When McKinsey Cuts Jobs, Boards Take Notes

McKinsey’s decision to cut thousands of roles as AI reshapes its operating model signals a structural shift in how professional services value work, talent and delivery.

Why This Decision Matters More Than It First Appears

When The Times reported that McKinsey & Company is preparing to eliminate thousands of roles as artificial intelligence advances, the headline framed it as another chapter in the consulting sector slowdown. That reading misses the point.

This is not simply a cyclical adjustment or a cost-cutting response to softer demand. It is something more consequential. McKinsey has spent decades advising boards on workforce optimisation, operating model redesign and efficiency-driven transformation. Now it is applying those same prescriptions to itself. That is the moment worth paying attention to.

When the firm that has defined “corporate best practice” decides that automated workflows and large language models can replace a significant proportion of its support workforce, it decisively shifts the conversation from experimentation to expectation.

For many boards, this decision does not require further validation. It provides it.

From Advice to Evidence

McKinsey’s reported plan is to reduce its workforce by roughly ten per cent, primarily targeting centre-based and support functions rather than client-deployed roles. This follows earlier reductions in technology and knowledge teams and a broader recalibration after years of rapid expansion.

The firm grew from approximately 17,000 employees in the early 2010s to more than 45,000 at its peak. That growth model was linear. More clients meant more people. More people meant more leverage. That logic no longer holds.

Artificial intelligence has materially altered the economics of knowledge work. Tasks that once justified large teams of analysts, coordinators and internal specialists can now be handled faster and more consistently by automated systems. Research synthesis, benchmarking, data preparation, and presentation assembly are increasingly being carried out with machine assistance.

This is not theoretical. McKinsey is demonstrating it inside its own payroll. The implication for other organisations is difficult to ignore. If McKinsey can reduce back-office headcount by ten per cent while maintaining client delivery capability, many boards will reasonably ask why their own organisations cannot do the same.

The Quiet Redefinition of “Essential” Work

One of the most telling aspects of McKinsey’s approach is not the scale of the reductions but where they are occurring.

Leadership has been explicit in protecting roles that are directly involved in client delivery while cutting those concentrated in internal operations. This distinction is revealing.

It suggests that job security is increasingly tied to proximity to the customer and to outcomes that cannot be standardised. Roles that exist primarily to support internal processes are more likely to be automated. Roles that operate in messy, context-specific environments remain harder to replace.

For project delivery professionals, this distinction matters. Internal reporting, governance coordination, routine PMO activity and centralised support functions have historically been treated as necessary overhead. AI now challenges that assumption. If information can be generated, tracked and summarised automatically, the human value must come from interpretation, decision making and intervention.

The dividing line is becoming clearer. Work that protects existing operations without materially shaping outcomes is vulnerable. Work that actively creates value, resolves ambiguity and enables delivery under constraint remains in demand.

Growth Without Headcount Is No Longer Theoretical

For much of the past two decades, organisational growth and workforce growth were tightly coupled. Scaling revenue typically meant scaling teams. Managers accumulated influence through the number of people they led.

McKinsey’s shift signals a break from that model. The firm is now attempting to grow revenue while flattening or shrinking headcount, relying on technology to increase productivity per person. This is not unique to consulting, but it is obvious here because of McKinsey’s influence.

The implication for project delivery leaders is significant. Authority and credibility are no longer derived from team size. The efficiency and effectiveness of the delivery stack are increasingly measured.

Leaders who can orchestrate smaller, competent teams supported by intelligent systems will outperform those who rely solely on scale. The emerging archetype is not the large delivery organisation, but the compact, outcome-focused team with disproportionate impact. This is not an aspirational slogan. It is a structural response to changing cost dynamics.

Pressure on the Consulting Business Model

There is another dimension to McKinsey’s decision that deserves attention. Consulting revenue has remained relatively flat in recent years despite expanding capability. Clients have become more discerning about what they are willing to pay for.

If artificial intelligence can perform much of the work traditionally assigned to junior consultants, the economic justification for billing that work at premium hourly rates weakens. Many organisations are now using their own AI tools to conduct research, generate analyses and prepare internal materials. This erodes margins.

Staff reductions are therefore not only about efficiency gains. They are about protecting profitability in a market where the value of commoditised knowledge work is declining.

Any industry that relies heavily on selling time rather than outcomes should take note. When technology compresses production costs, pricing power follows.

What This Means for Project Delivery Professionals

For those responsible for delivering complex initiatives, McKinsey’s internal changes offer several practical lessons.

First, it is no longer sufficient to manage work. The expectation is to shape outcomes. Delivery roles must demonstrate clear contribution to business value rather than procedural compliance.

Second, skill profiles matter more than titles. Fluency in data, automation and AI-assisted tools is becoming the baseline. The differentiator lies in judgment, integration and leadership under uncertainty.

Third, organisational design must evolve. Delivery models built around layered coordination and manual reporting will struggle to justify their cost. Leaner structures supported by intelligent systems will become the norm.

These are not abstract trends. They are already being enacted by the organisations that advise others on transformation.

A Note on Perspective

Our view is that the most critical signal here is behavioural rather than rhetorical. What organisations do with their own workforce matters more than what they publish in thought leadership. McKinsey has provided a working example of how AI changes the internal calculus of value, cost and capability.

For project delivery professionals, this moment calls for an honest assessment. Which parts of your role would still exist if automation removed routine coordination and reporting? Where do you add value that technology cannot replicate? Answering those questions early is far preferable to having them answered for you.

“Artificial intelligence is reshaping how we operate and how our clients expect value to be delivered.” McKinsey spokesperson, The Times

“Consulting firms are under pressure as clients adopt AI tools internally and reduce reliance on external support.” Financial Times

What To Do Next

If you are leading projects, programmes or portfolios, now is the time to audit your delivery model. Identify where automation can remove friction and where human expertise genuinely drives outcomes. Invest in the skills and structures that make your teams indispensable rather than merely necessary.

This is not about reacting to a headline. It is about preparing for a structural shift that is already underway. If you want to continue exploring how these changes affect modern project delivery, this is the kind of analysis we focus on. Subscribe to Project Flux.