How do you reskill a team around AI workflows without losing velocity?

What happens to your team's thinking when everything gets easier?

That's not a philosophical question. It’s the practical, high-stakes dilemma every leader faces when they bring AI into their workflow. And spoiler alert: the immediate answer is usually not “thinking gets better.” In fact, in most cases, it gets worse—faster.

We're seeing a strange paradox unfold inside companies right now. AI is being positioned as a tool to multiply human potential. But more often, it’s functioning as an intellectual off-switch. The slider decks are slicker, the code ships faster, and the content practically writes itself. But quietly, dangerously, the thinking behind it all starts to erode.

Let me give you a real example.

At a creative agency, their strategy team started using AI to generate client presentations. Not just formatting them—coming up with actual ideas. It worked. Kind of. Within weeks, they were pumping out more decks than ever. But the “creative” insight for a fintech startup looked naggingly similar to the one they pitched to a healthcare client the month before. The substance was melting into sameness. Fast, efficient sameness.

We're Training Our Teams to Stop Thinking

This isn’t a luddite rant. Nobody’s saying ditch the tech.

But look closely at what’s happening: instead of freeing up humans to think better, AI is being used to avoid thinking altogether. Why struggle with assumptions or wrestle with first principles when you can ask ChatGPT, “Give me five reasons why this won’t work”? You’ll get fluent, coherent answers in ten seconds. Reasonable answers. Which is exactly the problem.

Because a reasonable answer isn't always the right one. Or even a good one.

It’s intellectual fast food: immediate gratification, slowly starving your cognitive metabolism. That messy middle—the ambiguous fog where novel insights are born—is quietly being engineered out of the modern workflow. You don’t even realize it’s missing until it’s too late.

Ask any exec how many truly original insights their team surfaced in the last quarter. Now ask how many “good enough” pieces of deliverables AI helped produce. See the gap?

Reskilling Isn’t Cosmetic. It’s a Mental Reboot.

A lot of companies are treating AI integration like a minor upgrade: toss a few tools into the mix, run a couple lunch-and-learns, and voilà—your team is “reskilled.” This is delusional.

Using AI doesn’t just add capabilities. It shifts the center of cognitive gravity. The kind of work that matters changes. Designers no longer just craft UIs—they design how models interpret context. PMs aren’t just writing specs—they’re orchestrating agents. Developers become system curators, not line-by-line code monkeys.

If you keep the thinking structure the same, but swap in AI tools, you’re not modernizing. You’re strapping a jet engine onto a horse-drawn carriage.

And here's the kicker: the first step in actually reskilling is slowing down.

I know that sounds like heresy, but hear me out. The fantasy that you can wire in AI upgrades while maintaining full throttle velocity is exactly the kind of consultant-speak that sells slide decks and torpedoes strategy. Real reskilling takes a temporary dip in speed—not because people are dumb or slow, but because you’re asking them to unlearn.

You’re asking them to stop doing work the old way, rethink where value comes from, and reorient around an entirely new spectrum of cognition. That’s not a course module. That’s a rewiring.

Reskilling ≠ “AI Training”

Throw “prompt engineering workshops” at your team all day, and you’ll build faster content pipelines that say nothing new.

That’s not transformation. That’s treadmill optimization.

You want real AI leverage? The kind that compounds?

Then stop thinking about skill as “using the tool.” Start thinking about it as owning the workflow. What parts should be human-governed? Where can AI take the wheel? What gets harder, not easier, now that the pace of iteration is faster than ever?

Let your team answer those questions out loud. Don’t whiteboard in secret and deliver a “new process” from above like it’s an Ikea shelf. Treat reskilling like a product launch. Run sprints. Assign an AI Enablement Lead—not the IT guy, but someone who understands both the craft and the new logic AI brings to it. Think “design jams with models,” not “training decks with bullet points.”

Stripe does this brilliantly. Their engineers regularly pause to retool their own environment. Not because they’ve fallen behind, but because they know that sustained high velocity requires fixing tooling and process at the source. That’s the mindset. Not perpetual motion, but regenerative engine-building.

Don't Reskill for the Jobs You Have—Reskill for What the Job Is Becoming

There’s this dangerous drift happening.

Teams are spending so much time using AI to replicate old workflows faster that they forget to ask whether those workflows even make sense now. A media company used to churn out SEO articles manually—20 a week. AI lets them 5x that volume. But instead of training every writer to use GPT like a turbo typewriter, the smart play was this: train editors to orchestrate prompt chains, tone filters, and QA systems. Half the team became AI-native producers. The others shifted to higher-order roles—or moved out.

Uncomfortable? Sure. Necessary? Absolutely.

The goal isn’t to keep everyone doing the same job, just faster. The goal is to redefine value in the new paradigm—clearly, honestly, and ruthlessly.

And that means asking the weirdly liberating question: now that AI exists, what should we stop doing entirely?

What human jobs still require human judgment? Where does taste, intuition, contrarian insight sit in your value stack? Because that’s your moat. That’s the edge AI can’t replicate—at least not yet.

Sacred Confusion

We’re losing something in the move-fast-with-AI narrative. It’s subtle, but lethal.

We’re automating away the messy middle. The fog of uncertainty. The awkward whiteboard sessions where no one has the answer and tension translates into innovation. And since there’s no KPI for “time spent being confused,” nobody notices it’s gone.

But the best work—the stuff that changes how businesses operate, how customers behave, how markets move—comes from that space. You don't stumble on breakthroughs by asking a chatbot. You stumble into them by combining two unrelated thoughts you had over coffee and a headache.

That doesn't happen on autopilot.

So maybe the golden skill isn’t learning how to use AI—it’s learning when to turn it off.

Block off the “think zone.” Make it sacred. Give your team permission—not just to use the most powerful tools, but to occasionally step away from the noise. To sit with hard, ambiguous questions and resist the urge to answer too soon.

Because what’s the point of moving faster if you no longer know where you’re going?

Final Thought: Redefine Velocity

You can duct-tape AI onto your processes and crank output. That’s not hard.

But if velocity means movement in the right direction, then you better make damn sure you’re not just speeding up mediocrity.

• Don’t reskill your team to preserve old workflows. Reskill to kill them.
• Pause intentionally. Slow down to unlearn before you speed up again.
• Guard the messy middle—it's where all original thinking starts.

Reskilling around AI isn’t a speed bump. It’s an off-ramp. And if you take it right, you don’t just go faster—you build better roads.