An internal data analysis at McKinsey revealed that resilience—specifically, having a setback and recovering—is a better predictor of making partner than perfect grades. The firm has changed its hiring process to actively screen for this trait.
McKinsey finds over half the challenge in leveraging AI is organizational, not technical. To see enterprise-level value, companies must flatten hierarchies, break down departmental silos, and redesign workflows, a process that is proving harder and longer than leaders expect.
The consulting giant is shifting its business model from pure advisory work (fee-for-service) to an outcomes-based approach. McKinsey co-creates a business case with the client and contractually underwrites the results, aligning its incentives directly with client success.
When facing controversy, McKinsey's leadership first asks where they should be humble and learn from mistakes (like their opioids work), and where they should be courageous and push back against criticism they disagree with (like their work in hard-to-abate climate sectors).
As AI handles linear problem-solving, McKinsey is increasingly seeking candidates with liberal arts backgrounds. The firm believes these majors foster creativity and "discontinuous leaps" in thinking that AI models cannot replicate, reversing a long-standing trend toward STEM and business degrees.
Citing extensive research, McKinsey's leader asserts that organizational speed is a critical performance driver. Faster companies consistently outperform more cautious, slower-moving competitors, suggesting that a bias for action is more valuable than avoiding all errors, despite corporate risk aversion.
McKinsey's global managing partner now considers AI agents part of the company's headcount. The firm rapidly scaled from 3,000 to 20,000 agents in just 18 months, viewing them as essential 'employees' that augment their human workforce, and expects to reach a 1:1 human-to-agent ratio by 2026.