We spent 2025 in conversation with L&D leaders — roundtables, interviews, and monthly working sessions across industries. We wanted to understand why some organizations move forward with AI while others stay stuck.
The patterns we found had almost nothing to do with the technology itself. The real blockers were structural: gaps in foundation, communication, and trust.
We’ve compiled what we learned into a practical guide for L&D leaders, and here’s the short version of what stood out.
The real blockers
Three problems showed up in nearly every conversation.
The first is foundation, or rather, the lack of it. Organizations often don’t have shared language about what responsible AI use looks like. What’s allowed? What’s off-limits? What does good use actually mean in this role, on this team, for this type of work? When those questions go unanswered, people default to caution. They hold back because it’s a reasonable response when expectations are unclear.
The second is silos. Teams experiment in isolation. Someone figures out a useful workflow, but there’s no process for sharing it, so someone else in another department spends weeks solving the same problem from scratch. Lessons learned in one part of the organization stay there. The company doesn’t build on its own experience, and instead of internal knowledge compounding, it gets lost.
The third is trust, or more precisely, training without it. We heard from teams that had built technically solid AI curriculum where the instructional design was sound, but adoption still wasn’t moving. When we dug into why, we found an emotional layer that training wasn’t reaching: people worrying about making visible mistakes, looking foolish in front of colleagues, or what AI means for their role in a year or two. Those concerns don’t surface in a skills assessment, but they shape behavior more than any module will. Until they’re acknowledged openly, training alone won’t move anyone.
These three problems are exactly the kinds of problems L&D is built to solve.
Think about what you already do: when a new system rolls out, you help people understand why it matters, how it connects to their work, and what good looks like in practice. You create the conditions for people to build skill and confidence at the same time. AI adoption isn’t a different discipline, it needs that same approach: the human-centered work you’ve always done, but applied to this new challenge.
Where are your people?
So if the blockers are structural and training alone isn’t enough, where do you actually start? With your people.
You might assume that AI readiness varies by department, that your tech teams are further along than your operations teams, or that senior leaders have a different relationship to AI than frontline staff. We didn’t find that. What we found was that readiness has more to do with how someone responds to uncertainty than where they sit on the org chart.
Some people need explicit permission before they’ll try anything. Others are already experimenting before anyone’s provided guardrails. Some will want to know about data handling, while others will want to understand how it scales across the enterprise.
These patterns were consistent enough that we mapped six distinct personas. Our guide goes into detail on each: what drives them, what blocks them, and what kind of support actually helps. Most organizations have all six in varying degrees, and when you understand your mix, you can design pathways that meet your people where they’re at.
So what do you do?
Once you understand the blockers and know who you’re working with, how do you move people forward?
Here’s what we heard from organizations that were making progress:
- Start with relevance — this ties back to the foundation problem. Training that leads with tools instead of purpose won’t land.
- Treat prompting as a skill worth teaching. Most organizations hand people access and hope for the best, which is why results vary so wildly.
- Make governance enabling. Policies that only say “no” create paralysis. Clear boundaries that give people room to experiment work far better.
- Build community around it. A colleague who figured something out is more persuasive than any announcement from leadership.
- And address the fear — that trust gap we mentioned earlier. Organizations that acknowledged concerns openly were further along than those that avoided it.
Where does your organization stand?
The guide walks through how to apply all of this to yourself, your team, and your organization. It includes the full maturity framework, detailed persona breakdowns, and conversation starters for each stage.
