𝐅𝐢𝐫𝐞𝐝 𝐟𝐨𝐫 𝐅𝐚𝐢𝐥𝐢𝐧𝐠 𝐚 𝐓𝐞𝐬𝐭 𝐍𝐨𝐛𝐨𝐝𝐲 𝐖𝐫𝐨𝐭𝐞

Three-quarters of executives say their company’s AI strategy is more performance than practical guidance.

Yet 60% say they plan to lay off employees who don’t follow it.

Both numbers come from the same survey of 2,400 executives and employees, published by WRITER in April.

Those two findings should never coexist.

The issue isn’t simply whether a new AI elite is emerging. It’s how companies are deciding who belongs in it.

Ninety-two percent of executives say they’re cultivating a new class of AI elite. Seventy-seven percent say employees who resist AI won’t be considered for promotion or leadership.

The stakes are enormous.

The standard often isn’t.

When proficiency is undefined but the consequences are severe, people don’t become proficient. They stop learning and start performing competence. They optimize for looking fluent instead of becoming fluent.

And the one behavior that actually builds expertise becomes dangerous.

Admitting, “I don’t know this yet.”

Asking a basic question.

Trying something imperfectly.

Those stop looking like learning behaviors and start feeling like career risks.

You cannot learn a technology in a room where curiosity is dangerous.

The objection to a two-tier workforce is usually about fairness, and that’s a valid concern. But something even more practical is happening.

It’s expensive.

When employees are rewarded for appearing capable instead of becoming capable, organizations create exactly the conditions that slow adoption. Fear produces posturing. Posturing produces busy work. Busy work produces no capability.

Which may explain why, in that same survey, nearly half of executives described their AI adoption efforts as a massive disappointment.

That disappointment wasn’t caused by the technology.

It was manufactured by the environment.

The encouraging news is that this isn’t a difficult problem to solve.

That 75% figure may actually be the most hopeful statistic in the entire report. It suggests most organizations aren’t missing better technology. They’re missing a clear standard.

Write one page.

Define what AI is actually for in your business.

Describe what good use looks like.

Explain where AI should not be used—and why.

Measure outcomes instead of counting prompts or policing tools.

Clarity spreads fluency far faster than fear ever will.

Once people understand what they’re trying to accomplish, they usually adopt the right tools remarkably quickly.

The AI elite many companies are trying to create is mostly waiting for leadership to define the job.

Before you measure AI proficiency, define it.

𝐇𝐚𝐬 𝐲𝐨𝐮𝐫 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐭𝐨𝐥𝐝 𝐩𝐞𝐨𝐩𝐥𝐞 𝐰𝐡𝐚𝐭 𝐠𝐨𝐨𝐝 𝐀𝐈 𝐮𝐬𝐞 𝐥𝐨𝐨𝐤𝐬 𝐥𝐢𝐤𝐞?

#AIAdoption #Leadership #FutureOfWork #AIStrategy #Management

Contact

bruno.gentil@sherpaconsultingasia.com

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