The AI Transformation Playbook: my first book, and why I wrote it
Dixon, R.J., 2025d. The AI Transformation Playbook: A Step-by-Step Guide to Becoming an AI-Enabled Organisation. Manchester: Dixon AI Press. ISBN 978-1-9194005-0-1. 92 pages, paperback and Kindle, published 19 December 2025. Available at amazon.co.uk/dp/1919400508. In brief. The argument in one sentence: AI transformation is not a technology programme, it's a people, education, and change programme, and it unfolds in seven recognisable stages that organisations skip at their cost.
Why I wrote it
Three years ago last month, OpenAI put a large language model in front of anyone with an internet connection (OpenAI, 2022). Since then I've spent most of my working life inside organisations trying to respond to it: boardrooms, hackathons, strategy days, and a great many rooms where someone senior says "we know we need to do something about AI" and then goes quiet. Knowing you need to change and knowing how to change are two very different problems, and almost everything written about AI so far has been about the first.
Across hundreds of those engagements the same pattern kept appearing. Organisations that got somewhere moved through recognisable stages, in the same order, at different speeds. Organisations that stalled had usually tried to jump a stage, most often by buying tools before anyone knew what to do with them. Once you've seen that pattern fifty times you owe it to people to write it down. So this book is the route map I wish I could have handed every leadership team on day one.
What it argues
The book makes five claims, and everything else hangs off them.
First, the operating environment changed on 30 November 2022 and hasn't settled since. Planning horizons that used to run one to three years now run three to six months. It's a punctuated equilibrium in the biologists' sense (Gould and Eldredge, 1977): years of incremental change, then a jump. In that world the classical, deliberate model of strategy struggles, because it assumes you can predict. What you can do instead is what Sarasvathy called effectuation: when prediction is impossible, work from the means you control (Sarasvathy, 2001). For an organisation today, the thing you control is your AI capability.
Second, that capability has five dimensions and they multiply rather than add. I first set this out in an article last year (Dixon, 2024c), and the book builds on it: Organisational AI Capability = Talent × Purpose × AI Literacy × AI Tools × Data Infrastructure. The multiplication matters. If any one of those is effectively zero, so is the whole. High literacy with no purpose produces clever toys; expensive tools with no data produce generic output. Every time I've seen an organisation disappointed by AI, there was a zero somewhere in that equation.
Third, transformation runs through seven stages. The Starting Point, where you find out where you actually are. Leadership Commitment. Organisational AI Literacy, where the whole workforce learns a common language. AI Application and Experimentation, where people apply that literacy to their own work. Collaborative AI Experimentation, where teams build together. Strategic Integration, where AI stops being a fifth objective on the list and becomes part of how the first four get delivered. And the AI-Enabled Organisation, which isn't a destination so much as a way of operating. The stages are descriptive before they're prescriptive: this is what happened in the organisations that succeeded, and the order held.
Fourth, who, not what. The fastest movers asked "who do we need to empower?" long before they asked "which tools should we buy?" They invested in shared language, foundational literacy, and the psychological safety to experiment in the open. Curiosity followed literacy, experimentation followed curiosity, and value compounded from there, one saved hour at a time.
Fifth, the trap at the end is automating the past. The book borrows two examples: newspapers that used digital tools to streamline print production rather than reimagine news, and Yellow Pages, which digitised its listings rather than build search. An organisation that reaches stage seven and uses AI to make its pre-2022 processes a bit faster has missed the point. The defining characteristic of an AI-optimised organisation is that it can change as fast as its environment does.
How GEFIE runs through it
Readers who know Dixon AI's work will recognise the spine. The GEFIE model (Get Urgent, Explore, Formulate, Iterate, Embed) came out of the research I did with Sumona Mukhuty and Arvind Upadhyay on generative AI and workforce engagement (Mukhuty, Dixon and Upadhyay, 2025), and in the book it provides the rhythm for stages three to six: an accelerator day to get urgent and explore, a value discovery day to formulate, a hackathon to iterate, and a strategy workshop to embed.
Underneath the middle stages sits the Purpose, Execution, Judgement framework I published in the summer (Dixon, 2025a). Purpose, Execution, Judgement (PEJ) is a framework for human-AI collaboration that separates the human-led purpose (why), the shared execution (how), and the human-applied judgement (which output matters). It's the answer to the question every hackathon team eventually asks, which is where the human stops and the machine begins.
And for stage seven the book offers the three pillars of the AI-optimised organisation: governance that moves at the speed of the technology, continual capability development, and a three-layer project portfolio in which every employee builds small things, AI champions scout the frontier with pilots, and a few well-resourced strategic projects transform core workflows and deliver measurable return. To make progress visible, the book introduces a measure I've become rather attached to. A "Who Built What That Did Which Meant" sentence is a single verifiable act of innovation: a named person, an artefact they built, what it does, and what changed as a result. If any of the four parts is missing, it doesn't count. Count the sentences and you're measuring capability rather than logins.
Who it's for
Leaders and leadership teams who are past "should we?" and stuck on "how, exactly?" I wrote it to be read in an evening, because the people who most need it don't have a week. Use it diagnostically first, to work out which stage you're in and why the last initiative stalled, then strategically, to plan the next one. I'd also like it to earn a place on a strategy reading list, because it's an attempt to apply what we already know about leading change (Kotter, 1996) and the diffusion of innovations (Rogers, 2003) to the fastest environmental shift most of us will see in our careers.
If you've heard me speak over the last two years, you'll recognise the argument. The book grew out of the talks: the strategic drift line I drew for recruitment leaders last year (see Our Viking moment), and the "don't miss the boat twice" case I made at TEDxWarrington (see Don't miss the boat twice). What the book adds is the how.
An excerpt
From the closing chapter, on agency:
"Agency in this era is not the ability to slow the pace of technological change, but the capacity to act with intention inside it. The organisations that thrive in the period between ChatGPT and the emergence of AGI are those that learn to move with the environment rather than brace against it. They do not attempt to predict the future. They build the capability to adapt to whatever arrives. That capability is the foundation of agency in the modern organisation."
Where to get it
The paperback and Kindle editions are on Amazon. If you read it and it helps, tell me which stage you found yourself in. That's the data I'm collecting next.
References
Dixon, R.J., 2023. Don't miss the boat twice: my TEDx talk on the ChatGPT challenge. [online] robdixon.ai, 15 December. Available at: https://www.robdixon.ai/post/dont-miss-the-boat-twice-tedxwarrington [Accessed 4 September 2026].
Dixon, R.J., 2024. Our Viking moment: why the winning strategy changed on 30 November 2022. [online] robdixon.ai, 4 September. Available at: https://www.robdixon.ai/post/our-viking-moment-strategy-change-ai [Accessed 4 September 2026].
Dixon, R.J., 2024c. Defining AI Capability: A Holistic Framework for Organisational Readiness in an AI-Driven World. [online] LinkedIn, 18 November. Available at: https://www.linkedin.com/pulse/defining-ai-capability-holistic-framework-readiness-ai-driven-dixon-omrle [Accessed 4 September 2026].
Dixon, R.J., 2025a. Where Does the Human Stop and the AI Begin? Purpose - Execution - Judgement (PEJ): A Framework for Human-AI Interaction in the Post-ChatGPT Era. [online] LinkedIn, 30 June. Available at: https://www.linkedin.com/pulse/where-does-human-stop-ai-begin-purpose-execution-judgement-dixon-bn0se [Accessed 4 September 2026].
Dixon, R.J., 2025d. The AI Transformation Playbook: A Step-by-Step Guide to Becoming an AI-Enabled Organisation. Manchester: Dixon AI Press. ISBN 978-1-9194005-0-1. Available at: https://www.amazon.co.uk/dp/1919400508.
Gould, S.J. and Eldredge, N., 1977. Punctuated equilibria: the tempo and mode of evolution reconsidered. Paleobiology, 3(2), pp.115–151. Available at: https://doi.org/10.1017/S0094837300005224.
Kotter, J.P., 1996. Leading Change. Boston, MA: Harvard Business School Press. Available at: https://www.kotterinc.com/bookshelf/leading-change/.
Mukhuty, S., Dixon, R. and Upadhyay, A., 2025. Industry 5.0 era of digital supply chain: a generative artificial intelligence (GenAI) action model for workforce engagement. In: J. Vilko, S. Nazir, M. Ali and M. Torkkeli, eds. Technological Innovations and Industry 5.0. Amsterdam: Elsevier, pp.37–53. Available at: https://www.sciencedirect.com/science/article/abs/pii/B9780443338137000038.
OpenAI, 2022. Introducing ChatGPT. [online] OpenAI, 30 November. Available at: https://openai.com/index/chatgpt/ [Accessed 4 September 2026].
Rogers, E.M., 2003. Diffusion of Innovations. 5th ed. New York: Free Press. Available at: https://www.simonandschuster.com/books/Diffusion-of-Innovations-5th-Edition/Everett-M-Rogers/9780743258234.
Sarasvathy, S.D., 2001. Causation and effectuation: toward a theoretical shift from economic inevitability to entrepreneurial contingency. Academy of Management Review, 26(2), pp.243–263. Available at: https://doi.org/10.5465/amr.2001.4378020.
Further reading. Added September 2026: later work that builds on the book.
Dixon, R.J., 2026. The CEO's Guide to AI: what I told a room full of CEOs in Chicago. [online] robdixon.ai, 3 September. Available at: https://www.robdixon.ai/post/ceos-guide-to-ai-chicago-keynote [Accessed 4 September 2026].
Dixon, R.J., 2026b. Purpose, Execution, Judgement: A Framework for Human-AI Collaboration and Organisational AI Capability in the Post-ChatGPT Era. SSRN Working Paper. Available at: https://dx.doi.org/10.2139/ssrn.6282200 [Accessed 4 September 2026].
Dixon, R.J., 2026e. AI Innovation Architecture: Nested Purpose, Closed-Loop Dynamics, and the Chief Agency Officer. SSRN Working Paper. Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6546178 [Accessed 4 September 2026].
Dixon, R.J., 2026i. Stop Putting IT in Charge of AI (and 8 Other Mistakes Organisations Keep Making). [online] LinkedIn, 8 August. Available at: https://www.linkedin.com/pulse/stop-putting-charge-ai-8-other-mistakes-organisations-rob-dixon-ge0he [Accessed 4 September 2026].
Rob Dixon is Founder and CEO of Dixon AI Ltd and lectures at Manchester Metropolitan University. He writes and speaks on AI capability, strategy, and human-AI collaboration. Bibliography · Google Scholar · ORCID · LinkedIn · dixonai.com
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