The Human Judgment Gap

Why smart companies are eliminating their most critical AI asset

Hey there,

Last week I had a conversation with a former student—a program manager at a Fortune 500 company—that crystallized something I've been observing across dozens of organizations: Companies racing to implement AI are systematically eliminating the very roles that determine whether AI initiatives succeed or fail spectacularly.

Her insight: "[Business leaders believe] teams might not need a product manager because it's AI doing that work of giving you so many product suggestions. However, who's going to take up that role of validating if this works in my market? That's a product manager's role. Companies are forgetting that and cutting that off."

She's witnessing the Human Judgment Gap in real time—and most leadership teams don't even realize they're creating it.

When Logic Breaks Down

Executives see AI generating product requirements, competitive analyses, and strategic recommendations so they conclude that roles focused on these activities are becoming redundant. The logic seems airtight until you ask a few crucial questions: Who determines whether AI suggestions actually work in your specific market, with your specific customers, under your specific constraints? Who distinguishes between AI recommendations that sound sophisticated and those that actually drive results? Who decides when AI's confident-sounding analysis is missing critical context that only comes from market experience?

AI doesn't eliminate the need for human judgment—it amplifies it exponentially. Every AI-generated suggestion requires human discernment about applicability, feasibility, and strategic fit. But companies are systematically eliminating the people who provide that judgment precisely when they need it most.

AI doesn't eliminate the need for human judgment—it amplifies it exponentially.

The gap between "AI can generate suggestions" and "these suggestions work in our context" is widening, not closing. And most organizations are cutting the bridge instead of building it. Layoffs accelerate this gap, removing experienced judgment from exactly the moment when AI amplification makes that judgment most valuable.

How Organizations Walk Into the Trap

My student described the pattern: "All companies are now in a race...but none of them had clarity on how you could do it. People are jumping into concepts very quickly without understanding the basics."

Companies are treating AI transformation like cost reduction instead of capability development. They're asking "What roles can we eliminate?" instead of "What judgment capabilities do we need to build?"

The trap works like this:

1. AI tools demonstrate impressive individual capabilities

2. Leadership identifies roles that seem to overlap with AI functions

3. Budget pressure accelerates elimination decisions

4. Teams lose the human judgment required to distinguish between AI suggestions that sound good and AI suggestions that actually work

5. AI initiatives fail because there's no systematic validation process

6. Companies blame AI limitations instead of recognizing their judgment gap

Most organizations won't realize they've fallen into this trap until after expensive failures teach them what systematic human judgment was actually worth.

Why Curation Matters More Than Ever

Successful AI adoption demands what my student called "personalized curated programs" that help teams understand when to trust AI versus when to override it. You can't just distribute AI tools and hope for transformation—you need systematic approaches for evaluating AI output against real-world constraints.

You can't just distribute AI tools and hope for transformation—you need systematic approaches for evaluating AI output against real-world constraints.

This is fundamentally different from technology adoption. Previous transformations—desktop computing, internet, mobile—enhanced human capabilities but didn't require constant judgment about when the technology was right versus wrong. AI suggestions can be brilliantly wrong in ways that seem perfectly logical. That makes human judgment not just important but existentially critical.

Consider the difference between AI automation that drives efficiency versus AI automation that drives revenue. Most teams can identify opportunities to automate routine tasks—that's table stakes. But distinguishing between AI applications that create genuine competitive advantage and those that simply make you efficient at the wrong things? That requires the kind of strategic judgment companies are systematically eliminating.

The companies building competitive advantage aren't asking "How can AI replace this role?" They're asking "How can we build systematic capabilities for determining when AI enhances versus when it undermines our strategic objectives?"

AI Amplifies Everything—Including Bad Decisions

I've written before that AI is a force multiplier. But whether that's a good force or a bad force, AI doesn't care. AI amplifies whatever human judgment guides it—brilliant strategy becomes more brilliant, but flawed assumptions become more catastrophically flawed. The quality of human judgment determines whether AI multiplication creates competitive advantage or competitive disaster.

Organizations eliminating judgment capabilities aren't just reducing costs—they're eliminating their ability to ensure AI multiplies the right forces instead of the wrong ones. Every layoff that removes experienced judgment widens this gap further.

Two Paths Forward

We're at a critical inflection point where the Human Judgment Gap will resolve in one of two ways:

Scenario 1: Companies continue eliminating judgment-heavy roles until AI failures become so expensive that they're forced to rebuild the human discernment capabilities they eliminated. This is the "expensive education" path—learning through catastrophic failures that systematic human judgment was actually their most valuable AI asset.

Scenario 2: Smart leaders recognize this pattern now and reverse course before the expensive failures. They invest in building systematic AI judgment capabilities instead of eliminating them. They understand that the future belongs to organizations that can rapidly distinguish between AI suggestions that create competitive advantage and those that create expensive mistakes.

The choice isn't whether this reckoning will happen—it's whether your organization learns this lesson before or after your competitors do.

Your Strategic Response

If you're witnessing elimination decisions that feel premature, if you're seeing AI initiatives launched without systematic judgment processes, if you're watching organizations treat discernment capabilities as cost centers instead of competitive advantages—you have two critical opportunities to take action:

First, for leaders ready to build systematic AI capabilities instead of hoping tools create transformation: I work with organizations to develop comprehensive AI transformation approaches that build judgment capabilities rather than eliminate them. My private cohort programs create systematic frameworks for distinguishing between AI suggestions that create competitive advantage and those that create expensive mistakes. [Click here to learn more.]

Second, and perhaps more importantly: Share this perspective with the decision-makers in your organization or network who need to understand why systematic judgment capabilities are becoming more valuable, not less valuable, in the AI era.

The Human Judgment Gap will only close through one of two mechanisms: expensive failures that force recognition, or strategic insight that prevents them. The leaders making elimination decisions today need to understand what they're actually eliminating—and what that will cost them when their AI initiatives start failing because they eliminated the capability to diagnose.

Most companies are about to learn that AI is a force multiplier that amplifies human judgment rather than replacing it. Will they learn this through strategic insight or expensive education? Only time will tell. But the window for learning this lesson cheaply is closing faster than most leaders realize.

If you want to see this judgment distinction in action—between AI that drives efficiency versus AI that drives revenue—join me and Jacob Bank (Founder/CEO of Relay.app, ex-Google Product Director) for our Lightning Lesson on September 30th: "Build an AI Agent to Turn Feature Release to Instant Revenue." We'll demonstrate exactly the kind of strategic AI thinking that requires human judgment to identify and implement. [Free registration here.]

Break a Pencil,

Michael

www.breakapencil.com

P.S. If you recognize this pattern in your organization but aren't sure how to build systematic judgment capabilities, my private cohort approach addresses exactly this challenge. We focus on building frameworks that help teams distinguish between AI suggestions that drive competitive advantage and those that create expensive mistakes—before the failures teach those lessons expensively. [Click here to learn more.]

P.P.S. The most important action you can take this week: Forward this to the leader in your organization who's making resource allocation decisions around AI transformation. They need to understand that judgment capabilities are appreciating assets, not depreciating costs. The future belongs to organizations that recognize this before their competitors do.