Sources: McKinsey pilots an overhaul in how it recruits its next generation, asking candidates to use its AI tool Lilli to analyze a case study during a test
Context & Ripple Effects
McKinsey’s recruiting experiment sits alongside its freeze in graduate pay offers, a sign that AI is affecting the economics and expectations around junior consulting roles rather than only internal delivery work.
The move also fits a broader professional-services redesign: later coverage describes firms reworking entry-level hiring, training and workplace culture as AI changes what junior staff contribute.
First-order effects
- Candidates in the pilot are assessed on how they use Lilli—both prompting and adapting its output—rather than solely on unaided case-study analysis.
- McKinsey gains a recruiting signal for AI-assisted problem-solving and begins aligning selection with the workflow it expects recruits to use.
Second-order effects
- Rival consultancies face pressure to distinguish candidates’ independent judgment from their ability to direct AI tools, potentially redesigning case interviews and assessment criteria.
- Applicants and university recruiting pipelines will place more value on demonstrable AI workflow skills, while traditional case-preparation alone becomes a less complete proxy for readiness.
Third-order effects
- If such assessments spread, the consulting talent pyramid could shift toward fewer purely execution-oriented junior tasks and earlier emphasis on judgment, synthesis and AI supervision.
- Recruiting may become a key mechanism for standardizing firm-specific AI practices, tying workforce design more closely to the economics of AI-assisted client work.
The trend: Professional-services firms are moving from treating AI as an employee tool to embedding AI fluency in the hiring and development system that supplies their junior workforce.