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The “A-Player” Bet Is a Judgment Bet

Verne Harnish has spent four decades telling founders to hire fewer people and pay them more. That advice just got sharper teeth.

In a recent piece for Chief Executive, Harnish laid out what AI does to a workforce sorted into A, B, and C players. A-players use AI to extend an already-strong instinct for the business. B-players use it to close the gap between where they are and where they need to be. C-players use it to generate their usual mistakes at a faster clip—same tool, three different outcomes, because the tool was never the variable that mattered.

I’d put it more bluntly: AI doesn’t fix judgment. It scales it.

That should stop you mid-scroll, because many hiring processes were never built to screen for judgment in the first place. They were built to screen for skills — can this person run the platform, close the deal, manage the P&L? Skills are exactly what a subscription can now supply on demand. The screen that used to work is now testing for the one thing AI has made cheapest to acquire.

The Framework Underneath the Framework

Harnish’s own model for employee value — Will, Values, Results, Skills, from his book 12 Habits of Valuable Employees — is worth reading, because three of those four categories aren’t skills at all. They’re judgment wearing other names.

“Will” is the capacity to keep making good calls when the plan stops working, and nobody’s told you what to do next. “Values” is the discipline of holding a line when the easy read of the room says bend it. “Results”, in Harnish’s model, are what show up when someone repeatedly reads a situation correctly and acts on it — not when someone follows a checklist well.

“Skills” is the outlier in the group. It’s the one category that’s genuinely commoditizing, the one where the gap between your best and your average performer is shrinking fast. Will, Values, and Results don’t shrink. If anything, AI is what’s exposing how wide those gaps already were — they were just easier to paper over when execution speed hid the difference.

That’s the quiet argument buried inside Harnish’s talent advice. Hire for A-players, not because they’ll prompt better, but because prompting was never the scarce skill to begin with.

Why Your Hiring Process Won’t Catch This

Here’s the uncomfortable part for a fast-growth company. Most founders built their hiring process during the years when execution speed was the differentiator — when the person who could do the work fastest and cleanest was the hire worth fighting for. That process is calibrated to detect exactly the trait AI now supplies for free, and poorly calibrated to detect the trait that’s left standing once it does.

You can watch this play out in a single interview loop. The candidate who impresses the room is usually the one with the smoothest command of the tools and the language of the role. The candidate whose judgment would actually hold up under a bad quarter rarely announces itself in 45 minutes of rehearsed answers. Most interview processes reward the former and have no mechanism to detect the latter, which is exactly the opposite of what a lean, AI-leveraged team now needs.

This is the same blind spot I built the Invisible Break Diagnostic to expose in strategy — the gap between what a leadership team believes is aligned and what’s actually operating, invisible until it costs you a quarter. The hiring version of that break is just as real and just as invisible until the wrong hire is already three months in and the team has quietly reorganized itself to cover for him.

The Execution Canvas works from the same premise: a strategic plan is only as good as the judgment of the people executing it, so the plan itself has to force decisions to the surface rather than hide them in a task list. The same discipline applies one level down, at the hire. If your process can’t surface how a candidate reasons under uncertainty, it isn’t testing for what your company will actually need from them in year two.

Here’s My Take

There’s a structural reason this matters more now than it did five years ago. A ten-person team built the old way had redundancy. A weak call by one person got caught, softened, or quietly corrected by four others doing adjacent work. Slower, but safer.

A lean team leveraged by AI doesn’t have that redundancy anymore. Fewer people are making more consequential calls, faster, with a tool that will confidently execute whatever direction it’s pointed in — good or bad. One bad judgment call doesn’t get absorbed by the system anymore. It gets amplified by it.

Harnish is right that the future belongs to smaller teams of A-players working with AI instead of around it. What he’s really describing, whether he’d frame it this way or not, is a workforce increasingly sorted by judgment, not skill. The founders who see that clearly — and who rebuild their hiring, their on-boarding, and their strategic planning around it — are the ones who’ll be running lean and dangerous. The ones who don’t will be running lean and exposed, and they won’t know it until the quarter that proves it.

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