McKinsey’s 25,000 AI agents grabbed the headlines, but the more consequential number is the roughly one-third of the firm’s revenue now tied to outcome-based engagements. Across the industry, AI is not just changing how consultants work – it is rewriting how they get paid.
When maddaisy.com examined McKinsey’s 25,000 AI agent deployment last week, the focus was on scale: was deploying one digital agent for every 1.6 human employees a first-mover advantage or an expensive experiment? The answer may depend less on the agent count and more on a quieter transformation happening in parallel – the shift from selling time to selling outcomes.
The Model That Built an Industry Is Under Pressure
For decades, consulting has run on a straightforward exchange: expertise for time, billed by the hour or the project. It is a model that has produced extraordinary margins, but it carries an inherent misalignment. Consultants profit from the complexity of a problem, not necessarily from solving it quickly.
AI agents threaten that dynamic directly. When an algorithm can synthesise research in minutes that previously took analysts weeks, the hours-based model starts to look exposed. McKinsey’s own data – 1.5 million hours saved on search and synthesis work – quantifies exactly how much billable time AI has already removed from the equation.
Rather than watching margins erode, McKinsey is pivoting. Speaking on the HBR IdeaCast in February, CEO Bob Sternfels confirmed that outcome-based engagements – where McKinsey co-invests alongside clients and ties fees to measurable business results – now account for roughly a third of the firm’s revenue. Two years ago, that figure was negligible.
The Economics Only Work with AI
This is where the 25,000 agents become strategically coherent. Outcome-based consulting is inherently riskier than fee-for-service; the firm only earns if the client succeeds. To make the economics work, you need two things: lower delivery costs and higher confidence in results.
AI agents address both. QuantumBlack, McKinsey’s 1,700-person AI division, now drives 40% of the firm’s total work. Non-client-facing headcount has fallen 25%, while output from those teams has risen 10% – the “25 squared” model. The savings create the margin headroom needed to absorb the risk of outcome-based pricing.
It is not just McKinsey making this calculation. BCG has deployed “forward-deployed consultants” who build AI tools directly within client organisations, effectively embedding methodology as software rather than slides. Capgemini has trained 310,000 employees on generative AI, though its agentic AI bookings only reached 10% of quarterly pipeline by Q4 2025. Accenture has stopped reporting AI bookings separately because, as the firm noted in its Q1 fiscal 2026 results, AI is now embedded in virtually every engagement.
The Client Side of the Equation
The timing is not coincidental. As maddaisy.com reported last week, PwC’s 2026 CEO Survey found that 56% of chief executives still cannot demonstrate revenue gains from AI. When the client cannot prove value, the consultancy offering to underwrite outcomes holds a powerful negotiating position – essentially saying, “we believe in this enough to stake our fees on it.”
The irony is that consultancies are asking clients to trust their AI capabilities while the industry’s own track record on AI delivery remains uneven. Deloitte’s own 2026 State of AI report found that 42% of organisations consider their AI strategy “highly prepared” but feel markedly less ready on infrastructure, data governance, and talent.
McKinsey faces this credibility gap from a different direction. The firm’s State of AI research found that only 5% of companies globally see AI hitting their bottom line. Positioning itself as the firm that can deliver measurable outcomes means McKinsey is, in effect, claiming to solve a problem that its own research says almost no one has solved.
What Changes for Practitioners
For consultants and the organisations that hire them, three practical implications stand out.
Procurement shifts. If outcome-based pricing becomes the norm, procurement teams will need to evaluate consulting engagements more like joint ventures than service contracts. That means assessing the firm’s AI capabilities, data infrastructure, and delivery methodology – not just the partner’s credentials and the day rate.
The talent model is splitting. Sternfels has been explicit that McKinsey wants “great consultants and/or great technologists, groomed to be both.” The traditional path – from analyst to associate to engagement manager – now runs alongside a technical track where consultants build and deploy AI systems. BCG’s vibe-coding consultants are an early version of this hybrid role.
Governance becomes shared. When a consultancy co-invests in outcomes and deploys AI agents within a client’s operations, the governance question becomes bilateral. As maddaisy.com has covered extensively, the gap between AI deployment speed and governance maturity is already the defining risk of 2026. Outcome-based models widen this gap further, because neither party has clear precedent for who owns the risk when an AI agent produces flawed analysis that drives a business decision.
The Bigger Picture
The consulting industry has weathered previous disruptions – offshoring, automation, the rise of in-house strategy teams – by evolving its value proposition. This time, the change is structural. AI agents do not just reduce the cost of delivering advice; they make it possible to charge for results instead.
McKinsey’s 25,000 agents are best understood not as a technology deployment but as a financial instrument – the infrastructure that underwrites a new revenue model. Whether the model works depends on something no agent can automate: whether clients actually achieve the outcomes both parties are betting on.