The Five Value Levers (AHEAD)

Definition

There is no single way for a company to capture value from AI and data — there are five, and they pull in different directions: Automate (replace human-run processes with the new machine), Halo (instrument products/people/places to generate new data-driven revenue), Enhance (use the machine to amplify a skilled worker's output), Abundance (use the machine to cut price by 10x or more and open new mass markets), and Discovery (use the machine to power genuinely new R&D and invention). The book's central strategic claim is that a company must identify which of its initiatives sit in which bucket and resource each one differently, rather than running one generic "digital transformation."

In the Book

Chapter 6 introduces the framework right after cataloguing "four traps" that kill digital initiatives (treating digital as a side project, chasing every shiny technology, ignoring process integration, and "digital denial" — the belief that "our industry is different," rebutted with a melting-point metaphor). The five levers are then previewed with a specific business each is meant to unlock: robotic process automation in claims processing and accounts payable (Automate, detailed in Chapter 7); instrumenting products for new commercial models (Halo, Chapter 8, and the book's earlier Code Halos work); machine-augmented frontline workers (Enhance, Chapter 9); 10x/100x price reductions that create markets of abundance (Abundance, Chapter 10, illustrated later by Narayana Health's low-cost heart surgery); and R&D reinvented around AI (Discovery, Chapter 11, illustrated by the analogy of the Model T spawning suburbia, big-box retail, and fast food — markets nobody could have predicted from the original invention). The book names GE, Philips, McGraw-Hill, Nike, Under Armour, and Toyota as companies pursuing several of these levers simultaneously as "hybrid winners."

Why It Matters

Splitting "digital strategy" into five distinct value levers prevents an organization from applying one KPI, one team structure, or one funding model to fundamentally different kinds of bets — cost-cutting automation, data monetization, worker augmentation, market-expanding price cuts, and speculative invention each have different time horizons and success metrics, and conflating them is a common reason transformation programs stall. The taxonomy is a template for sorting any portfolio of technology-driven initiatives, not just AI ones.