Chapter 31 Further Reading: AI Strategy for the C-Suite

AI Strategy Frameworks

  1. Iansiti, M. & Lakhani, K.R. (2020). Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World. Harvard Business Review Press. The definitive book on how AI reshapes competitive strategy. Examines how AI-native companies build advantages through data network effects, scale, and scope. The discussion of "operating models" and "AI factories" directly informs the operating model choices in Section 31.8. Essential reading for any executive developing an AI strategy.

  2. 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, this book focuses on the strategic dynamics of AI adoption -- including the "between times" when AI is powerful enough to disrupt but not yet mature enough to replace existing systems. The treatment of AI timing decisions (when to move, when to wait) directly supports the first-mover vs. fast-follower analysis in Section 31.4. More strategically nuanced than most AI business books.

  3. Davenport, T.H. & Mittal, N. (2023). All In on AI: How Smart Companies Win Big with Artificial Intelligence. Harvard Business Review Press. Based on research into companies that have made large-scale AI commitments, this book identifies patterns of success and failure in enterprise AI strategy. Case studies include Ping An, Capital One, DBS Bank, and Anthem. The framework for "AI-fueled organizations" complements the AI Strategy Canvas from Section 31.2.

  4. Ng, A. (2018). "AI Transformation Playbook." Landing AI White Paper. Andrew Ng's concise, practical guide to enterprise AI strategy. Covers the sequence of AI adoption (start with pilot projects, build an AI team, provide broad training, develop a strategy, establish communication), which directly supports the sequential capability building advocated in Section 31.14. At only 12 pages, it is the most efficient introduction to AI strategy for time-constrained executives. Referenced also in Chapter 6's further reading, it remains relevant at the strategic level.

  5. Fountaine, T., McCarthy, B., & Saleh, T. (2019). "Building the AI-Powered Organization." Harvard Business Review, July-August 2019. McKinsey consultants describe the organizational requirements for scaling AI beyond pilots. Addresses the pilot purgatory problem (Section 31.11), the importance of cross-functional teams, and the role of leadership in enabling AI adoption. Practical and well-grounded in real organizational dynamics.

Competitive Dynamics and Data Strategy

  1. Varian, H.R. (2019). "Artificial Intelligence, Economics, and Industrial Organization." In The Economics of Artificial Intelligence, edited by A. Agrawal, J. Gans, and A. Goldfarb. University of Chicago Press. Google's Chief Economist analyzes the competitive economics of AI, including data network effects, economies of scale in AI, and winner-take-most dynamics. Academic but accessible, this chapter provides the theoretical foundation for the competitive dynamics discussion in Section 31.3.

  2. Hagiu, A. & Wright, J. (2020). "When Data Creates Competitive Advantage." Harvard Business Review, January-February 2020. A nuanced analysis of when data creates genuine competitive advantage and when it does not. Challenges the assumption that "more data always wins" by examining data network effects, data exclusivity, and data depreciation. Directly relevant to the moat-vs-commodity analysis in Section 31.3. Short and sharply argued.

  3. Furman, J. & Seamans, R. (2019). "AI and the Economy." Innovation Policy and the Economy, 19(1), 161-191. Examines the macroeconomic implications of AI adoption, including market concentration, labor displacement, and productivity effects. Useful for executives who need to understand the broader economic context in which their AI strategies operate. Also relevant to the societal dimensions discussed in Chapter 38.

Board Governance and Executive Leadership

  1. National Association of Corporate Directors (NACD). (2024). Director's Handbook on AI Oversight. NACD in partnership with Carnegie Mellon University. The most authoritative resource on board-level AI governance. Covers board AI literacy, committee structures, risk oversight, and the fiduciary dimensions of AI decision-making. Directly informs Section 31.6 and 31.7. Required reading for any board member or executive responsible for AI governance.

  2. Baquero, Y.M. & Baquero, G. (2023). "Artificial Intelligence, Board Oversight, and Corporate Governance." Stanford Law Review Online, 76. Examines the legal and governance dimensions of board AI oversight, including the duty of care, the duty of loyalty, and the evolving regulatory expectations for board engagement with AI. Provides the legal foundation for the fiduciary duties discussion in Section 31.6.

  3. Ransbotham, S., Kiron, D., & Gerbert, P. (2023). "Achieving Individual -- and Organizational -- Value with AI." MIT Sloan Management Review and Boston Consulting Group Annual AI Report. The 2023 edition of MIT SMR and BCG's annual AI survey, based on data from over 3,000 organizations globally. Provides empirical evidence on AI adoption patterns, the gap between AI leaders and laggards, and the organizational capabilities that distinguish successful AI strategies. The finding that strategic late-but-disciplined adopters outperform early-but-unfocused adopters directly supports the first-mover analysis in Section 31.4.

Case Study Sources

  1. Iansiti, M. & Lakhani, K.R. (2019). "Ping An, Good Doctor: China's Pioneering Online Health Care Platform." Harvard Business School Case Study 620-039. A detailed case study of Ping An's transformation, focusing on the Good Doctor healthcare platform. Provides depth on the strategic logic, organizational design, and AI capabilities behind Ping An's most innovative platform. Essential companion to Case Study 1.

  2. Yoo, Y. & Kim, S. (2019). "GE's Digital Transformation: A Case Study of the Digital Industrial Strategy." Proceedings of the International Conference on Information Systems (ICIS). An academic analysis of GE's digital strategy, examining the organizational, technological, and strategic factors that contributed to Predix's failure. Provides the empirical detail behind Case Study 2 and the organizational misalignment analysis.

  3. Winig, L. (2019). "GE's Big Bet on Data and Analytics." MIT Sloan Management Review, Spring 2019. A practitioner-oriented analysis of GE's Predix strategy, including interviews with GE executives and industry analysts. Particularly useful for understanding the internal dynamics -- the cultural conflicts, the talent challenges, and the leadership churn -- that undermined the strategy.

  4. Birkinshaw, J. & Cohen, J. (2020). "Why Every Company Needs a Chief AI Officer (and What They Actually Do)." Harvard Business Review, December 2020. Examines the role of AI leadership at the executive level, drawing on interviews with CAOs and CDOs across industries. Addresses the delegation-vs-abdication distinction raised in Section 31.6 and provides practical guidance on AI executive roles.

Operating Models and Organizational Design

  1. Colson, E. (2019). "What AI-Driven Companies Can Teach Us About Building Algorithms." Harvard Business Review, January-February 2019. Written by Stitch Fix's Chief Algorithms Officer, this article advocates for embedding data scientists in business teams rather than centralizing them. Provides the organizational design perspective behind the embedded model in Section 31.8. Also referenced in Chapter 6's further reading, its organizational insights are even more relevant at the strategic level.

  2. Tambe, P., Cappelli, P., & Yakubovich, V. (2019). "Artificial Intelligence in Human Resources Management: Challenges and a Path Forward." California Management Review, 61(4), 15-42. Examines how AI affects organizational design, talent management, and workforce planning. Relevant to the talent dimensions of the AI operating model (Section 31.8) and the workforce communication challenges in Section 31.10.

  3. Ross, J.W., Beath, C.M., & Mocker, M. (2019). Designed for Digital: How to Architect Your Business for Sustained Success. MIT Press. Draws on research from MIT's Center for Information Systems Research to describe the architectural principles of digital transformation. The distinction between "operational backbone" and "digital platform" maps closely to the platform-vs-application distinction in Section 31.5. Useful for executives designing the technology architecture of an AI strategy.

Communication and Change

  1. Kotter, J.P. (2012). Leading Change, updated edition. Harvard Business Review Press. The classic framework for organizational change, applicable to AI transformation. Kotter's eight-step model -- particularly the emphasis on creating urgency, building a guiding coalition, and communicating the vision -- directly informs the change management dimensions of AI strategy communication (Section 31.10). We will revisit Kotter's framework in depth in Chapter 35.

  2. Bessen, J. (2019). Learning by Doing: The Real Connection Between Innovation, Wages, and Wealth. Yale University Press. An economic historian's argument that technology-driven productivity gains require extended periods of learning and organizational adaptation. The implication for AI strategy: the gap between AI deployment and AI value creation is measured in years, not months. Provides the patience perspective that counterbalances the urgency rhetoric in most AI strategy discourse.

Measuring AI Strategy

  1. Brynjolfsson, E. & McElheran, K. (2016). "The Rapid Adoption of Data-Driven Decision Making." American Economic Review, 106(5), 133-139. Empirical research demonstrating the link between data-driven decision making and firm performance. Provides the foundation for measuring AI strategy effectiveness at the organizational level. The methodology -- using large-scale surveys to correlate data practices with financial outcomes -- is the evidentiary basis for the "connect AI to competitive outcomes" principle in Section 31.12.

  2. McKinsey & Company. (2023). "The State of AI in 2023: Generative AI's Breakout Year." McKinsey Global Survey. The annual McKinsey AI survey, referenced throughout this textbook, provides the most comprehensive data on enterprise AI adoption, spending, value creation, and organizational practices. The 2023 edition's finding that 72% of organizations had adopted AI but only 22% had scaled it across functions is the empirical basis for the pilot purgatory analysis in Section 31.11.

Broader Strategic Perspectives

  1. Porter, M.E. (1996). "What Is Strategy?" Harvard Business Review, November-December 1996. Porter's classic article on the nature of strategy -- the distinction between operational effectiveness and strategic positioning -- remains the intellectual foundation for AI strategy. The argument that strategy is about choosing what not to do is directly relevant to the AI context, where the temptation to "do AI everywhere" is a specific instance of the strategic error Porter identified three decades ago.

  2. Rumelt, R. (2011). Good Strategy Bad Strategy: The Difference and Why It Matters. Crown Business. Rumelt's framework -- a strategy consists of a diagnosis, a guiding policy, and coherent action -- provides the analytical backbone for evaluating AI strategies. His concept of "bad strategy" (vague aspirations, refusal to choose, mistaking goals for strategy) maps directly to Companies A and B from the opening scenario.

  3. Christensen, C.M. (1997). The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail. Harvard Business Review Press. Christensen's disruption theory is highly relevant to AI strategy, particularly for incumbent firms facing AI-native competitors. The theory explains why established companies may rationally underinvest in AI (because their existing customers do not yet demand it) and why this rational response can be strategically fatal. Relevant to Athena's competitive challenge from NovaMart and to the competitive dynamics analysis in Section 31.3.

These readings span strategy theory, organizational design, competitive economics, governance, and communication. For executives short on time, start with Ng (2018) for a concise action framework, Iansiti and Lakhani (2020) for the competitive dynamics, and the NACD handbook (2024) for governance. For deeper engagement, Agrawal, Gans, and Goldfarb (2022) provides the most nuanced analysis of AI strategy timing, and Porter (1996) remains the indispensable foundation for understanding what strategy actually is.