I’ve been looking at my opening slide for three days.
Not because I’m stuck, but because I’m obsessed with getting the story right for the people in the room. Next week, I’m presenting to a federal team of instructional designers and strategists—people who have spent their entire careers helping others master the “hard things.”
I’m about to walk in and tell them that almost everything we’ve been measuring about success is shifting under our feet. That’s a heavy opening. And to do it right, I have to be intentional about who I’m actually talking to.
Every Room Has the Same Five People
Research from Boston Consulting Group consistently finds the same five types of people in every organization navigating AI. And here’s the number that should stop every leader cold: more than 85% of employees are still in the early stages of AI adoption. Not resistant. Not opposed. Just early. Less than 10% have reached meaningful AI collaboration, and only about 5% of companies are generating AI value at scale.
Which means the people in your next training session aren’t where leadership thinks they are.
- The AI Champion is first to test, first to share, already three steps ahead. They’re your most powerful asset in any room.
- The Independent Explorer is quietly building skills nobody assigned them. Self-directed, curious, not waiting for permission.
- The Organizational Adopter will engage once the tools and direction are clearly established. Open but cautious. Waiting for proof before they commit.
- The Passive Observer knows AI exists and isn’t convinced it’s worth the trouble yet. They’re not opposed — they’re unconvinced. Waiting for someone to show them something real.
- The Cautious Skeptic is the most experienced person in the room and the most resistant. They’ve seen tools come and go. Their hesitation isn’t ignorance. It’s a belief gap that facts alone won’t close.
None of these people are wrong. All of them will be in the audience. And the worst thing I could do is walk in and try to resolve that tension by picking a side.
The best thing I can do is design an experience that meets all five where they are and moves them somewhere more capable.
Math Had Its Calculator. Writing Has AI. Neither Survived Intact.
I’ve been thinking about what that actually means. And I keep coming back to calculators. I remember when calculators arrived in math classrooms. The same fear. The same bans. The same conviction that if students could just push a button, they weren’t really learning. And I remember how that resolved — not by removing the tool, but by rethinking what we were actually trying to teach. We stopped grading computation. We started grading reasoning.
Then I thought about writing. And my English teacher.
She had a phrase she repeated so often it stuck permanently: “Writing only gets better with rewriting.”
I believed that for decades. I still believe part of it.
But AI has complicated it in a way I’m still working through. Because what that phrase really meant — what she was trying to teach — wasn’t that the act of rewriting was sacred. It was that the thinking writing forces you to do is what matters. The struggle to find the right word slows you down enough to figure out what you actually believe. The rewrite is where the idea gets clearer.
AI can now do the surface work of rewriting faster than any of us. What it cannot do is have the idea. What it cannot do is know what you believe — or help you figure it out — unless you bring the thinking first.
With math, we were protecting computation. With writing, we’re protecting something closer to the soul — the primary way humans have made sense of experience, connected across time, and figured out what they think.
That’s worth protecting. Just not by banning the tool.
We’re Not Behind on Technology. We’re Behind on Policy.
This isn’t just an organizational challenge — it’s a global one, and the policy infrastructure is being built whether we’re ready or not. The EU AI Act is already classifying AI systems by risk level and placing real obligations on organizations that deploy them. UNESCO’s Digital Competency Framework is giving countries a roadmap for what genuine AI literacy looks like — not just AI adjacency. And the US Department of Labor has launched its AI Literacy Framework, AI Apprenticeship, and its “Make America AI-Ready” initiative, offering free national coursework defining what AI competency looks like for every American worker.
Most organizations are still catching up. And most L&D teams are still writing policies that treat AI like a cheat code rather than a capability to be taught and measured. That gap — between where policy is heading and where practice currently sits — is exactly where learning professionals matter most right now.
And yet policy lands on people, not organizations. It arrives in rooms full of Champions who are already ahead of it, Skeptics who’ve seen mandates come and go, and Passive Observers who are still waiting to be convinced it matters. That’s the gap no framework closes on its own. That’s the work.
The Question That Changes Everything
The wrong question is: How do we stop people from using AI?
The right question is: Assuming everyone in this room — every learner, every professional, every person you’re designing for — already has access to generative AI: what human capabilities do you care most about? And how will you design experiences that surface them?
That question changes everything. It stops the conversation about policing and starts the conversation about designing.
I don’t have a perfect answer yet. I’m not sure anyone does. But I think it’s the right question. And I think we’re the right people to be asking it.
I’ll be back soon to share what I heard in that room — what landed, what surprised me, and what it’s telling me about where this field needs to go next.
In the meantime — which of those five people are you right now? And what would you most want someone to understand before they walked in to lead that conversation with your team?
Lee Taylor-Nelms, Ed.D.