by Megan Torrance, TorranceLearning
Across most organizations right now, AI projects are multiplying faster than anyone planned. A new tool here, a pilot there, an enterprise rollout that someone in IT is quietly panicking about. In each of these efforts, the same question keeps surfacing: “Who has the skills to actually make this work with people?”
IT is raising their hand. Operations is raising their hand. Sometimes a newly minted “AI Czar” is raising their hand.
Here’s what I want to say to everyone in the Learning & Performance (L&P) community reading this: your hand should be in the air, and you probably don’t even realize how qualified you already are!
Why are you qualified? It’s not because you’re the best at the technology (although we don’t have to rule that out), but because the work we’ve been doing for years — the workflow analysis, the performance ecosystem thinking, the moments-of-need mapping — is exactly what AI adoption requires. We just haven’t been calling it that.
It’s just another day at the office for us.
Let me walk you through what I mean.
You can read a workflow and see what’s underneath it.
This is where it starts for us, and it’s where AI adoption must start too. Task analysis, workflow mapping, performance gap analysis — we look at what people do and ask why and how and what would have to be true for this to go well. Everyone else is asking, “What tasks can AI do?” We ask, “At which moment does this person need what, and what does the work environment need to provide?” Those are not the same questions, and only one leads somewhere useful.
You know performance support is the architecture, not the afterthought.
Let’s say it plainly: performance support is not a consolation prize for training that didn’t happen. It is the architecture! And what’s become clear as AI tools embed themselves into workflows (e.g., a well-designed prompt template at the moment of Apply, a custom assistant surfaced at the moment of Change, an embedded guide at the moment of Solve) is that this is performance support at a scale we have always imagined but could never quite build. We know how to design for these moments. Our community has been practicing for this exact problem.
You have trust networks across the whole organization.
L&P professionals are rare in that they have genuine relationships — and real credibility — with people at every level and in every function. You’ve worked with the frontline worker and the VP. You’ve been in the warehouse and the boardroom. AI adoption doesn’t succeed through mandates from above; it succeeds through trust built across the organization. That is your home territory.
You know how to interview humans about what they actually do.
SME interviewing is a core L&P skill. Turns out it’s also exactly what you need in order to figure out which parts of a job AI can and can’t augment. We have been doing task analysis for decades. We just didn’t call it “AI readiness assessment.” Time to rename it.
You translate complexity into clarity.
The L&P community is full of professional sense-makers. New technology that nobody fully understands yet, surrounded by jargon and hype and conflicting signals? That is our home turf. We have been making the complex accessible our entire careers.
You know when training is — and isn’t — the answer.
This might be the most underrated skill of all. We’ve spent our careers pushing back on requests to “just train them.” We understand that motivation, environment, support tools, and incentives matter as much as knowledge. That kind of thinking is going to be critical as organizations confuse “running AI training” with “achieving AI adoption.” We know those are not the same thing.
So, what do we do with all of this?
Start by making it visible—to ourselves and to our organizations. The next time someone is forming a team around an AI strategy, an AI pilot, or an AI change management effort, make the case for why L&P belongs on that team.
Then, pick one skill and put it to work this week. Map a workflow for AI readiness. Design a performance support resource for a team that just got a new AI tool. Facilitate a conversation with a skeptical group that hasn’t yet been heard. Start where we always start: with the work, the worker, and the moment of need.
The organizations that get this right won’t be the ones with the best technology. They’ll be the ones that figured out the human side … and this community has been studying the human side its entire existence.
We’ve been doing this work longer than most people realize. Time to say it out loud.