In April 2026, McKinsey published the second edition of Rewired: The McKinsey Playbook on How Leading Companies Win with Technology and AI, the revised and expanded update McKinsey markets as Rewired 2.0. Co-authored by QuantumBlack leaders and McKinsey AI veterans, it's an expanded guide built on six enterprise capabilities: transformation roadmapping tied to real value, a talent bench of skilled experts, an operating model that moves at pace, a flexible distributed technology environment, embedded data, and disciplined scaling and adoption.
The book offers fresh analysis on agentic workflows, the new economics of technology and AI transformation, and industrialising AI safely at scale. If you are leading digital or AI change in a Benelux enterprise - especially in revenue-critical stacks like Adobe Marketo Engage, Salesforce, or Microsoft Dynamics - this is relevant executive reading.
But McKinsey's playbook, like most strategic frameworks in this space, concentrates on the how of transformation. The question it does not fully answer is: where does durable economic value actually accumulate?
"The organisations capturing AI value in 2026 are not the ones with the most advanced AI tools. They are the ones with the most governed foundations underneath those tools."
Three things happened after this piece was published, and all three sharpen the argument. McKinsey's own survey data now measures the leak. The EU moved its high-risk AI deadlines to December 2027 and left the transparency duties in place. And the State of Martech 2026 report put the commoditisation thesis plainly: LLMs commoditised faster than enterprise context did, and what is not commoditised is the proprietary context wrapped around them.
Where the Rewired playbook is strong
McKinsey's six-capability framework is grounded in real transformation data. The emphasis on C-suite alignment, talent, and disciplined adoption addresses the execution failures that kill most enterprise technology programmes before they produce results. The research on AI industrialisation - moving from pilots to scalable production - is particularly relevant for enterprise marketing leaders facing board pressure to show AI ROI in 2026.
The updated analysis on agentic workflows is also timely. Early enterprise deployments of autonomous agents are surfacing a consistent pattern: agents that work in sandboxed demonstrations fail in production because they cannot connect to governed data, comply with consent frameworks, or hand off reliably to execution systems. McKinsey's emphasis on building the right technological foundation before scaling agents reflects what practitioners are observing in the field.
The question Rewired doesn't fully answer
What the Rewired playbook does not make explicit is the structural reason why foundation investment is systematically underfunded in enterprise organisations. It describes what good looks like. It does not explain why the gap between what good looks like and what most stacks actually look like persists despite years of transformation investment.
The Value Gravity™ framework offers an explanation. Enterprise marketing stacks have three layers with fundamentally different economic properties. The AI Capability layer at the top has the highest innovation velocity and the lowest switching cost. New tools appear constantly, capability gaps between platforms close quickly, and this is where vendor marketing concentrates and where budget announcements are made.
The Commercial Foundation at the base - CRM, CDP, identity resolution, data governance, consent management - has the highest switching cost and the highest integration depth. Value accumulates here slowly, over years, through data quality work and governance decisions that are operationally intensive and rarely promotable. But every layer above depends on it.
Investment follows announcement velocity. Announcement velocity is highest at the top. Foundation work has almost no announcement velocity at all. This is the structural reason why enterprise investment concentrates upward while the foundation remains underdeveloped - not through poor decision-making, but through the structural logic of how technology budgets get approved and where executive attention lands. The same pattern shows up when a board hands down an AI mandate without a foundation assessment.
McKinsey's own numbers now measure the leak
McKinsey's State of AI survey, published in November 2025, found that 23 percent of organisations are scaling an agentic AI system somewhere in the enterprise, and that in any given business function no more than 10 percent have reached scale. Thirty-nine percent of respondents attribute any level of EBIT impact to AI, and most of those put it below 5 percent of enterprise EBIT. Value enters at the top of the stack and stops somewhere before the bottom line.
The trust survey published in March 2026, covering roughly 500 organisations, points at where it stops. Nearly two-thirds name security and risk as the top barrier to scaling agents. Organisations with explicit accountability for responsible AI score 2.6 on maturity against 1.8 for those without it. Neither of those is a model capability problem. Both sit in the Foundation layer, where the switching cost is highest and the announcement velocity is zero.
This is the pattern the Value Gravity™ framework predicts, and it is the same gap I described in the AI ROI gravity problem. Capability at the top is abundant and cheap to acquire. The governed data, identity and consent underneath it are scarce, slow and expensive, and they decide whether anything above them reaches production.
Rewired says what to build.
Gravity says where it lands.
Money and attention flow up. Value sinks down. The gap between them is the Gravity Leak, and Rewired is how you close it.
The six Rewired capabilities at a glance. Strategic road mapping. Talent building. Agile operating models. Distributed technology platforms. Embedded data. Disciplined scaling and adoption.
What this means for enterprise leaders in 2026
Platforms are becoming AI's orchestration spine. Enterprise systems are not being replaced by agents. They are evolving into the stable, governed layer that agents plug into. Winners will be the organisations that own the context plane - governed data and workflow infrastructure - not just the tools at the top.
Governance deadlines moved. The foundation work did not. The Digital Omnibus on AI entered into force on 27 July 2026 and pushed the AI Act's high-risk obligations from 2 August 2026 to 2 December 2027, with systems embedded in regulated products following in August 2028. Transparency duties under Article 50 still started on 2 August 2026. Eighteen extra months does not build an identity graph, a consent model or a data lineage anyone can defend under questioning. It changes when you get asked about it. Proactive data, identity and policy governance is still a speed advantage in regulated B2B sectors, and the deferral quietly raised the price of filing it under compliance cost. The Cyberbeveiligingswet, meanwhile, did not move at all.
Ecosystem depth beats isolated features. Hybrid stacks win when orchestrated well. AI tools that cannot connect to governed data and execution layers will not deliver at enterprise scale, regardless of the capability they demonstrate in controlled environments.
McKinsey's Rewired is worth reading. But the most important question it raises - whether the foundation beneath your AI investment is ready to support what you're building on top of it - requires a diagnostic answer, not a strategic framework. That is the question the Gravity Scan is designed to address.
Frequently asked questions
What is McKinsey's Rewired 2.0?
The second edition of Rewired, published April 2026, built on six enterprise capabilities: transformation roadmapping tied to real value, a talent bench of skilled experts, an operating model that moves at pace, a flexible distributed technology environment, data embedded throughout the organisation, and adoption and scaling that make solutions pay. It adds analysis on agentic workflows and industrialising AI at scale.
Did the EU AI Act high-risk deadline change in 2026?
Yes. The Digital Omnibus on AI entered into force on 27 July 2026 and moved stand-alone high-risk obligations to 2 December 2027 and embedded systems to 2 August 2028. Transparency obligations under Article 50 still applied from 2 August 2026.
Why does enterprise AI investment concentrate in the top layer of the stack?
Investment follows announcement velocity, and announcement velocity is highest where new tools appear fastest. Foundation work, meaning data quality, identity resolution, consent and governance, produces almost no announcements, so it is systematically underfunded even when every layer above depends on it.
What does Value Gravity™ add to the Rewired playbook?
Rewired describes what to build. Value Gravity™ describes where the value lands once it is built, and why it drains out of the layers with the lowest switching cost.
Where does AI value actually accumulate in an enterprise stack?
In the Value Gravity™ model, durable value accumulates in the Foundation layer, meaning the data model, identity, consent and governance that live in CRM, CDP and data platforms, because that layer has the highest switching cost and integration depth. The Experience layer at the top has the most innovation velocity and the lowest switching cost, so value there commoditises quickly.
Is the Value Gravity™ model a five-layer framework?
No. Value Gravity™ uses three layers: the Experience layer at the top, the Orchestration layer in the middle, and the Foundation layer at the base where durable value accumulates.
The Gravity Scan maps your marketing stack across 28 assessment areas - identifying where foundation maturity is sufficient, where it is not, and what to address before the next AI investment decision.
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