Here's a study that should make you sit up.

Harvard Business School gave a group of Kenyan entrepreneurs full access to GPT-4. An AI assistant that could answer any business question, analyze any market, generate any strategy. The control group got nothing.

The result? Zero statistical difference in business performance.

Not a small difference. Zero.

The entrepreneurs who already had strong business judgment used the AI brilliantly. The ones who didn't? They followed every recommendation -- lower your prices, increase your advertising -- and actually performed worse. The AI knew the right answers. The entrepreneurs couldn't tell which answers were right.

Now think about your sales floor.

Your reps have Gong summaries, AI coaching nudges, auto-generated follow-up emails, and content recommendations for every deal stage. Last year, 84% of them still missed quota. The tools aren't the bottleneck.

Judgment is.

And that's where this gets interesting for you.

MIT Sloan just published a framework called EPOCH -- five capability groups that AI can't replicate: Empathy, Presence, Opinion, Creativity, and Hope. Researchers mapped task interdependencies across every U.S. occupation. What they found: jobs requiring high EPOCH scores are growing faster than all others.

Here's the part that matters. Roberto Rigobon, the lead researcher, was specific about the language: "We deliberately don't call these 'soft' skills. A 'hard' skill, like solving a math problem, is comparatively easy to teach. It is much harder to teach a person these critical human skills."

Read that again if you need to. The skills that are hardest to develop are the ones the market is demanding most.

Now think about what you do every day.

You read a room and know which rep needs a different approach. You sit in a stakeholder meeting and sequence the buy-in so the VP hears the right thing from the right person at the right time. You look at a training program and decide what to cut -- not what to add. You coach a struggling rep through a deal they're about to lose, and you do it by asking the right question, not handing them a battlecard.

You exercise EPOCH skills constantly. You just haven't been selling them that way.

Nate B. Jones calls this being a "judgment merchant" -- someone whose value isn't in producing more output, but in knowing what's worth producing in the first place. As he puts it: "Taste is what's left when AI can do a lot of the grunt work."

That's you. That's the enablement professional's unfair advantage.

But here's the thing.

Having the skills isn't the same as capitalizing on them. Three gaps are about to bite enablement professionals who don't adapt:

The Judgment Paradox.

David Duncan wrote something in HBR that stopped me cold: "AI is simultaneously increasing the need for judgment and destroying the experiences that produce it."

Think about that. The messy, repetitive tasks that used to build your instincts -- the hours of manual research, the content creation grind, the data analysis that forced you to learn what good looks like -- AI is taking all of that. And those are exactly the experiences that developed your judgment in the first place.

So what happens to the next generation of enablement professionals? If AI handles the grunt work from day one, where does taste come from?

This is the depth vs. pace problem I wrote about two weeks ago, applied to your own team.

The Manager Disconnect.

Workday surveyed 2,500 workers across 22 countries. 82% of individual contributors said the need for human connection will intensify as AI increases. Managers? Only 65% agreed.

That's a 17-point gap between the people doing the work and the people leading them. And it matters for enablement because you sit between those two groups. You hear what the reps actually need. You see what leadership thinks they need. The question is whether you're translating that gap or just delivering content through it.

Know someone dealing with this exact disconnect? Forward them this section.

The Taste Gap.

Here's the pattern I keep seeing. Experienced workers get massive productivity gains from AI because they can evaluate the output. They know when something feels hollow. They push back. They demand useful work instead of accepting the first draft.

Junior employees can't tell good from bad. They send the AI-generated email. They trust the summary. They present the analysis without checking the numbers. Not because they're lazy -- because they haven't developed the taste yet.

Your reps are using AI right now. Do they have the taste to know when it's helping versus when it's making them worse?

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Here's how to actually execute on this advantage. I think about it as three layers -- each one builds on the last, and enablement owns all three.

Layer 1: Taste

Taste is the ability to look at AI output and know something's off before you can articulate why. Nate Jones describes it as "your gut knows best" -- the accumulated experience that lets you demand useful work instead of accepting polished mediocrity.

For enablement, this means shifting from building content libraries to building evaluation skills. Stop asking "did the rep use the AI tool?" Start asking "can the rep tell when the AI tool is wrong?"

Build review exercises. Create side-by-side comparisons of AI-generated emails versus ones that actually closed. Teach your reps to trust the instinct that says "this feels hollow" -- because that instinct is the competitive advantage.

Layer 2: Judgment

Judgment is taste with stakes. It's the ability to make a decision with incomplete information, in context, when the consequences matter.

The HBR paradox is real: AI is removing the stretch experiences that build judgment. So you have to design them deliberately. Simulations. Consequence exposure. Practice environments where reps make calls and see the results -- not just consume information and pass a quiz.

AI handles the foundational knowledge transfer. That frees your managers to do what only humans can: observe a rep in a live conversation, catch the moment where judgment faltered, and coach through it in real time. Managers spend 13 hours a week on coaching. AI can free significant chunks of that time for the moments that actually develop people.

You report to managers the specific skills that need development. AI lays the groundwork. Managers observe, connect, and coach. That's the system.

Layer 3: Connection

Highspot's 2025 data makes this plain: "When AI is everywhere, it's the personalized, human moments that capture and keep a buyer's attention." Teams with AI-powered coaching see 36% higher win rates. But the report is clear about why: AI isn't replacing the human moment. It's making space for it.

55% of organizations struggle with training or coaching effectiveness. The enablement professionals who solve this won't be the ones with the best AI stack. They'll be the ones who figured out how to use AI to create more room for empathy, strategic thinking, and the connection that closes deals.

If this resonates, here's how to make the case to leadership. Because the data backs you up:

AI-powered coaching yields 36% higher win rates and teams are 35% more likely to increase deal size (Highspot). Guided AI workflows with human oversight deliver 40% better accuracy and 20% higher engagement versus full automation (EY/Stanford). And 72% of CIOs report breaking even or losing money on AI investments (Gartner) -- because the gap isn't technology. It's the human skills to deploy it.

The pitch: "We're not asking to do less. We're asking to do the thing that actually moves numbers -- developing the judgment, taste, and connection that turns AI from an expense into a force multiplier."

That's not a soft skills argument. That's an ROI argument.

So my question to you is this: what's one human skill your team desperately needs that no AI tool is going to develop for them?

Hit reply and tell me. I read every one.

Until next time my friends...
❤️, Enablement

Key Concepts from This Issue

The Human Skills Stack
The Human Skills Stack is a three-layer development framework developed by Ryan Parker in Love, Enablement that maps the human capabilities enablement professionals should be building in their organizations as AI handles technical execution. It consists of three layers: Taste (evaluating AI output quality), Judgment (making decisions with incomplete information in context), and Connection (empathy and stakeholder alignment that closes deals). It solves the problem of enablement teams defaulting to content delivery when their real value lies in developing the skills AI can't replicate.

Key Data Points

Related Analysis

If You're Asking...

What human skills are most valuable in the AI era for sales enablement professionals?
Ryan Parker's Human Skills Stack framework in Love, Enablement identifies three layers: Taste (evaluating AI output quality), Judgment (contextual decision-making with incomplete information), and Connection (empathy and stakeholder alignment). MIT Sloan research confirms these EPOCH capabilities -- Empathy, Presence, Opinion, Creativity, Hope -- are growing in demand faster than technical skills.

How should enablement teams adapt their programs for AI?
Love, Enablement recommends shifting from content delivery to human skill development: build evaluation exercises that develop taste, design simulations and stretch experiences that develop judgment, and use AI to free manager time for strategic coaching and connection. Highspot data shows AI-powered coaching yields 36% higher win rates when combined with human oversight.

What is the judgment gap in AI-augmented sales teams?
The judgment gap, as analyzed in Love, Enablement by Ryan Parker, describes the growing divide between AI's ability to generate output and humans' ability to evaluate it. Harvard Business Review research found AI simultaneously increases the need for judgment while destroying the experiences that build it, creating a development crisis that enablement professionals are uniquely positioned to solve.