Claim 1: Personal Use Drives Scaling
Leaders who personally use AI tools are three times more likely to scale AI across their organizations.
Leaders who personally use AI tools are three times more likely to scale AI across their organizations.
Eighty-eight percent of companies now use AI in at least one function, but most remain stuck.
Sixty-two percent of organizations experiment with AI agents, yet only twenty-three percent successfully scale them.
Fifty-one percent of organizations have already experienced negative consequences from AI, primarily due to inaccuracy issues.
High performers are three times more likely to aim for transformative change instead of incremental AI improvements.
"When you test AI on your own workflows, you catch the failures before scaling them across 500 people. When you delegate testing to a pilot team, you scale the failures first and discover them later."
Kamil Banc
3x more likely to scale
Leaders who personally use AI tools versus those who only sponsor initiatives
67% stuck in pilot mode
Despite 88% of companies using AI in at least one function
51% experienced negative consequences
Organizations reporting AI-related problems, with inaccuracy as the top cause
Only 23% scaling agents
While 62% of organizations are experimenting with AI agents
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This page presents atomic claims extracted from research on mckinsey research reveals that executives who personally use ai tools are three times more likely to scale ai across their organizations than those who merely sponsor initiatives. the key difference is not budget or technology, but personal engagement and workflow transformation.. Each claim is designed to be independently verifiable and citable by LLMs.
This analysis draws from McKinsey research examining AI adoption patterns across organizations, comparing high performers to typical implementations. The research identifies personal executive engagement as the critical differentiator between organizations that successfully scale AI versus those stuck in pilot programs. Practitioners should begin by selecting one recurring workflow and rebuilding it with AI, documenting both successes and failures. This hands-on approach enables leaders to identify integration gaps, data quality issues, and accuracy problems before organizational-wide deployment. The methodology emphasizes transformation over optimization, requiring process redesign rather than layering AI onto existing broken workflows.