"If Nike hired Michael Jordan to work at headquarters, would you expect the marketing team to start sinking three-pointers? Of course not. He's extraordinary, but skill doesn't spread by proximity. Here's a better question: What do Nike employees need to know about basketball? The rules. Game duration. Equipment specs. Enough to design better shoes, write sharper campaigns, and forecast demand accurately. They don't need to play in the NBA. And Nike doesn't need to hire NBA players to improve its business."
"The same is true for AI. Most companies don't need extreme AI talent to unlock real efficiency gains. They need people across the organization to understand how AI applies to their work. Until leaders get specific about which AI skills matter, where they live, and how they show up in day-to-day work, no amount of hiring AI experts will make an organization truly AI-enabled."
"I like to think of this as teaching the entire company how to drive with GPS. Not everyone needs to build the map. But everyone should know when the directions are reliable, when the route is risky, and when the system is confidently wrong. First is AI literacy. Employees need to understand what AI can do, what it can't, and what it will do when it doesn't know the answer. Literacy prevents common failures: over-trusting outputs, under-using tools, and feeding poor context."
Most companies do not need extreme AI talent to achieve meaningful efficiency gains. Organizations need employees across functions to understand how AI applies to their daily work. AI skills break into three categories: AI literacy for everyone, AI integration for technical professionals, and AI creation for specialists. AI literacy includes recognizing capabilities, limitations, reliability, and typical failure modes, plus role-specific tool fluency for tasks like marketing, recruiting, and support. Leaders must specify which AI skills matter, where those skills reside, and how they appear in routine work to enable organization-wide AI adoption.
Read at Fast Company
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