How AI Is Forcing McKinsey and Its Peers to Rethink Pricing

nThe hour is up

For the better part of a century, the economics of management consulting have rested on a beautiful fiction: that the value of advice can be measured in time. An analyst’s hours, a partner’s days, a team’s weeks on site — these were the denominator around which entire firms were built, pyramids of talent whose profitability depended on billing more hours than competitors at rates clients would reluctantly accept. The fiction held because nobody had a better alternative.

Artificial intelligence has now supplied one.

The pressure is visible in the numbers, in restructured partner pay, and in the quiet desperation with which firms like McKinsey, BCG, and Bain are repositioning themselves not as advisers but as delivery partners. The consultancy industry’s pricing model — the bedrock of a $700 billion global market — is cracking. The question is not whether it will change. It already is. The question is who benefits.

A familiar disruption, an unfamiliar pace

The consulting industry has survived disruptions before. Offshoring squeezed margins in the 2000s. The post-2008 austerity wave hammered public-sector mandates. The pandemic briefly collapsed travel-dependent engagement models. Each time, the billable-hour survived, battered but intact.

This time is structurally different. What AI is compressing is not demand for advice — that remains robust — but the labour input required to produce it. The Management Consultancies Association’s January 2026 member survey found that 77% of UK consulting firms have already integrated AI into their systems, with 76% deploying it specifically for research tasks and 68% having increased automation of core workflows. Meanwhile, the global AI consulting and support services market, valued at $14 billion in 2024, is forecast to expand at a compound annual growth rate of 31.6% to reach $72.8 billion by 2030 — a trajectory that reflects how thoroughly the tools are reshaping both supply and demand.

When AI compresses the time required to produce work, hourly billing stops being a proxy for value. It becomes a liability.

The AI consulting pricing model is already shifting — and McKinsey is leading it

In November 2025, Michael Birshan, McKinsey’s managing partner for the UK, Ireland, and Israel, made an admission that would have been unthinkable five years ago. Speaking at a media briefing in London, Birshan told reporters that clients were no longer arriving with a scope and asking for a fee. Instead, they were arriving with an outcome they wanted to reach and expecting the fee to be contingent on McKinsey’s ability to deliver it. “We’re doing more performance-based arrangements with our clients,” he said. About a quarter of McKinsey’s global fees now flow from this outcomes-based pricing model.

That 25% figure is both significant and revealing — significant because it marks a genuine departure from decades of billable-hour orthodoxy, revealing because it shows that three quarters of McKinsey’s revenue remains anchored to the old model. The transition is real. It is not complete.

The driver is largely internal. McKinsey’s Lilli platform — an enterprise AI tool rolled out firm-wide in July 2023 — is now used by 72% of the firm’s roughly 45,000 employees. It handles over 500,000 prompts a month, auto-generates PowerPoint decks and reports from simple instructions, and draws on a proprietary corpus of more than 100,000 documents, case studies, and playbooks. By McKinsey’s own reckoning, Lilli is saving consultants 30% of their time on research and knowledge synthesis. When a tool saves 30% of the hours that used to justify an invoice, the invoice requires a different rationale.

BCG has pursued a parallel path. Its internal assistant “Deckster” drafts initial client presentations from structured datasets in minutes. BCG disclosed in April 2026 that roughly 25% of its $14.4 billion 2025 revenue — approximately $3.6 billion — derived from AI-related work, the first time any Big Three strategy firm has made that figure visible. Bain’s “Sage” platform performs comparable functions. PwC, which became OpenAI’s first enterprise reseller, committed $1 billion to generative AI in 2023 and subsequently deployed ChatGPT Enterprise to 100,000 employees. KPMG followed with a $2 billion alliance with Microsoft.

Collectively, the Big Four and major strategy houses poured more than $10 billion into AI infrastructure between 2023 and 2025. The investments were real. The pricing implications they’re now confronting were perhaps underestimated.

What is outcome-based pricing in consulting — and why does AI accelerate it?

Outcome-based pricing ties a consulting firm’s compensation to measurable results — revenue growth, cost reduction, market-share gains — rather than to the hours or scope of work delivered. It existed before AI, but AI transformation projects suit it naturally: they are multi-year, multidisciplinary, and generate data that makes performance tracking tractable.

As Kate Smaje, McKinsey’s global leader of technology and AI, noted in November 2025, the shift “developed over the past several years as McKinsey started doing more multi-year, multidisciplinary, transformation-based work.” AI didn’t originate the model. It made it commercially necessary.

The structural problem no press release addresses

Here is where the analysis must get uncomfortable for the firms themselves.

The productivity gains AI is generating inside McKinsey, BCG, and Bain are not, in any consistent way, being passed on to clients. One detailed analysis of MBB pricing practices published in 2025 concluded bluntly: firms’ external pricing “hasn’t moved” even as internal AI tools have displaced significant analyst labour. Clients are still paying as if junior consultants spent 80-hour weeks building the models from scratch. In many cases, Lilli or Deckster did it in an afternoon.

This creates a credibility problem that compounds over time. Sophisticated procurement teams at large corporations are beginning to ask questions about methodology, tool usage, and the provenance of deliverables. Deloitte Australia’s AU$440,000 refund to a government client over unverified AI-generated outputs — reported in 2025 — turned what had been a theoretical concern into a profit-and-loss event. Ninety percent of enterprise buyers, according to subsequent surveys, now want explicit AI governance disclosures built into contracts.

The Financial Times has reported that McKinsey is already adjusting its internal partnership economics in response, planning to shift a greater share of partner remuneration into equity as AI-driven outcome-based pricing makes consulting revenues more volatile and harder to predict quarter-to-quarter. Partners, in other words, are being asked to absorb the risk that used to sit with clients. That is a profound structural change — and one the recruitment and retention of top talent will have to accommodate.

The Amazon McKinsey Group launched in January 2026 — a joint venture combining McKinsey’s strategy capability with AWS cloud infrastructure and AI tooling — represents the most explicit attempt yet to fuse the advisory and implementation roles into a single, outcome-accountable offer. Engagements are scoped for transformations expected to deliver at least $1 billion in measurable client impact. It is a bet that scale and technology integration can justify premium fees in ways that billable hours increasingly cannot.

The counterargument: not all hours are created equal

It would be wrong to read this as consulting’s obituary. The critics of outcome-based pricing are not wrong to worry.

The model introduces its own distortions. When fees depend on measured outcomes, consultants have an incentive to define those outcomes narrowly, to work on problems whose success is easily attributable, and to avoid the ambiguous, long-horizon strategic work that generates the least data but often the most genuine value. A firm paid to raise revenue by 8% in 18 months may not tell a CEO that the business model is structurally broken. A firm paid by the hour has no such structural inhibition.

There is also the question of risk allocation. Outcome-based contracts push downside exposure onto the consulting firm, which sounds appealing to clients until they realise that firms will price that risk into their upside. McKinsey isn’t offering to share downside and cap upside. The performance-based arrangements being described are, in practice, hybrid structures — some fixed base, performance kickers on top — not pure contingency. That’s a meaningful distinction.

Sceptics within the industry point to a second problem: attribution. Did McKinsey’s intervention raise the client’s revenue, or did a favourable macroeconomic tailwind? Determining causality in complex business environments is genuinely hard, and the history of performance-based arrangements in other professional services — notably investment banking and private equity advisory — suggests that disputes over attribution tend to be costly and corrosive.

“Outcomes-based pricing didn’t start because of AI,” Smaje acknowledged in November 2025. The honest implication of that statement is that it won’t be resolved by AI either.

What firms, clients, and the talent market face next

The second-order effects of this pricing shift will ripple well beyond contract structures.

The consulting pyramid — the hierarchy of analysts, associates, managers, partners, and senior partners whose labour cost structure has remained largely stable for three decades — is under genuine pressure. McKinsey’s own research has estimated that approximately 45% of activities traditionally performed by consultants could be automated with existing technology. If Lilli handles research, synthesis, and deck generation, the case for the analyst class — the bottom of the pyramid that cross-subsidises partner economics — becomes harder to sustain.

Hiring data from 2025 suggests firms are already adjusting. The UK Management Consultancies Association survey projected 5.7% consulting revenue growth in 2026 and 7.4% in 2027, with AI services driving the greatest expansion for 66% of firms. Yet headcount growth is not tracking revenue growth — a gap that implies productivity gains are being captured by existing staff rather than expanded teams.

For clients, the shift creates genuine leverage — but only for those sophisticated enough to use it. Enterprise buyers who understand what AI can and cannot do, who can write performance metrics that are both meaningful and attributable, and who are prepared to challenge deliverable provenance will extract real value from the new model. Those who outsource that judgment to the firms themselves will find that outcome-based pricing, in practice, looks a lot like billable hours with better marketing.

The talent market will bifurcate. Consultants who can manage AI-augmented workflows, design outcome metrics, and demonstrate delivery accountability will command premiums. Those whose competitive advantage was research bandwidth and slide-deck velocity — tasks now automated at scale — face a more difficult conversation. Research published in late 2025 found that consultants using AI tools completed tasks 25% faster at 40% higher quality, but the strategic thinking, relationship management, and client judgment that justify senior fees remain, for now, distinctly human.

The tension that will define the next decade

There is a phrase circulating in elite consulting circles that captures the bind precisely: firms are being asked to be accountable for outcomes they do not fully control, using tools whose productivity gains they have not fully disclosed, in a market where clients are only beginning to understand what to demand.

The billable hour was imperfect. But it had the great virtue of simplicity: time spent, time charged. What replaces it will be messier, more contested, and more lucrative for the firms that define the terms before their clients do.

McKinsey’s quiet overhaul of partner pay is the most honest signal of what the industry privately believes: that the revenue model is becoming structurally volatile, and that the people at the top of the pyramid need to share in the uncertainty their AI tools have created. That is not a reassuring message dressed up as progress. It is a reckoning.

The hour was always a fiction. The question now is what honest accounting looks like when a machine has done the work.