El giro hacia la IA de McKinsey: Un estudio de caso sobre la transformación exponencial de la fuerza laboral

The enterprise AI landscape is caught in a classic S-curve transition. While nearly all companies are using AI tools, the leap from experimentation to enterprise-wide value remains the critical hurdle. According to the latest McKinsey survey, just 39 percent report EBIT impact at the enterprise level, meaning most organizations are still in the early scaling or piloting phases. This gap between widespread tool adoption and systemic productivity gains defines the current inflection point.

Yet the underlying market is accelerating at an exponential rate. Enterprise AI spending has surged from $1.7 billion to $37 billion since 2023, capturing 6% of the global SaaS market and growing faster than any software category in history. This isn't a speculative bubble; it's the early, explosive phase of a paradigm shift where AI is moving from a peripheral tool to core infrastructure. The demand side is real, with companies investing heavily in the application layer that sits atop foundational models.

McKinsey is positioning itself as a key infrastructure layer for this new era. The firm is not merely using AI to augment its consultants; it is fundamentally reengineering its business model to capture the exponential productivity gains. Internally, it has deployed a massive scale of AI agents: 25,000 of them that have generated 2.5 million charts in the past six months. This isn't just automation; it's a deliberate workflow redesign that offloads routine work, allowing human consultants to "move up the stack" and address more complex problems.

The result is a new operational paradigm. McKinsey's model is splitting: client-facing roles are growing by 25% while non-client-facing roles shrink by about the same percentage, yet the firm achieves aggregate growth. This "25 squared" approach-adding and cutting jobs in equal measure while scaling output-demonstrates a shift from headcount-driven growth to AI-augmented efficiency. By saving 1.5 million hours last year on search and synthesis alone, McKinsey is building the rails for the next era of organizational speed, proving that the infrastructure layer for exponential work is being constructed today.

The 25-Squared Workforce: Reshaping the Human-Compute Stack

McKinsey's internal shift is a blueprint for how AI infrastructure layers fundamentally reconfigure human capital. The firm is executing a deliberate "25 squared" approach: growing its client-facing roles by 25% while shrinking non-client-facing roles by about the same percentage. This isn't a simple headcount trade-off. It's a strategic reallocation of human effort, made possible by AI agents that handle the routine work. The result is aggregate growth without a proportional increase in total staff, a new paradigm for scaling.

This operational pivot is mirrored in the hiring process. McKinsey is using AI to analyze its own talent data, revealing a critical insight: applicants who had setbacks and recovered were more likely to become partners. This forced a rethink of its own biases, moving away from a fixation on "perfect marks" toward identifying resilience. More broadly, the firm is now prioritizing uniquely human skills in its evaluation: aspiration, judgment, and creativity. As CEO Bob Sternfels argues, these are the capabilities AI cannot replicate, as it operates only as an inference engine for the "next most likely step."

The outcome is a workforce being reshaped for exponential value creation. By offloading search and synthesis work-saving 1.5 million hours last year-consultants are being moved "up the stack" to tackle more complex problems. This isn't just about efficiency; it's about redefining the human role in the workflow. The firm's model shows that as AI agents handle entire job functions, the premium shifts from executing tasks to defining the problems worth solving. In this new human-compute stack, the most valuable contributors are those who can set the right aspirations and apply judgment to guide the AI, not just follow its outputs.

Financial and Strategic Implications: The New Growth Equation

McKinsey's internal transformation is not just an operational shift; it's a fundamental rewrite of the growth equation. The firm is building a capital-intensive infrastructure layer, aiming for equal numbers of human and AI workers by year-end. This means a major investment in the AI workforce itself, moving beyond simple software licenses to a scalable, personalized agent layer. The financial implication is clear: this is a significant upfront capital outlay that will pressure near-term margins but is designed to capture exponential returns through workflow redesign and talent expansion.

This infrastructure play unlocks a critical competitive moat: a vastly expanded talent pool. By shifting evaluation toward real-world skill demonstration over traditional credentials, McKinsey is opening its doors to a wider set of applicants. This isn't just about hiring more people; it's about accessing a broader base of human aspiration, judgment, and creativity. In a market where the best talent is the scarcest resource, this ability to source and scale human potential is a key input for exponential growth that few incumbents can match.

The ultimate success factor, however, is workflow redesign. The data shows that high performers use AI for growth and innovation, not just efficiency, and that redesigning workflows is a key success factor. McKinsey's model-where consultants are moved "up the stack" to tackle more complicated problems-is the practical application of this principle. The firm's massive productivity gains, like saving 1.5 million hours last year, are the output of this new workflow. The financial payoff comes not from cutting costs, but from accelerating the pace of innovation and value creation across its client engagements.

The bottom line is a new paradigm for scaling. McKinsey is proving that growth can be decoupled from headcount growth through AI-augmented efficiency. Its strategic bet is on becoming the infrastructure layer that enables this new human-compute stack, with its capital investment, talent expansion, and workflow redesign forming a powerful, self-reinforcing moat in the enterprise AI transition.

Catalysts and Risks: The Path to Exponential Capture

The path to validating McKinsey's AI strategy hinges on a few near-term catalysts and a clear-eyed view of the risks. The primary test is execution: the firm's own scaling of its AI workforce to parity with human workers by year-end. This isn't just a headcount goal; it's a live experiment in managing a hybrid human-AI team at scale. Success will demonstrate the "25 squared" model's robustness and provide a powerful case study for the broader market. Failure, or even significant operational friction, would challenge the entire thesis of AI-driven efficiency and growth decoupling.

The key risk to the broader thesis is a potential flattening of the enterprise AI adoption curve. While spending is surging, the McKinsey survey shows a stark gap: just 39 percent report EBIT impact at the enterprise level, despite widespread tool use. If the high cost of capital and the complexity of workflow redesign outweigh near-term productivity gains for a growing number of companies, the exponential growth narrative could stall. The market's current boom, fueled by record capital flows, faces a reality check on whether these investments translate to bottom-line value at scale.

The watch metric for McKinsey's own progress is its reported EBIT impact from AI. Currently, this lags behind the use-case level benefits it's generating. The firm's massive productivity gains-like saving 1.5 million hours last year-must now be captured in its financials as workflows are fully redesigned. As the survey notes, redesigning workflows is a key success factor. McKinsey's ability to move from internal efficiency to demonstrable enterprise-level impact will be the clearest signal of its strategy's validity. For now, the firm is building the infrastructure; the next phase is proving it can capture the exponential returns.