Appendix C: Further Reading

This appendix points to the most useful books, papers and resources for managers who want to go deeper on specific topics. Each entry includes a brief note on what makes it worth reading and, where relevant, which part of this book it relates to. Resources are organized by theme rather than chapter, since most span more than one area.

The AI landscape moves quickly. Online resources in particular may have changed since this list was compiled. Where possible, pointers are to primary sources or established publications that are likely to remain accessible.

Understanding AI: The mental model

For managers who want a deeper technical foundation without becoming engineers, these resources explain how large language models work at a level that is genuinely useful without being overwhelming.

“Attention Is All You Need” - Vaswani et al. (2017) The original research paper describing the transformer architecture that underlies most modern AI systems. Technically demanding, but reading the abstract and introduction gives a sense of the intellectual leap that made current AI possible. Available free online.

“A Jargon-Free Explanation of How AI Large Language Models Work” - Timothy B. Lee and Sean Trott, Ars Technica (2023) A plain-language walkthrough of how language models represent words, process text and learn from training data. Available free online.

“What Is ChatGPT Doing… and Why Does It Work?” - Stephen Wolfram (2023) A long, careful walkthrough of how language models work, from someone who has thought deeply about computation. More demanding than the Ars Technica piece but more thorough. Available free on Wolfram’s website.

AI strategy and leadership

For the strategic and leadership dimensions covered in Chapters 9 and 10.

“Competing in the Age of AI” - Marco Iansiti and Karim Lakhani (Harvard Business Review Press, 2020) Written before the current generation of generative AI but prescient about the organizational transformation AI requires. Strongest on the economics of AI-native businesses and what they imply for incumbents.

“Power and Prediction: The Disruptive Economics of Artificial Intelligence” - Ajay Agrawal, Joshua Gans and Avi Goldfarb (Harvard Business Review Press, 2022) A rigorous treatment of AI as a technology that reduces the cost of prediction and what that means for organizational strategy. Useful for thinking through where AI creates competitive advantage and where it does not.

Governance and risk

For the governance frameworks and risk management covered in Chapters 3, 4 and 5.

“Algorithmic Accountability: A Primer” - Robyn Caplan et al., Data & Society (2018) A clear introduction to the governance challenges posed by algorithmic decision-making. Available free from the Data & Society website. Relevant to the bias and accountability discussions in Chapters 4 and 5.

“Responsible AI Practices” - Google AI Google’s published principles and practices for responsible AI development. Useful as a reference for organizations developing their own frameworks. Available free on Google’s AI website.

EU AI Act - European Parliament and Council (2024, as amended) The full text of the EU’s landmark AI regulation. Dense, but its risk classification framework is directly relevant to any organization assessing its AI governance obligations in European markets. Available free on the EUR-Lex database.

“NIST AI Risk Management Framework” - National Institute of Standards and Technology (2023, with subsequent updates) The US government’s framework for managing AI risk. Comprehensive and well-structured. Particularly useful for organizations in regulated sectors or those supplying to government. Available free on the NIST website.

“The Age of Surveillance Capitalism” - Shoshana Zuboff (PublicAffairs, 2019) A challenging read, but useful context for managers thinking about data governance and the political economy of data. Its relevance to AI is indirect. Most relevant to Chapters 4 and 5.

Augmentation and the future of work

For the people and organizational dimensions covered in Chapters 9 and 10.

“The Technology Trap” - Carl Benedikt Frey (Princeton University Press, 2019) A historical perspective on how technology transitions affect labor markets. Sobering and rigorous. Useful context for managers navigating the augmentation conversation with their teams.

“The Work of the Future: Building Better Jobs in an Age of Intelligent Machines” - David Autor, David Mindell and Elisabeth Reynolds (MIT Press, 2022) An empirically grounded examination of how AI and automation are reshaping labor markets, with policy implications. Useful for managers thinking about workforce strategy.

The wider debate

This book doesn’t take a position on the long-term questions about AI, but readers who want to explore them have good starting points.

“Human Compatible: Artificial Intelligence and the Problem of Control” - Stuart Russell (Viking, 2019) Written by one of the leading AI researchers, this book argues for a fundamental rethink of how AI systems are designed to ensure they remain aligned with human values. Highly readable for non-specialists, though it is a long-term view of AI rather than a management guide.

Sector-specific resources

For the sector context covered in Chapter 7. These are starting points rather than comprehensive guides - each sector has a substantial literature of its own, and regulators and professional bodies update their guidance regularly, so check the current position for your jurisdiction.

Financial services: Financial regulators and standard-setting bodies, including the Financial Stability Board and national central banks and supervisors, publish reports and guidance on AI in financial services.

Healthcare: Health services, health ministries and medical device regulators in most countries publish guidance on AI in healthcare that is accessible to non-technical readers.

Legal services: Bar associations and law societies, such as the Law Society in the UK and the American Bar Association, publish guidance on AI in legal practice that covers professional obligations as well as practical applications.

Public sector: Government digital and AI offices in many countries publish frameworks and case studies for AI in government that are relevant to public sector managers.

Retail, manufacturing, media and professional services: Trade associations and professional bodies in your sector are the best starting point, alongside the relevant regulators, such as consumer protection authorities, health and safety bodies, copyright and press bodies, and accounting or engineering institutes.

Practical prompting and briefing

For the practical skills covered in Chapter 2.

“Prompt Engineering Overview” - Anthropic documentation Anthropic publishes detailed guidance on effective prompting for Claude. Practical and frequently updated. Available free on the Claude documentation site (docs.claude.com). Other AI vendors publish comparable guides for their own models.

“Prompt Engineering Guide” - DAIR.AI A comprehensive open-source guide to prompt engineering techniques. More technical than Chapter 2 of this book but a useful reference for teams who want to systematize their briefing practices. Available free at promptingguide.ai.

Staying current

The AI landscape changes faster than any book can track. These sources are worth following for ongoing developments.

The Gradient - A publication covering AI research and applications for informed non-specialists. Consistently high quality.

Import AI - Jack Clark’s weekly newsletter on AI developments. Technical but accessible to motivated non-specialists.

AI Snake Oil - Arvind Narayanan and Sayash Kapoor’s newsletter and book on AI limitations and hype. Essential counterweight to vendor claims and media coverage.

MIT Technology Review - Reliable, balanced coverage of AI developments with appropriate skepticism about hype.

This reading list reflects resources available at the time of writing. The AI field moves quickly - check for more recent editions and resources, particularly for regulatory and governance topics.