McKinsey & Company is experiencing a dramatic AI-driven transformation that has fundamentally reshaped how the consulting giant operates, according to global managing partner Bob Sternfels. Speaking at the Consumer Electronics Show (CES) in Las Vegas, Sternfels revealed that AI adoption saved McKinsey an impressive 1.5 million hours in search and synthesis work in just the past year alone.
The firm has deployed 25,000 AI agents that have generated 2.5 million charts in the past six months, demonstrating the scale of AI integration across McKinsey’s operations. These AI systems excel at tasks like data synthesis, research, and visualization—work that previously consumed significant consultant time. As AI agents handle these routine tasks, Sternfels explained that McKinsey consultants are now “moving up the stack” to focus on more complex, strategic problems that require uniquely human capabilities.
Despite AI’s transformative impact, Sternfels identified three critical skills that AI models fundamentally cannot replicate: the ability to aspire, judgment, and true creativity. These skills represent the future of human work in an AI-augmented workplace.
The ability to aspire involves setting ambitious visions and goals that inspire others. As Sternfels put it, AI cannot decide whether to “go to low Earth orbit, the moon, or Mars”—that strategic vision-setting remains uniquely human. Judgment encompasses the ability to set appropriate parameters and make ethical decisions based on company values and societal norms, something AI models lack since they have “no right and wrong.” True creativity means thinking orthogonally—outside existing patterns—rather than simply predicting the “next most likely step” as inference models do.
Sternfels also discussed how AI is transforming talent acquisition strategies. He argued that traditional credentials like university prestige should matter less, advocating instead for skills-based evaluation. For technical roles, he suggested employers should examine candidates’ GitHub portfolios rather than their diplomas, focusing on actual demonstrated capabilities. This shift could democratize access to employment, creating “different pathways” for a wider range of people to enter the workforce based on merit rather than educational pedigree.
Key Quotes
What can the models not do? Aspire. Set the right aspiration. Do you go to low Earth orbit? Do you go to the moon? Do you go to Mars? That’s a uniquely human capability.
Bob Sternfels, McKinsey’s global managing partner, explained why strategic vision-setting remains beyond AI’s capabilities, emphasizing that humans must define ambitious goals and inspire others to pursue them.
There’s no right and wrong in these models, and so how do you set the right parameters?
Sternfels highlighted the critical role of human judgment in AI deployment, noting that humans must establish ethical boundaries and operational parameters based on values and societal norms that AI cannot inherently understand.
The models are inference models — the next most likely step.
When discussing creativity, Sternfels explained AI’s fundamental limitation: it can only predict patterns from existing data, while humans can think orthogonally and develop entirely novel approaches outside established frameworks.
Let’s actually get to the content, and could that actually start meaning that a wider set of people can enter the workforce with different pathways?
Sternfels advocated for skills-based hiring over credential-based evaluation, suggesting this shift could democratize access to professional opportunities and create more equitable employment pathways in an AI-driven economy.
Our Take
McKinsey’s transformation offers a compelling case study in AI’s dual impact: massive efficiency gains coupled with workforce evolution rather than replacement. The 1.5 million hours saved represents approximately 750 full-time employees’ annual work, yet McKinsey isn’t shrinking—it’s redirecting human talent toward higher-value activities. This “moving up the stack” phenomenon may become the dominant pattern across knowledge work.
Sternfels’ framework of aspiration, judgment, and creativity provides a practical lens for understanding AI’s limitations. These aren’t abstract concepts but concrete capabilities that organizations can cultivate through training and culture. The shift toward skills-based hiring is particularly significant, potentially disrupting the credentialism that has dominated professional services. However, the challenge will be developing reliable assessment methods that truly measure these capabilities at scale without introducing new biases.
Why This Matters
This story provides crucial insights into how one of the world’s most prestigious consulting firms is navigating the AI revolution, offering a blueprint for other organizations. McKinsey’s experience—saving 1.5 million hours annually—demonstrates AI’s tangible productivity gains at enterprise scale. More importantly, Sternfels’ identification of irreplaceable human skills provides a roadmap for workers and educators in an AI-dominated future.
The emphasis on aspiration, judgment, and creativity signals a fundamental shift in what makes workers valuable. Rather than technical execution or information synthesis—tasks AI increasingly handles—the premium will be on strategic thinking, ethical decision-making, and innovative problem-solving. This has profound implications for education systems, corporate training programs, and career development strategies.
The call to move beyond credential-based hiring toward skills-based evaluation could democratize opportunity while also addressing talent shortages. If implemented broadly, this approach could disrupt traditional higher education models and create more equitable pathways to professional careers, particularly in technology fields where demonstrated ability matters more than pedigree.
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Source: https://www.businessinsider.com/mckinsey-boss-shares-human-skills-ai-models-cant-do-2026-1