Company-wide AI Implementation in Five Steps

Join me and Dan Shipper for a live session on what AI fluency looks like at the executive level tomorrow, Tuesday, June 2. We’ll walk through how the leaders we work with—at hedge funds, private equity firms, and Fortune 500 companies—are using AI in their day-to-day, and what they wish they’d done differently six months in. RSVP.

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Sitting across from the chief operating officer of a health tech company earlier this year, I watched her name a problem many executives are feeling but few say out loud.

“Our junior employees are probably much more native with this technology,” she said. “And we need to make sure we’re sticking with it. Makes me feel like a dinosaur to say that, but it’s true.”

Confessions like this come up regularly during our executive training sessions: Leaders aren’t working directly with AI on sophisticated tasks, even as they’re guiding planning decisions about the technology. They know they should spend more time learning the tools, but they haven’t committed to it yet. That’s understandable; executives are incredibly busy. But what we see in our sessions is that leaders who haven’t gotten their hands dirty don’t clearly understand the practical opportunities and challenges of AI. That health tech executive’s admission sparked an important conversation about how a coordinated company-wide approach to AI implementation starts with executive AI fluency—but doesn’t stop there.

We see this pattern in every engagement we run in our consulting work. Over the past two years, we’ve trained thousands of people at companies including the New York Times, Ripple, Headway, and Thumbtack, and at investment firms managing over $100 billion in assets. We’ve done the workshops and watched what changed six months later.

AI usage in the workplace is now widespread, but it’s an altogether different ballgame to build organizational capability that truly realizes financial gains.

McKinsey defines AI high performers as organizations that report both significant value from AI and more than a 5 percent impact on earnings before interest and taxes (EBIT). These companies are nearly three times as likely as others to have fundamentally redesigned their workflows, but they remain a minority: Only 6 percent of the nearly 2,000 organizations surveyed met the criteria for success.

As AI has gone from performing party tricks to completing an entire day’s worth of human work in three short years, enterprise AI adoption has moved through three distinct waves. First came the license wave: companies bought access to tools like ChatGPT, Claude, and Microsoft Copilot and waited for productivity gains to appear. Then came the prompt wave: companies ran training sessions, built prompt libraries, and encouraged teams to experiment with custom GPTs. Now we are entering the implementation wave: prompt libraries are giving way to skills libraries, agents, evals, and workflows with named owners.

The METR chart in our full guide shows how far the technology has progressed, but we’ve seen that many organizations implementing AI haven’t kept up with the sea change. The bottleneck for AI adoption has moved from model capability to organizational capability.

That’s why we built a practical guide for executives who have bought AI tools but are not yet seeing real value from them. The loop is simple:

Get fluent. Use the tools yourself before directing anyone else to use them. Know what your company has access to, what the policies allow, and what the friction feels like. If you haven’t built something with AI in the last 30 days, start there.

Assign AI champions. Pick operators with bandwidth. Give them protected time (at least two days per month), a clear mandate, and enablement. They are responsible for taking workflows from “works in a demo” to “works in production.”

Pick one painful workflow. Let your champions choose. They know what work is most tedious and worth automating. Start with something frequent, data-rich, and narrow enough to test in a week. You don’t need a full workflow mapping exercise.

Build to 95 percent. An automation that works 80 percent of the time is a demo. Real automation requires gold-standard examples, structured evals, human review gates, and a named owner who maintains it when the model updates. Once you have a skill that works reliably 90-95 percent of the time, you’ve gotten value from AI.

Scale what works. This is where the champion role is key. Run show-and-tells. Train adjacent teams on proven workflows. Kill what doesn’t work and expand what does. One visible win creates pull across the organization.

This guide turns that loop into a 60-day plan for executives, with checklists and rubrics drawn from Every’s consulting work with dozens of top companies. You can read it in full here.

Natalia Quintero is the head of Every Consulting.

Thanks to Tom Matsuda for editorial support.

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