The Three M's (Materials, Machines, Models)

Definition

The book's organizing claim is that becoming "digital" requires aligning three elements at once: new raw materials (today, the data generated by instrumenting people, places, and things), new machines (systems of intelligence that turn that data into decisions and action), and new business models (the commercial structures built to monetize the first two). Historically, no industrial revolution has run on just one of these — coal and iron without the steam engine and factory system produced nothing, and the same logic applies to the current AI-driven build-out.

In the Book

Chapter 2 introduces the Three M's while making the case that century-old and even 40-year-old incumbent companies are "extraordinarily well-positioned" for the coming digital boom, because they already understand their markets, products, and regulations, and hold the physical assets worth instrumenting — the missing piece is alignment. The book then devotes an entire chapter to each M in turn: Chapter 4 to the machines (systems of intelligence), Chapter 5 to the materials (data as a raw material, argued to be "better than oil" because it doesn't deplete with use), and Chapter 6 to the models (the five AHEAD value levers, plus the four traps that derail model-building even when the other two M's are in place). The "Connect the Three M's" lesson recurs concretely in the Chapter 8 case study of Discovery Limited, the South African insurer, which is credited with aligning new data (health-behavior instrumentation), a new machine (the systems analyzing 250 people's worth of pattern-hunting), and a new business model (a wellness-plus-insurance company) simultaneously.

Why It Matters

The framework is a diagnostic for stalled transformation efforts: if a data initiative, an AI platform investment, or a new digital business model isn't paying off, the Three M's lens asks whether the other two elements are actually in place, since having only one or two out of three has historically produced very little economic lift on its own. It generalizes past AI specifically to any moment when a new resource and a new production technology both appear — the payoff still waits on a matching business model.