Back in the summer of 2023, the AI buzz was loud and growing louder by the day. In nearly every conversation, learning leaders were asking versions of the same question: How do we get our people ready for this? Not just ready to click through a webinar or try out a chatbot, but really ready—to use it, adapt with it, and lead with it.
Spoiler: It's not just about training. It’s about transformation. And not the kind that happens overnight. In fact, it’s ongoing.
Lately, I’ve been in a lot of conversations—some casual, some strategy-deep—about what it takes to skill up teams for AI. One pattern keeps emerging: The organizations getting the most out of generative AI are the ones doing the most to support their people. They’re not just training on a single tool. They’re building the capacity to work with AI as a class of technology.
So let’s talk about that. Not the hype, but the real work of helping humans thrive in an AI-enabled workplace.
Three layers, not a one-and-done
There’s a temptation, especially when technology is moving fast, to jump straight into tools. “Let’s train everyone on [insert shiny new AI app here]!” And sure, some exposure helps. But that approach usually skips over the foundation people need to actually use the technology with confidence and clarity.
Instead, we’re thinking in layers: AI Literacy, AI Proficiency, and AI Fluency. Each one builds on the last, and each serves a different (but connected) purpose in your transformation strategy.
We’re building on smart thinking from leaders like Markus Bernhardt and Brandon Carson, who frame the conversation around AI literacy and fluency. Their work emphasizes the importance of strategic understanding and dynamic governance across the enterprise.
We agree—and we recognize an additional, practical step that many teams need in between: Proficiency. That’s the hands-on, role-specific skill building that helps people move from knowing what AI is to actually using it well in their day-to-day work.
It’s not about choosing one framework over another. It’s about scaffolding a learning journey that supports both individual growth and organizational change.
1. AI literacy: Start with the basics
Think of this like digital literacy a decade ago—it’s table stakes now. AI literacy is about demystifying technology: What is AI? What can it do (and not do)? Where is it showing up in our tools and processes? What are the ethical risks and guardrails we need to understand?
This isn’t about teaching folks to write code or design neural nets. It’s about awareness, process, and policies. And yes, it often feels like compliance training—that’s because it is a kind of workplace safety. I’ve now heard this from two sources, Markus Bernhardt and a military cyber-intelligence SME: AI literacy is the new cybersecurity.
It’s that important.
Done well, AI literacy builds not just knowledge, but curiosity. It gives people enough grounding to move from fear or skepticism into experimentation.
2. AI proficiency: Practice, not theory
Once you know what AI is, the next step is learning how to use it—on the job, in your context, for your goals.
Proficiency is where things start to feel tactical. It’s how you bring AI into your workflow and how your workflow changes as a result. That could mean summarizing meeting notes, writing performance reviews, analyzing survey data, or creating microlearning content in half the time.
At this layer, the questions shift from what is possible and permissible to how to do these things well.
This is also where risk increases. Without strong literacy under it, proficiency can lead to misuse—or just meh results. The two have to go hand in hand: know the tool, know the rules, then learn to wield it well.
3. AI fluency: From "I" to "we"
Here’s where it gets powerful. Fluency is what happens when AI use moves from individual experimentation to collective capability.
It’s the shift from:
- “What can I do with AI?” to “What can we do together?”
- Trying things out to building shared practices
- Using AI to designing how AI is used across the team
In fluent organizations, people collaborate on prompts. They refine workflows. They co-create guidelines. And they share what’s working—and what’s not—openly.
Fluency is less about technical skill than it is about culture. It’s where experimentation gets normalized and where learning loops (and trust) take root.
How this shows up in practice
When I talk to L&D teams, I ask: How are you scaffolding this journey?
Because it is a journey. And it doesn’t happen in one workshop or one lunch-and-learn. It’s about designing learning that’s iterative, role-relevant, and community-driven.
Some starting points:
- Map use cases before you build the program. What are people trying to do? Where does AI help? Start there.
- Don’t treat all learners the same. Literacy, proficiency, and fluency are different stages that require support. Each department has different needs.
- Build in refresh points. AI changes fast. Yesterday’s best practice might be obsolete tomorrow. Keep the learning loop open.
- Make it social. Fluency grows when teams experiment together. Host AI jams. Share prompts. Build a repository of “stuff that works.”
- Measure what matters. Not just attendance or completion—but confidence, usage, and impact. You may measure impact in terms of time savings, reduction in burnout, more effective output, etc. Be sure your measurements align with the business goals for using AI in the first place.
One more thing: How you roll it out matters
This part often gets overlooked, but it’s crucial.
The way you implement your AI skilling efforts communicates everything about your organization’s approach. Are you empowering people to experiment? Are you modeling trust and transparency? Are you treating this like a checkbox or a capability?
Every course, every communication, every pilot project is a chance to show your people: We’re in this together. We’re figuring it out. And we’re doing it in a way that aligns with our values.
Because, at the end of the day, skilling up for AI isn’t just about keeping up. It’s about showing up—together, curiously, and with a commitment to do this well.
What will you do with this?
So here’s the nudge: Where is your organization on this literacy–proficiency–fluency path?
What’s one step you could take to move your team forward? It could be a conversation. It could be a pilot. It could be a question in your next staff meeting: “What’s changed?”
Because change is here. AI is here. The question is — are we building the skills, the systems, and the shared understanding to meet it?
Let’s make it more than just adoption. Let’s make it a transformation.
Go all in on transformation!
If you’re eager to learn how to use AI and emerging technologies to transform learning at your organization, don’t miss our Pillars of Learning: Technology full-day seminar, a pre-conference event ahead of the Learning Leadership Conference (Learning 2025). Led by Megan Torrance, this seminar explores generative, predictive, and analytical AI, data analytics technologies, and next-generation learning platforms.
Come to the Learning 2025 conference a day early and launch your learning transformation! Pillars of Learning: Technology is on September 30, 2025; the Learning Leadership Conference is October 1–3, 2025. Registration for both events is open now!
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