You’re Not Competing With AI. You’re Competing With Whoever Figures It Out First.
Why the real risk to trainers isn’t the technology. It’s standing still while everyone around you speeds up.
A friend of mine is good at their job. Years of experience. Sharp instincts. The kind of person other people ask for by name.
Over the past year, I watched something shift for them. Their team started using AI for the stuff that used to eat hours: drafting slides and outlines, building first-pass content, writing comms. At first it was just a couple people experimenting. Then it wasn’t. Projects that used to take a week started taking days. Turnaround expectations crept up. Nobody announced it. That’s just what was suddenly possible, and possible has a way of becoming expected.
My friend hadn’t picked up those tools yet. So they kept working at the pace that used to be completely normal. Six months earlier, that pace was fine. Now it wasn’t. Nobody said anything. No conversation, no flag, no “hey, you’re falling behind.” Just a slow, uncomfortable sense that something had changed and they couldn’t quite name it. As far as I know, they’re still sitting in that feeling. There’s no tidy ending to this story yet.
It’s Not AI vs. You. It’s You vs. Someone Who Knows How to Use It.
I’ve felt my own version of this. A few years ago, when AI tools started showing up everywhere, my first reaction was the big, vague kind of fear: this is going to take my job.
So I did the thing that felt least comforting at the time. I started taking courses on AI and large language models. Not to become a developer. Just to understand what this stuff actually was, what it could do well, and where it fell apart.
What I landed on wasn’t exactly reassuring, but it was clarifying. AI probably isn’t coming for my job. But someone who’s figured out how to use it well, who’s faster and has freed up more of their time for the parts of this work that actually need a human, that person might be.
That’s the real shift. It’s not human vs. machine. It’s people who’ve built AI into how they think and work vs. people still operating at the old pace without realizing the baseline moved. My friend isn’t behind because they got worse at their job. They’re behind because the definition of “normal speed” changed around them while they weren’t looking.
What This Actually Looks Like
“Use AI more” doesn’t tell you anything useful. So here’s where it actually shows up for me. Three areas, each doing something different.
Synthesizing. Anytime I’m working with a lot of qualitative input, interview notes, survey responses, open-ended feedback, there’s a real cost to finding the themes by hand. You’re not just sorting through volume. You’re fighting your own hypotheses, the patterns you already expect to find, creeping into what you “hear.” I’ll have AI do a first pass: pull out recurring themes, flag what seems to matter most, surface tensions or contradictions. Then I validate it myself, and just as important, I check it against the people who actually said the thing. Does this match what you meant? Did we miss something? Did it invent something nobody said? That accountability step never goes away. But the brute-force part, reading everything and holding it all in your head at once, is where AI earns its place.
Delegating. I’ll hand AI a first pass at a project brief, a draft outline, a rough structure for an activity or assessment. The part people skip, and the part that actually makes the difference, is what comes first. Before I ask for anything, I give it the task, the outcome I’m aiming for, and the guidance it needs to get there: who the audience is, what the real goal/problem to solve is (not just the topic), what constraints we’re working within, what adult learning principles should shape this, whether we’re building toward a skill or just knowledge. That’s the first mile, and it’s where most of the quality gets decided.
The last mile is everything after the draft comes back. I read it the way I’d read a junior colleague’s first pass. What’s missing? What assumption did it make that I wouldn’t have made? A first draft built on real context is something you can shape. A first draft built on “write me something about X” is just noise with formatting.
Thought partnership. This is the one that surprises people most. When I’m working through a new strategy or building something with a team, figuring out what’s actually causing a problem, defining what success looks like, designing a tool or approach, I’ll bring AI in the way I’d bring in a colleague. I’ll say: “You’re an L&D expert. Now you’re a measurement and evaluation specialist. Now you’re an experienced data analyst. What am I missing here?”
But the real value isn’t in what comes back the first time. It’s in what happens next. If something feels off, I’ll push back: “No, I don’t think that’s the right framing because X. Try again assuming Y.” And just as often, it pushes back on me first: “Double check your thinking. It looks like you’re making an assumption that X is true.”
That back-and-forth is where the actual thinking happens. Critical thinking doesn’t disappear when AI is in the loop. It just shows up differently. Instead of generating everything from a blank page, it shows up as the willingness to argue with what’s in front of you.
The Hardest Part Isn’t the Tools
None of this is technically hard. The tools are accessible. The learning curve is real but not steep. There’s no shortage of guides on how to use any of it.
The hard part is everything underneath. It’s being willing to feel like a beginner again, at a point in your career where you’re used to being the expert in the room. It’s handing off a first draft of something when part of you has always believed the first draft is where your value lives. It’s admitting that the way you’ve always worked, the pace, the process, might not be the pace anymore, even if it was fine a year ago.
That’s the part my friend is stuck on, I think. Not the tools themselves, but the discomfort of starting from zero on something everyone around them seems to have already figured out. And that discomfort is exactly why people put it off, which is how the gap gets wider.
If any of this feels familiar, if there’s a quiet voice telling you the ground is shifting and you haven’t moved yet, you don’t need to overhaul how you work this week. Pick one thing. Synthesizing, delegating, or thought partnership. Try it on one real task. See what comes back, and what it frees up. The gap doesn’t close by waiting until it feels less uncomfortable. It closes by starting before you feel ready.
Worried that AI is changing the game faster than you can keep up, and not sure where to even start? Explore Letskillup’s Train the Trainer programs — built to help trainers stay sharp, adapt fast, and keep doing the parts of this work no tool can replace. 🚀
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