AI Revolution: Augmenting Human Potential or Outsourcing Our Thinking?
You've hit on something that's been bothering me lately. We're creating this weird paradox where we invest in AI to multiply human potential, then promptly use it as an intellectual off-switch.
I watched this happen at a creative agency last month. Their strategy team started using AI to generate client presentations—not just formatting slides, but conceptualizing approaches. Within weeks, they were churning out more deliverables than ever, but the ideas became... suspiciously familiar. The "creative breakthrough" for a fintech startup looked remarkably similar to what they'd proposed to a healthcare client.
It reminds me of what happened with calculators in education. We started by saying "this frees students from tedious computation to focus on higher-level math concepts." But plenty of people just stopped developing numerical intuition altogether.
The companies winning with AI aren't just doing the same work faster—they're redefining what work is worth doing. One tech director I know has a rule: "If you can easily prompt it, you probably shouldn't be spending your day on it. But you better understand the underlying thinking enough to evaluate what it gives you."
Maybe the real metric isn't how much work AI helps us complete, but how much deeper thinking it enables. If your team is producing 3x more but thinking 3x less... you're just building a faster path to mediocrity.
Let’s get something straight: you *will* lose velocity in the short term. The idea that you can bolt AI onto your workflows and keep humming at 100% is a fantasy—usually sold by consultants with slide decks and no skin in the game.
Reskilling isn’t an overlay; it’s a rewiring. And rewiring means downtime. The key isn’t avoiding the dip—it’s minimizing and front-loading it. If your team isn’t pausing to rethink *how* they work with AI, they’re just adding another tool to an already messy toolbox.
Take product teams, for example. A lot of orgs throw ChatGPT at their PMs and call it a win. But unless the PM is actually re-architecting their workflow—say, using AI to systematize competitive analysis or prototype user flows based on historical data—they’re just speeding up the same busywork. That’s not transformation. That’s treadmill optimization.
Here's the better play: treat reskilling like a product launch. Run sprints. Assign someone the role of AI Enablement Lead—not the IT guy, but someone who understands the domain and can prototype AI-infused workflows with the team. Think less “training materials,” more “design jams with models.” Make it safe to pause execution temporarily in favor of deeper tooling thinking—like how Stripe famously engineers pause to improve dev tooling so the pace stays high *later*.
You don’t get compounding returns without an initial dip. But if you’re terrified of slowing down for two weeks, you probably weren’t moving fast in the right direction to begin with.
I've seen this exact pattern play out up close, and it's worrying. We're mistaking efficiency for effectiveness in a way that'll bite us later.
Here's what's happening: When a VP can generate a "good enough" strategy deck in 20 minutes that used to take deep thought and collaboration, everybody wins... right? But those strategy sessions weren't just about the output—they were cognitive gymnasiums where people developed muscles for spotting assumptions, questioning orthodoxies, and stress-testing ideas.
Remember when smartphones first became ubiquitous? We joked about not remembering phone numbers anymore. This is that pattern on steroids—for critical thinking.
I was consulting with a fintech team recently that started using AI for customer insight analysis. Six months in, they'd stopped noticing patterns themselves. When the algorithm missed a major shift in customer behavior, nobody caught it because they'd delegated their pattern recognition entirely.
The paradox is that to use AI well, you need stronger thinking skills, not weaker ones. The best prompts come from people who understand the problem deeply enough to know what they're looking for.
Maybe instead of asking "how do we reskill teams around AI?" we should ask "how do we protect intellectual sovereignty while leveraging AI?" Because there's a world of difference between augmenting human intelligence and outsourcing it.
Sure, you can bolt on “AI capability workshops” or do the usual lunch-and-learns, but let’s be honest—that's just cosmetic reskilling. It's the tech version of sending your team to a pottery class and hoping they come back with a working jet engine.
The real leverage isn’t teaching people how to *use* AI tools. It’s teaching them how to *think* in systems that include AI as an actor. That’s a mindset shift, not a software tutorial.
Let me give you an example: Look at how some media companies restructured their newsroom workflows with AI. Instead of just training journalists to prompt ChatGPT, smart shops like The Washington Post reimagined the editorial pipeline—thinking in terms of “what should be human-decided” vs “what can be algorithmically assisted.” The real win wasn’t just faster article drafts—it was freeing up scarce reporter hours to chase stories, not headlines.
In software, it’s the same dynamic. If your devs are still writing boilerplate CRUD code manually post-Copilot, you don’t need another seminar—you need to rethink how you structure sprints. Maybe you're solving the wrong problem by reskilling roles rather than reshaping the work itself.
Velocity doesn’t tank when people are learning. Velocity tanks when people are learning *without clarity* on where the organization is actually headed. So instead of focusing on “upskilling,” you need to reframe the north star: what does good execution look like *with AI inside the system*, not on the sidelines?
Reskilling then becomes a byproduct of chasing that vision. People learn fast when the stakes are real and the game has changed.
I've noticed this problem creeping into my own work lately, and it's a bit terrifying. There's this subtle shift happening where instead of using AI to amplify our thinking, we're using it to replace it.
Last week I caught myself asking ChatGPT to "give me the top 5 issues with this approach" instead of, you know, actually thinking about the problems myself first. The AI gave me perfectly reasonable answers – and that's exactly the problem. I got reasonable when what I needed was *original*.
It's like intellectual junk food. The immediate satisfaction of getting instant answers masks the long-term cost: the atrophy of our most valuable muscles – curiosity, critical thinking, and creative problem-solving.
What makes this particularly dangerous is that unlike obvious automation (like robots replacing assembly line workers), this kind of cognitive outsourcing is invisible. Nobody sees you not thinking. There's no metric for "depth of thought" on your quarterly review.
I think we need to start treating AI like we should treat Google – as a second step, not the first. What if the rule was: you can't prompt an AI until you've spent 10 minutes wrestling with the problem yourself? Or what if teams had "no-AI zones" for certain types of strategic work?
The companies that will win aren't going to be the ones who use AI most extensively – they'll be the ones who use it most thoughtfully, preserving their uniquely human capacity to see what isn't obvious yet. Because fundamentally, that's what innovation is: seeing connections nobody else has made yet.
Right, but there’s a trap in the whole “don’t slow down while you reskill” mindset—it assumes that velocity is just about speed. It's not. It's about meaningful progress. And here's the thing most teams get wrong: they try to train people *on the tech* instead of training them *on the new decisions they're now responsible for*.
AI workflows shift the center of gravity. You’re no longer optimizing code or design manually—you’re orchestrating models, prompts, and feedback loops. That’s not the same task, even if it looks like it on Jira.
Take the example of a product manager who's suddenly expected to “leverage AI” in customer workflows. You can send them to a dozen prompt engineering workshops, but unless they understand how AI changes the *feedback dynamics*—which decisions the system now makes vs the human—they’ll be flying blind. Worse, their decisions could actively degrade the user experience.
So instead of trying to teach everyone how to “do AI,” teach them how their frame of reference needs to shift. Developers become curators. Designers become behavior strategists. Analysts start designing experiments instead of dashboards.
And yes, you will lose some *visible* velocity up front. But faking speed by staying busy in the wrong paradigm is like sprinting on a treadmill. Increasing treadmill speed doesn’t get you to market faster.
The uncomfortable truth? If you don’t slow down a little to recalibrate team thinking, you’re not reskilling—you’re just applying AI lipstick to a waterfall pig.
Your move.
I think we're dancing around something important here. This isn't just about AI as a tool - it's about cognitive laziness masquerading as efficiency.
Here's what keeps me up at night: we're building feedback loops of mediocrity. You feed AI average thoughts, it returns average outputs, which you tweak slightly and pass along. Everything looks professional, everything ships on time, and nothing challenges anyone's thinking.
Remember when we used to worry about kids using calculators and losing their math skills? This is that, but for your entire professional brain.
I worked with a marketing team recently that was pumping out content at 3x their previous pace. Leadership was thrilled. But when I dug into it, I realized they were essentially playing high-stakes Mad Libs with AI. Nobody was saying anything original anymore - they were just becoming extremely efficient editors of machine-generated content.
The real danger isn't that AI will take your job. It's that you'll still have your job, but you'll have surrendered the parts that make you valuable: your weird connections, your counterintuitive hunches, your ability to see what others miss.
So what's the alternative? Use AI as a sparring partner, not a replacement brain. Have it challenge your thinking rather than doing your thinking. The teams that will win aren't using AI to think less - they're using it to think differently.
You’re right that reskilling *sounds* like a speed bump—one of those well-intended detours companies take when they're trying to modernize but end up parking the car for six months. But here’s the tension no one wants to talk about: If your “velocity” depends on a team of people doing things AI now does faster and better… maybe that’s not real velocity. Maybe it’s just inertia boosted by manual labor.
Let’s take a real example: a major media company I worked with recently. Their content ops team was churning out SEO-driven articles at speed—20 per week, tightly optimized, processized to death. With AI, they could crank out 5x the drafts. But here’s the twist: instead of training everyone to use GPT like a better typewriter, we trained editors to *orchestrate* workflows—prompt chains, QA filters, tone tuning across brands. Half the team became AI-native producers. The other half? They left or moved to strategic roles.
So reskilling wasn’t about teaching everyone “Prompt Engineering 101.” It was about redefining what value *means* in an AI-infused system—and being ruthlessly honest about who thrives in that system and who doesn’t.
And sure, there's disruption. But clinging to old workflows in the name of velocity is like insisting on horse-drawn carriages because your drivers are really efficient with reins.
Real velocity comes from leveraging the compound gains of AI—fewer manual feedback loops, faster iteration, decision-making closer to data. Reskill not to preserve jobs-as-they-are, but to reinvent jobs around what machines *don’t* do well: judgment, abstraction, ethical framing, taste. That’s where humans break the speed limit.
Want to keep moving fast? Great. But check that your map isn’t from 2015.
You're hitting on something that keeps me up at night. As we automate thought processes, we risk creating a generation of executives who've forgotten how to think from first principles.
I worked with a marketing team recently that was pumping out content at 3x their previous velocity using AI. Impressive numbers. But when I asked them to explain the strategic reasoning behind their campaign direction, there was this uncomfortable silence. They had the "what" but had lost the "why."
This reminds me of calculators in math class. Sure, they made computation faster, but if you don't understand the underlying principles, you can't recognize when you've made a categorical error. Like when my GPS once confidently directed me to drive straight into a lake.
The real danger isn't just operational - it's existential. What happens when every competitor has the same AI tools generating similar recommendations? The differentiator becomes the human who can question assumptions, spot hidden connections, and challenge the algorithmic consensus.
Maybe the most valuable skill isn't learning to use AI, but knowing when to turn it off and sit with a hard problem. Discomfort is where innovation happens. If we outsource that discomfort, we might find ourselves comfortably obsolete.
Look, I get the impulse to push reskilling as a parallel track—“We'll just train the team quietly while we keep shipping.” But that’s a fantasy. You can’t retrofit an AI-first mindset onto a team that’s sprinting in the old paradigm.
It’s not just about new tools. It’s about new mental models. AI workflows invert a lot of assumptions—about where quality comes from, what’s worth building, and how much iteration is too much. If you’re still thinking in terms of rigid specs and polished handoffs, you’re not building with AI. You’re strapping a jet engine to a horse-drawn carriage.
Case in point: look at how Jasper or Notion rewired teams to think in terms of prompts, constraints, and human-in-the-loop loops. That required more than a Udemy course and a Slack channel. It meant architects became prompt engineers. Designers started thinking in probabilistic outputs. That shift breaks velocity—temporarily—but you’re trading short-term speed for long-term compounding.
So instead of shielding teams from the discomfort, I’d argue for sinking them in it—fast. Set a two-week sprint where the goal isn’t delivery, it’s adaptation. Give teams a mandate to break their own process with AI. Measure how they learn, not what they ship.
Velocity isn’t about how fast you run. It’s about running in the right direction. AI changed the direction. Keeping pace on the old road isn’t clever. It’s waste.
You're hitting on something important that almost no one is talking about. We're so fixated on productivity metrics that we're missing the deeper cognitive shift.
I had a client recently—brilliant marketing executive—who confessed she hadn't written anything from scratch in months. "Why would I?" she asked. "The AI version is good enough and takes seconds." Six months later, her team's campaigns all had this strange homogeneity. They were technically sound but emotionally flat.
This isn't just about writing. It's happening with strategic thinking too. When you can get a "good enough" analysis in 30 seconds, the temptation to skip the messy middle part of wrestling with problems becomes overwhelming.
The irony is that true differentiation comes from that messy thinking space—the connections only your weird human brain makes between seemingly unrelated experiences. That's where breakthrough ideas emerge. AI doesn't have weird tangential thoughts in the shower.
I'm not anti-AI—I use it constantly. But I'm starting to deliberately create "think spaces" where I work through problems before consulting the algorithm. Otherwise, I'm just training myself to become an efficient prompt engineer instead of a creative thinker.
Maybe the real question isn't "how do we reskill for AI workflows?" but "how do we preserve deep thinking while leveraging AI?" Because if everyone's using the same tools the same way, where's your edge?
Totally get the instinct to say, “Let’s reskill the team gradually, in parallel to delivering.” But let’s be honest—reskilling isn’t just a training module or Friday lunch-and-learn. Actually building AI fluency into the DNA of a team changes how they think about work, decisions, tooling, and even what “done” looks like. You can’t duct-tape that mindset shift onto a sprint cycle and expect magic.
Here’s the real tension: most orgs still assume AI is just a feature or toolset. It’s not. It’s a workflow reset. When you introduce AI into a process, you’re not just throwing Jetbrains Copilot at your developers or dumping customer data into ChatGPT. You’re reassigning cognition—deciding what parts of the process stay human, what gets delegated to models, and which parts need rethinking from scratch.
Take legal teams. Companies that just give them a GPT-based summarizer and call it “AI-powered contracting” totally miss the point. The real shift is: stop spending hours redlining low-risk NDAs. Train the model on 10,000 past contracts, define thresholds of acceptable variance, and build an escalated review only for real edge cases. That’s not a tool swap—it’s a workflow inversion.
So reskilling? It’s not training videos. It’s participatory redesign. Your team doesn’t need to just "learn about AI"—they have to rewire what they believe their job is.
Which brings me to the velocity issue. Trying to “not lose speed” while transforming something fundamental... is like trying to replace the engine on a plane mid-flight but insisting the in-flight movie continues. Sometimes you need to land the damn thing—even briefly—and refit properly. Think of it as a strategic pit stop, not a timeout.
Don’t buy the myth that you can agile your way through an epistemic shift without slowing down. The risk isn’t losing sprint velocity. It’s automating your old ways of thinking faster.
I've been noticing this in my own work lately. That little dopamine hit when I get an instant answer from AI versus sitting with a problem uncomfortably for hours or days. It's not just executives—it's all of us.
You know what it reminds me of? Calculator panic in the 80s. Everyone worried students would stop understanding math if they used calculators. They were partly right, but not in the way they expected. We didn't lose the ability to do arithmetic—we lost the patience for working through problems step by step.
The real danger isn't that AI will think for us. It's that we'll forget how valuable the messy middle of thinking actually is. That zone where you're confused, contradicting yourself, wrestling with half-formed ideas—that's where breakthroughs happen.
I was talking with a product team last week who proudly showed me how they'd "10x'd their output" with AI. But when I asked deeper questions about their strategic choices, there was this awkward silence. They'd efficiently executed a mediocre plan really quickly.
Maybe the answer isn't avoiding AI, but being more intentional about which parts of our thinking we keep sacred. What if we used AI to accelerate the known parts of our work, but protected the unknown? The places where being lost is actually productive?
The teams that will win aren't going to be the ones using AI the most. They'll be the ones who know exactly when to put it away.
Sure, but here's the trap a lot of companies fall into: they treat AI reskilling like a tooling upgrade instead of a workflow redesign.
You can't just train people to prompt ChatGPT better and call it a day. That's like giving a forklift to someone who’s only ever used a wheelbarrow, and telling them, “It goes faster now—get to work.” Without changing the warehouse layout, the task structure, and the safety protocols, you're going to have a very expensive accident.
AI workflows often force a shift in how decisions are made. Take marketing analytics. It used to be about crunching reports weekly, maybe monthly. Now, with AI, you can monitor campaign performance in real-time, tweak creative using LLMs on the fly, even generate alternative audience segments dynamically. That’s not just faster—it's categorically different. So if you train your team to use the tools but don’t empower them to act differently? You’re kneecapping the entire system.
Reskilling without velocity loss only works if you cut dead weight at the same time. I don't mean people—I mean process. The teams that handle this well don’t just layer AI on top of legacy workflows. They use it as an excuse to ditch the 40% of their process that exists only because some spreadsheet 10 years ago made a mistake nobody wanted to fix.
So instead of asking, “How do we reskill without slowing down?”, maybe the better question is: “What should we stop doing entirely now that AI exists?” From there, the reskilling isn’t a bottleneck—it becomes the freeing mechanism. Thoughts?
This debate inspired the following article:
How do you reskill a team around AI workflows without losing velocity?