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Rewired: McKinsey's Playbook on How Leading Companies Win with Technology and AI

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Rewired (2nd ed., 2026) — Lamarre, Smaje, Levin, Singla & Sukharevsky [SAMPLE INGEST]

Scope of this ingest (honesty disclosure per CLAUDE.md guardrail)

What was read: a 30-page sample of the ~600-page second edition (front matter + full table of contents + the “Your guide to Rewired” introduction + first 5 of the “Your transformation manifesto” themes + back-of-book Index for terminology reference).

What was NOT read: chapters 1–39 themselves, the four case studies in Section 7 (DBS Bank, Freeport-McMoRan, LATAM Airlines, Toyota Motor North America), and likely additional manifesto themes (only 5 are visible in the sample; the structure suggests more follow).

The wiki should treat this source page as authoritative for the framework structure, the 6-capability model, the headline numbers, and the editorial framing of the second edition — but not for the chapter-level content. Chapter-level claims will be added if/when the full book is ingested.

TL;DR

Second edition (2026) of McKinsey’s tech-and-AI transformation playbook by Eric Lamarre, Kate Smaje, Rob Levin (with Alex Singla and Alexander Sukharevsky). 40% of the second edition is entirely new content, another 25% substantially expanded — primarily reflecting the rise of generative and agentic AI since the first edition (2023).

Definition: “A Tech & AI transformation is the process of developing organizational and technology-based capabilities that allow a company to continuously improve its customer experience and lower its unit costs; and over time sustain a competitive advantage.”

Core thesis: technology is broadly available; advantage comes from enduring capabilities that allow a company to harness any technology effectively. McKinsey calls these rewired companies.

The 6-capability “Rewired” framework (Exhibit I.1, unchanged from the first edition; “timeless” core):

  1. Business-led roadmap — top-down aspiration, alignment on economic leverage points, reimagination of business domains
  2. Talent — upskilled business leaders + density of engineering talent
  3. Operating model — business, tech, and operations closer together for faster innovation cycles
  4. Technology — modern software engineering and platforms for reuse and time-to-value
  5. Data — unified, easy-to-consume data
  6. Adoption and scaling — change management; end-to-end process reconfiguration; impact measured in business KPIs

Companies must be strong across all six to win.

Headline empirical anchor (from study of ~200 companies, with 20 deep-dive AI leaders):

  • 20% EBITDA uplift on average from Tech & AI transformations
  • 1–2 year breakeven
  • $3 of incremental EBITDA for every $1 invested
  • Concentrated efforts on 1–3 business domains (not scattered use cases)

Key claims (from the introduction and first 5 manifesto themes)

What’s new in the second edition

“All told, 40% of this second edition is entirely new, and another 25% has been substantially expanded — an indication of just how fast the field has evolved in only three years.”

Two forces drive the update:

  1. Agentic AI introduces new capability requirements: integration of unstructured data; development of agentic workflows.
  2. Managerial practice has evolved — most notably in expectations that business leaders (not IT) reimagine their domain with technology, and in the depth of organizational change demanded for faster innovation cycles.

What “Rewired” is not about (per the introduction)

  • Not about broad adoption of AI tools in employees’ day-to-day work — “tool adoption quickly becomes table stakes.”
  • Not a catalog of use cases — focus is on how leaders decide where to apply technology and how to build/scale/sustain solutions.
  • Not a book on tools or algorithms — the authors deliberately spend little time on specific technology.

The 6 sections (with section-level new/expanded content noted)

SectionTitleWhat’s new in 2nd ed
OneCreating the Transformation RoadmapAgentic-AI workflow selection; lessons from agentic trailblazers; C-suite role mapping
TwoBuilding Your Talent BenchHuman–agent collaboration; new capabilities for an agentic world
ThreeAn Operating Model That Outruns the CompetitionThree operating-model archetypes; controlled transition guides; new chapters on faster enterprise blueprint and agentic operating model
FourTechnology for Speed and Distributed InnovationAI-transformed software development (copilots → autonomous agent factories); engineering capability for building agentic systems
FiveEmbedding Data EverywhereUnstructured data for GenAI/agentic apps; automated data protection in LLM world; data as strategic asset class
SixAdoption and Scaling: Your Force MultipliersHow agentic AI changes risk; engineering digital trust; course-correction when momentum stalls
SevenTransformation Journey Stories4 case studies — DBS Bank, Freeport-McMoRan, LATAM Airlines, Toyota Motor North America

Manifesto themes (5 of unknown N visible in the sample)

1. Technology alone doesn’t create advantage — enduring capabilities do

“The same companies that have been winning before by building capabilities that allow them to harness any technology effectively. We call them rewired companies.”

Anchors the book’s focus on capability building over tool adoption. Section One.

2. Economic leverage points are your best focal points

Examples from the case-study companies (Section Seven, not yet read):

  • Freeport-McMoRan — process yield and throughput in mining
  • Toyota Motor North America — supply-chain integration in automotive

“Most companies have long lists of scattered use cases. Successful ones focus on achieving deep business transformation in the few areas that strategically matter.”

3. If the value you’re creating doesn’t move the business, you’re getting it wrong

The headline empirical numbers:

  • ~20 leader companies studied in depth (across industries)
  • 20% EBITDA uplift average
  • 1–2 year breakeven
  • $3 of incremental EBITDA per $1 invested
  • Concentration on 1–3 business domains
  • “Stage-gated” investments

4. Building the Tech & AI muscle of your senior business leaders should be a top priority

“We don’t have a single success story where senior business leaders were not in the driver’s seat.”

Business leaders 1–3 levels below the CEO who combine domain expertise with tech/data/AI know-how are the formidable business transformers. IT leaders support; business leaders drive.

5. Every Tech & AI transformation is a people transformation — the 70% shifts

McKinsey’s named talent-density target:

  • 70%+ of talent in-house (vs outsourced)
  • 70%+ “doer” engineers who build software-based solutions (vs PMs/consultants)
  • 70%+ at higher skill levels (competent or expert)

“Small, highly skilled teams that outperform large armies of lower-skilled staff.”

In the agentic era, the role shift continues:

  • Engineers move from routine coding → architecture, workflows, constraints, quality controls
  • Business leaders move from task management → setting objectives, success metrics, trade-offs
  • “Fewer people doing higher-leverage work, with clearer accountability and faster learning loops”

(Themes 6+ are not in the sample.)

Methodology (per the introduction)

  • ~200 companies studied
  • ~20 deep-dives across industries
  • Three years of work since the first edition
  • Authors’ McKinsey client-engagement experience as the primary data source
  • “These companies define our current frontier of best practice.”

AI-tooling acknowledgement (notable disclosure)

“Adobe Firefly was used to assist in the creation of section opener images. GenAI was used as an editorial aid for structuring and concision. All use of GenAI tools was in support of original McKinsey thinking, authorship, and creation. Any relevant outputs were reviewed, edited, validated against original sources, and cross-checked for accuracy by McKinsey editors.”

(Compare with the 2026 AI Index’s similar but more detailed disclosure.)

Vocabulary visible in the back-of-book Index (terminology preview only)

The full chapters use these named concepts (page references from the book’s Index, which is in the sample even though the chapters are not):

  • 70% shifts / 30–70 shifts — talent density target
  • Domain-to-value delivery (DVD) — McKinsey’s process model (compared with SDLC)
  • Strategic flywheels — value-loop concept
  • Stage-gating — investment pacing
  • Talent migration plan / Talent win room (TWR)
  • Tech talent skill pyramid
  • Three-dimensional value sizing assessment
  • T-shaped data transformation approach
  • Two-pizza teams (Bezos-derived)
  • Two-tier talent model
  • Value driver trees
  • Strangler fig approach (modernization)
  • Domain & platform model, Digital factory model, Distributed operating models, Enterprise-wide model — the operating-model archetypes
  • Workbenches (engineering)
  • Zero-cost thinking (GenAI implication)
  • Eval types: semantic similarity, retrieval accuracy, task success rate

These are McKinsey-branded vocabulary; the wiki should not commit to them as standalone concepts until the chapters that define them have been ingested.

Cross-references to existing wiki entities

The Index reveals at least two entities already in the wiki are referenced:

  • Peter Weill (book p. 26) — referenced in the technology / platforms discussion. Already a wiki entity from MIT CISR work.
  • Stephanie Woerner (book p. 286) — referenced in the platforms discussion. Already a wiki entity from MIT CISR work.

This is a real cross-pollination: McKinsey’s “Rewired” framework cites MIT CISR’s research on platforms, and the wiki already has the MIT CISR side via 2026-04-28-mit-sloan-ai-maturity. A future deep-read of Chapter 17 (“The platforms that power the enterprise”) and Chapter 18 (“Make your tech flexible and scalable—hello APIs!”) will deepen this cross-link.

Methodology notes

  • Source format: 30-page library/OverDrive PDF sample of a Wiley book (filename pattern L-NNNNNNNN-pdf*.pdf). Per CLAUDE.md guardrail, this is a sample — the full book has not been read.
  • Cover and copyright confirm: Eric Lamarre + Kate Smaje + Rob Levin (with Alex Singla and Alexander Sukharevsky); Wiley 2026; ISBNs 9781394381906 (Cloth) / 9781394381913 (ePUB) / 9781394381920 (ePDF).
  • First edition (2023) is referenced throughout; this 2nd edition is described as 40% new + 25% substantially expanded.

Limitations / caveats

  • Sample-only ingest: confidence is limited to the framework, the 6 capabilities, the manifesto themes 1–5, and the headline numbers. Chapter-level claims (operating-model archetypes, agentic workflow selection methodology, the four case studies) are deferred.
  • Single-vendor framework: McKinsey is the publisher; the empirical claims (20% EBITDA, $3:$1 ROI, 1–2 year breakeven) are from McKinsey client work — vendor-of-deployment data, not independent measurement. Independent corroboration would come from non-consulting field studies.
  • Selection effect on the “20 deep-dive companies”: these are described as “world beaters … leaders in AI” — i.e. the top of the distribution, not a representative sample.
  • Manifesto themes 6+ are not yet read.

Quotes worth saving

“Business leaders will be transforming their companies with technology for the rest of their careers.” — opening line, repeated from the first edition

“Rewired is not about the broad adoption of AI tools in employees’ day-to-day work. That’s important but tool adoption quickly becomes table stakes. Competitive advantage comes from something much harder: reimagining core business processes, products, and services end to end with AI systems developed and embedded at the core.”

“Imagine what becomes possible with 20 times more software development output for the same budget. Digital products, services, and business models that were once uneconomical—or simply impossible—suddenly are within reach.”

“The capabilities become the competitive advantage.”

“We don’t have a single success story where senior business leaders were not in the driver’s seat. IT leaders can support, of course, but it’s business leaders who need to drive the transformation.”

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