technical capability, is the primary determinant of AI success has been replicated in subsequent research and provides empirical support for the change management investment ratios cited in Section 35.1.
Resistance, Trust, and the Human Side
8. Edmondson, A. C. (2018). The Fearless Organization: Creating Psychological Safety in the Workplace for Learning, Innovation, and Growth. Wiley. Amy Edmondson's definitive work on psychological safety — the belief that one can speak up, raise concerns, and report failures without punishment. The book provides frameworks for building psychologically safe environments that are directly applicable to AI change management, where employees must feel safe to report AI failures, express concerns about automation, and provide honest feedback. The research cited in Section 35.11 draws on Edmondson's framework.
9. Lee, K.-F. (2018). AI Superpowers: China, Silicon Valley, and the New World Order. Houghton Mifflin Harcourt. Lee, a former head of Google China, provides a cross-cultural perspective on AI adoption and its workforce implications. His analysis of which jobs are most and least susceptible to AI automation — based on cognitive complexity, social interaction, and creative requirements — informs the workforce planning frameworks in Section 35.7. His proposed policy responses, including a "social investment stipend" and prioritization of human-centered service work, connect to Chapter 38's discussion of AI and the future of work.
10. Autor, D. H. (2015). "Why Are There Still So Many Jobs? The History and Future of Workplace Automation." Journal of Economic Perspectives, 29(3), 3-30. An essential economics paper that explains why technology consistently creates more jobs than it destroys — while also transforming the nature of work. Autor's analysis of task-based automation (machines replace tasks, not entire jobs) provides the intellectual foundation for the distinction between task automation and job elimination in Section 35.4. The paper is rigorous but accessible, and its historical perspective provides useful context for the Luddite case study.
The Luddite Movement and Technology History
11. Merchant, B. (2023). Blood in the Machine: The Origins of the Rebellion Against Big Tech. Little, Brown and Company. The most contemporary and directly AI-relevant history of the Luddite movement. Merchant draws explicit parallels between the concerns of nineteenth-century textile workers and twenty-first-century knowledge workers facing AI displacement. His nuanced treatment of the Luddites — neither romanticizing their tactics nor dismissing their concerns — informs Case Study 2 and provides essential historical context for understanding modern AI resistance.
12. Thompson, E. P. (1963). The Making of the English Working Class. Victor Gollancz. The classic historical study that rescued the Luddites from historical condescension. Thompson's meticulous documentation of the social, economic, and cultural context of early industrial resistance reveals the Luddites as rational actors responding to a profound economic disruption — not as "anti-technology" extremists. Dense and long but essential for anyone who wants to understand the human side of technological transformation at a deeper level.
13. Binfield, K., Ed. (2004). Writings of the Luddites. Johns Hopkins University Press. A primary-source collection of letters, manifestos, and petitions written by the Luddites themselves. Reading the Luddites in their own words reveals the sophistication of their analysis and the specificity of their demands — neither of which survive in the cartoon version of the movement that the word "Luddite" typically evokes. Invaluable for educators who want to bring the original sources into classroom discussion.
Workforce Planning and the Future of Work
14. World Economic Forum. (2025). The Future of Jobs Report 2025. World Economic Forum. The WEF's biennial survey of employers across 27 industries and 45 economies on expected changes to jobs and skills. The 2025 edition, which incorporates AI's accelerating impact, provides the most current data on which roles are expanding, contracting, and transforming. The finding that 69 percent of AI-driven job changes involve task modification rather than role elimination — cited in Section 35.4 — comes from this report. Essential reference for any workforce planning exercise.
15. Manyika, J., Lund, S., Chui, M., et al. (2017). "Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation." McKinsey Global Institute. A comprehensive analysis of how automation (including but not limited to AI) will reshape global labor markets. The report provides scenario-based projections for job displacement and creation across industries and geographies, along with policy recommendations for managing workforce transitions. The framework for categorizing roles by automation susceptibility informs the four-zone model in Section 35.7.
16. Agrawal, A., Gans, J., & Goldfarb, A. (2022). Power and Prediction: The Disruptive Economics of Artificial Intelligence. Harvard Business Review Press. The sequel to Prediction Machines (cited in Chapter 1's further reading). While the first book focused on AI as a prediction technology, this one examines how AI changes organizational decision-making — including the change management implications of shifting from human judgment to algorithmic recommendation. The book's framework for "decision automation" directly informs the human-AI collaboration levels in Section 35.9.
Human-AI Collaboration
17. Kasparov, G. (2017). Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins. PublicAffairs. Kasparov's memoir and analysis of his chess matches against IBM's Deep Blue, culminating in his advocacy for human-AI collaboration (the "centaur" model described in Section 35.9). Kasparov's core insight — that the combination of human judgment and AI computation produces results superior to either alone — has become a foundational principle of AI deployment. Written with surprising clarity and self-reflection, it is both a first-person account of technological disruption and a manifesto for collaborative intelligence.
18. Daugherty, P. R., & Wilson, H. J. (2018). Human + Machine: Reimagining Work in the Age of AI. Harvard Business Review Press. An Accenture-based analysis of how organizations are redesigning work to integrate AI. The book's framework for "missing middle" activities — tasks that emerge at the interface between human and machine capabilities — provides practical design principles for human-AI workflows. The authors' emphasis on "responsible AI" as a prerequisite for sustained human-AI collaboration connects this chapter's change management themes to the governance frameworks of Part 5.
Case Study Background
19. Deere & Company. (2022). "Smart Industrial Strategy." Annual Report 2022. Deere's own strategic framing of its AI transformation, including its precision agriculture roadmap and technology stack strategy. The annual report is particularly useful for understanding how Deere communicates its AI transformation to shareholders — a real-world example of executive-level AI communication (Section 35.6).
20. Schrage, M., Kiron, D., Hancock, B., & Breschi, R. (2023). "Reskilling in the Age of AI." MIT Sloan Management Review and Boston Consulting Group. A survey-based study of organizational reskilling programs, including success factors and common failure modes. The finding that interactive, in-person training significantly outperforms e-learning for AI skill development supports Athena's training design decisions in Section 35.8.
Measurement and Adoption
21. Rogers, E. M. (2003). Diffusion of Innovations. 5th ed. Free Press. (Originally published 1962.) The foundational work on how innovations spread through populations. Rogers' adoption curve — innovators, early adopters, early majority, late majority, laggards — provides the framework applied to AI adoption in Section 35.10. Now in its fifth edition, the book has been updated with contemporary examples but retains the elegance of its original framework. A classic that every MBA student should read.
22. Prosci. (2024). Best Practices in Change Management. 12th ed. Prosci Inc. Prosci's longitudinal benchmarking study of change management practices, based on data from over 6,000 change initiatives across industries. The 12th edition includes specific findings on AI and digital transformation projects, including adoption rates, resistance patterns, and the correlation between change management investment and project outcomes. The most data-rich resource available for evidence-based change management.
For additional resources on AI ethics and governance — topics that directly intersect with change management — see the further reading sections of Chapters 25-30. For workforce planning and the future of work, see Chapter 38's further reading.