The Breaking Point
Something Is Breaking Inside the Modern Corporation
It is not the balance sheet. It is not the brand. It is not the technology stack — those are mostly fine. What is breaking is harder to see on a slide deck and harder to fix with a budget line: the unwritten rules, shared assumptions, and organisational muscle memory that tell people how to behave, what to say, who to listen to, and what happens when you get it wrong.
Artificial intelligence did not create this tension. But it is making it impossible to ignore. A sweeping new index from KPMG, built from surveys of 300 C-suite leaders, analysis of earnings calls from 177 publicly traded companies, and hard capital data across six industry groups, puts numbers to what many executives are quietly living. The verdict is stark: 81% of executives say their boards have raised expectations for organisational adaptability. The organisations beneath them, in most cases, are not ready to meet it.
At the same time, the global technology sector has shed nearly 60,000 jobs in the first quarter of 2026 alone — 47.9% of those cuts attributed directly to AI and workflow automation. Oracle cut 18% of its workforce and redirected $8–10 billion to AI infrastructure in the same quarter. These are not isolated events. They are signals of a structural shift that most org charts were not designed to absorb.
"I don't want to be dramatic and say it's life or death. But I think it is life or death for some." — Atif Zaim, KPMG Deputy Chair
The Great Flattening
Middle Management Is the First Layer to Go
Gartner predicts that 20% of organisations will use AI to flatten their hierarchy by the end of 2026, eliminating more than half of current middle management positions. McKinsey calls it "The Great Flattening." Jack Dorsey and Roelof Botha have argued publicly that companies should function as "mini AGI" systems, leaving traditional corporate hierarchy behind. The Atlantic ran a piece asking whether AI is turning everyone into a middle manager — for a team of agents.
The logic is straightforward. Middle management exists, in large part, to move information up and down the hierarchy, to coordinate between teams, to translate strategy into execution, and to supervise output. Agentic AI can do all of those things — faster, at lower cost, and without the political friction. The question is not whether this will happen. The question is what replaces the human layer that disappears.
The KPMG data is unambiguous on one point: only 30% of executives say their organisation's structures, roles, and processes can reconfigure quickly as business needs change. Only 24% identified more dynamic talent deployment as a key change made over the last year. The C-suite has embraced the language of transformation. The org chart has not moved.
The Flattening in Numbers
Middle management positions at risk by 2027
Source: Gartner
of organisations using AI to flatten hierarchy by end of 2026
Source: Gartner
of executives say their org can reconfigure quickly
Source: KPMG Adaptability Index
The economist and organisational researcher Zak Kidd, co-founder of Ask Humans, frames the endpoint bluntly: "The future organisation is just equity holders and essential workers with LLMs in between." He argues that the management function of human beings becomes structurally redundant when large language models can perform the discernment work that justified it. It is a provocative claim. But the data suggests it is not as far off as most executives would like to believe.
The Culture Gap
Technology Investment Without Culture Change Is a Trap
Here is the finding that should concern every leader most: the industries most focused on innovation — most aggressive about new technologies, business model experimentation, and accelerating R&D — are not the most adaptable. The correlation is essentially zero.
Manufacturing and energy, which scores the highest strategic adaptability of any sector in the KPMG index at 71 out of 100, is not a sector known for radical reinvention. It adapts through disciplined scenario planning, centralised decision authority, and operational execution. TMT — the sector that most loudly evangelises transformation — scores 41 on cultural adaptability, near the bottom.
The pattern is consistent across the data. Companies are investing heavily in AI tools while systematically underinvesting in the human conditions that make those tools work. Executives are nearly twice as likely to increase technology spending as to invest in employee training. Fewer than 10% of executives identify increased psychological safety as one of the behaviours their organisation changed most in the past year. And 46% report burnout and change fatigue as an unintended consequence of their adaptability efforts.
"When you remove the management buffer and hand execution over to AI, the remaining humans are no longer the engine — they are the steering wheel. If your culture is misaligned, the AI will just scale your dysfunction at light speed." — Zak Kidd, Ask Humans
This is the innovation trap. You can deploy the best AI tools in the world into an organisation that does not have the culture to use them well — and what you get is not transformation. What you get is faster, more expensive versions of the same dysfunction you already had.
The Culture Investment Gap
Source: KPMG Adaptability Index 2026 — % of executives reporting each action as a top priority
The Agentic Shift
Agentic AI Is Not an Upgrade. It Is a Structural Change.
The conversation about AI in organisations has, until recently, been mostly about tools. Copilots. Assistants. Things that help individual workers do their jobs faster. That conversation is now changing — and the change is significant.
Agentic AI refers to systems that do not just assist but act. They plan, execute, coordinate with other agents, and make decisions across multi-step workflows — often without human intervention at each step. McKinsey's April 2026 research finds that nearly two-thirds of enterprises worldwide have experimented with agents, but fewer than 10% have scaled them to deliver tangible value. The bottleneck, in almost every case, is not the technology. It is the operating model.
Deloitte has just launched a dedicated Google Cloud Agentic Transformation Practice — a signal of how seriously the consulting industry is taking this shift. Google Cloud reports that its models now process more than 16 billion tokens per minute via direct API use, up sharply from a year ago. The infrastructure is ready. The organisations are not.
McKinsey identifies four steps for organisations preparing to scale agentic AI: identify high-impact workflows to "agentify," modernise data architecture, enforce data quality, and — critically — evolve the operating model. That last step is where most organisations stall. Human roles must shift from execution to supervision and orchestration. That is not a technology problem. It is a leadership and culture problem.
From Tool to Agent: What Changes
AI assists individual tasks
AI coordinates multi-step workflows autonomously
Human executes, AI supports
AI executes, human supervises and orchestrates
Value measured per task
Value measured across end-to-end workflow outcomes
Governance is optional
Governance is the foundation — without it, agents fail at scale
What Leaders Must Do
Five Things That Actually Matter Right Now
The research converges on a clear picture. The organisations that are navigating this well are not the ones with the most AI tools. They are the ones that have done the harder work of aligning culture, structure, and strategy before deploying the technology. Here is what that looks like in practice.
Stop treating AI as an IT project
The companies that are failing at AI transformation are the ones that handed it to the technology team and called it done. AI at scale is a leadership project. It requires decisions about roles, authority, culture, and governance that only senior leaders can make. If your AI strategy does not have a clear owner in the C-suite who is not the CIO, you have a structural problem.
Invest in culture before you invest in tools
The KPMG data is unambiguous: companies that invest in cultural adaptability outperform those that invest only in technology. That means psychological safety, tolerance for failure, and genuine training — not just access to tools. Fewer than 10% of executives report improving psychological safety as a priority. That number needs to change.
Redefine what management is for
If AI can coordinate, supervise, and synthesise information — the traditional functions of middle management — then the human manager's value must come from somewhere else. Judgment under ambiguity. Ethical reasoning. Relationship-building. Motivation. These are not soft skills. They are the new core competencies of management in an AI-augmented organisation.
Build governance before you scale agents
McKinsey's research is clear: the bottleneck to scaling agentic AI is almost never the model. It is data quality, governance, and operating model design. Organisations that rush to deploy agents without these foundations do not get transformation — they get automated chaos. Build the governance infrastructure first.
Measure adaptability, not just AI adoption
The companies with the highest shareholder returns in the KPMG index are not the ones with the most AI tools. They are the ones with the highest organisational adaptability scores — the ability to reconfigure structures, roles, and processes as conditions change. That is the metric that matters. Start measuring it.
The Real Question
When the Org Chart Breaks, What Holds?
The KPMG research includes a finding that has stayed with me since I first read it. The industries with the highest adaptability scores — manufacturing, energy, healthcare — are not the most innovative. They are the most disciplined. They have clear decision authority, consistent execution, and cultures that have been tested by disruption before. They know how to change because they have had to.
The technology sector — the sector that invented the tools now disrupting everyone else — scores near the bottom on cultural adaptability. It is the Kodak problem at scale. Kodak invented the digital camera. It was destroyed by it anyway. Not because it lacked the technology. Because it lacked the culture to deploy it.
The organisations that will navigate the next three years well are not the ones that move fastest. They are the ones that move with the most clarity about what they are actually trying to build — and what kind of organisation they need to be to build it. That clarity does not come from a new AI tool. It comes from leadership.
The org chart is breaking. That is not a technology problem. It is a leadership opportunity — and the window to take it is narrowing.
The data from BCG, McKinsey, KPMG, and Deloitte all points to the same conclusion: companies that made the cultural and structural bets — not just the technological ones — saw 4.4 times higher shareholder returns and nearly triple the revenue growth of more passive peers. The return on organisational transformation is not a soft number. It is the hardest number in the room.
The question is not whether AI will change your organisation. It already is. The question is whether you are leading that change — or just watching it happen.
Three Questions for Your Next Leadership Meeting
If AI takes over the coordination and information-flow functions of middle management, what is the human manager's job — and are we training for it?
Where in our organisation are we investing in technology without investing in the culture needed to use it well?
What does organisational adaptability actually look like for us — and how would we know if we had it?