I want to tell you about a classroom moment that stayed on my mind for a while. 😬
I was running a workshop on AI automation. Good group with very smart people. Professionals juggling clients, families, side projects, the whole circus. We’d gone through the theory, talked through use cases, and then I gave them the practical exercise: actually build an automation. Map a workflow. Connect the tools. Make the thing run by itself. 👌
You know what happened? They filed their nails. They focused on the other parts of the work. They opened LinkedIn. They tried to do anything and everything except that one exercise.
Here’s what made it quietly absurd.
I’d asked them earlier to describe their ideal client. Almost every single persona they put on their decks was some version of the same person: overwhelmed, time-poor, juggling professional responsibilities and personal life with both hands and no spare fingers.
The busiest person imaginable. And the solution they were building for that person? An automation that would give time back. That would handle the repetitive, the routine, the mind-numbing tasks their clients hated.
They could see that automation was the answer. For someone else. Just not for themselves. Not today. Maybe next week.
This is not a one-off. It’s a pattern. And it has a name. 🤔
Check What Makes Automation Hard to Grasp
The psychology has been peer-reviewed. You’re not special.
In 2015, researchers at Wharton ran a series of incentivised forecasting experiments. Participants watched an algorithm outperform a human forecaster, saw both make errors, and were then asked to choose which one they’d rely on going forward. The result was predictable and maddening: people ditched the algorithm the moment they saw it err … even when its errors were smaller and less frequent than the human’s.
They didn’t apply the same logic to the human forecaster. Humans get second chances.
Algorithms, apparently, do not. 🤷♂️
The follow-up study, also from Wharton, found the cure was almost embarrassingly simple: let people tweak the algorithm’s output, even slightly, and they used it. Give people a tiny bit of control, and the aversion collapses. It turns out we don’t actually fear automation. We fear losing agency. Which is a very different problem, and one that a three-day workshop typically does nothing to address.
Layered on top is something economists have documented since 1988: status quo bias. The systematic human tendency to stick with what we’re doing even when a better alternative is sitting right in front of us, free to use, with a tutorial and everything. Knowing about a tool and having access to it barely registers as progress if the default behaviour (doing the task manually, as usual) carries a disproportionate psychological weight.
This is why your Zapier account has three zaps, and two of them were built during a free trial in 2022. 😅
The Fear Nobody Says Out Loud… But I Do!
There’s a more uncomfortable explanation underneath all the procrastination, and I find it more honest than “I just haven’t had time.”
If you fully automate a task you’ve been doing by hand for years, you’ve just confirmed it was automatable all along. And if it was automatable, then a chunk of your professional identity (the part built on doing that task well, carefully, with expertise) was sitting on shakier ground than you thought.
That is not a pleasant realisation. So the brain, being the magnificent self-protecting organ it is, creates reasons not to automate. It finds the exercises boring. It decides now isn’t the right moment, and it reassures you that your situation is unique and the tools won’t really understand the nuance.
Research on AI identity threat identifies two distinct fears at work: the threat to expert status and the threat to autonomous decision-making. Both independently predict resistance to AI tools, and both are entirely independent of whether the tools are actually useful. You can know something is useful and still avoid it because using it feels like a small admission of replaceability.
Pew Research found in early 2025 that 52% of workers are more worried than hopeful about AI in their workplaces, and (this is the part that should give everyone pause) worry increased among people who actually used AI regularly, not decreased. Exposure didn’t calm the anxiety. It amplified it. The more you use the thing, the more you understand what it can do, and the more unsettling that becomes if you haven’t made peace with your own value proposition.
Harvard Business Review reported that 31% of knowledge workers admit to actively working against their own company’s AI initiatives. We’re not talking about passively ignoring them. It’s actively undermining them. 🫣
In a workshop setting, that looks like very detailed questions designed to find edge cases where the automation wouldn’t work, offered not in a spirit of curiosity but in a spirit of “aha, see, it can’t replace me.”
To which I want to gently say: nobody is trying to replace you. The automation is trying to replace the part of your day that you hate doing. Let it.
The “it’ll feel robotic” Objection is Mostly Wrong
The second most common resistance I hear, after the time excuse, is the authenticity one. “My clients will be able to tell.” “It’ll feel cold.”
“I need to write everything personally.”
There’s a kernel of truth in here. Research from Boston University and NYU found that people resist AI in situations where they believe their uniqueness isn’t being accounted for… and that this resistance collapses when the AI is personalised or when it’s positioned as supporting a human rather than replacing one. The aversion is real. It’s just aimed at generic automation, not thoughtful automation.
Here’s the part that tends to land like a cold shower. A 2023 study published in JAMA Internal Medicine asked licensed clinicians to rate responses to patient questions, blind to whether they came from a physician or ChatGPT. The AI responses were rated empathetic or very empathetic nearly ten times more often than the physicians’ responses. Nine point eight times. The study involved Reddit’s r/AskDocs — so it’s a specific context, not a general proof… but it’s a useful reminder that “human” and “warm” are not the same thing, and “automated” and “cold” are not the same thing either.
The cold emails and the robotic follow-up sequences people complain about aren’t bad because they’re automated. They’re bad because they’re generic. Bad automation is just bad communication at scale. Good automation is relevant communication at scale, which is actually harder to achieve manually because manual processes don’t scale.
McKinsey found that 71% of consumers actively expect personalised interactions and 76% get frustrated when they don’t receive them. What frustrates people isn’t automation per se, but feeling like a number. You can make someone feel like a number manually, and you can make them feel genuinely seen through a well-built automated sequence. The tool isn’t the variable. The thinking behind it is.
Same Tools, Wildly Different Outputs. Why?
Back to my classroom. Here’s the thing that quietly amazed me by the end of the workshop. Every student started with the same brief. The same tools. The same LLMs. The same instructions. And by the time the sessions were done, no two outputs looked alike.
One person had built something elegant and minimal, exactly what they needed. Another had gone sideways into something nobody asked for, but that turned out to be genuinely interesting. A third had solved a problem I hadn’t even identified as a problem. A fourth had made something a bit chaotic, but with a creative logic to it that, once they explained it, made complete sense given how their mind works.
None of it was generic. All of it was them.
This is the thing that gets lost in the “automation makes everything samey” conversation. The HBS and BCG research on 758 consultants using GPT-4 found that AI users completed tasks 25% faster with over 40% higher quality… but the bottom half of performers gained most in terms of floor-raising, while top performers gained differently, in strategic direction and throughput. The tool doesn’t produce average output, but output shaped by whoever is directing it.
Taste is the variable. Judgement is the variable. The person asking the question, framing the brief, knowing when an output is good enough and when it needs another pass: that’s entirely human. Sam Altman has said publicly that “taste” is one of the qualities he actively hires for, because execution is increasingly commoditised, and judgment about what’s worth executing is not.
Your creativity and criteria don’t disappear when you use an AI tool, rather amplified. The question is whether you trust yourself enough to let that happen. 🙌
The Cruellest Irony: Busy People Are Too Busy To Fix Being Busy
Let’s go back to those personas. The ones on the decks. The overwhelmed, time-starved professionals who needed automation to give them their lives back.
Here’s something Harvard and Princeton researchers documented over a decade of study: time scarcity does to cognitive capacity roughly what financial scarcity does. When you’re stretched too thin, you tunnel. Your brain narrows focus to whatever is urgent right now and literally cannot process the strategic options sitting just outside that tunnel. Including the option of building a system that would relieve the pressure. The busier you are, the less bandwidth you have to do the thing that would make you less busy.
This is why research on the “mere urgency effect” found that people systematically choose objectively worse, more urgent tasks over better, more important ones, and the effect is strongest in people who identify as busy. Busyness isn’t just a state, but a filter that blocks out exactly the kind of work that would end it.
There’s a social component too. Harvard Business School’s Ashley Whillans found that roughly 80% of working Americans feel “time poor”, and the research shows that feeling busy raises social status. People complain about it, but they also wear it. Being visibly slammed signals importance. Being visibly automated signals something else: efficiency, maybe, but not the same kind of heroic effort that our professional cultures tend to reward.
Nobody tells the story of how they set up a workflow on a Tuesday afternoon and now their Mondays are free. They tell the story of the all-nighter.
A PayPal survey of about 1,000 small businesses in 2025 found that 82% said AI is essential to their future, and only 25% had actually integrated it. The top barrier cited: not enough time to properly explore the tools.
Let that sit for a moment.
They know it’s essential.
They haven’t done it.
Because they’re too busy.
Doing the things that automation would handle.
Which would give them the time.
To implement the automation. 🤦♂️
It would be funny if it weren’t so expensive. 🤷♂️
What the Workshop Students Figured Out, Eventually
The ones who pushed through the discomfort and built the thing (even badly, even incompletely) came out the other side having learned something that no amount of watching tutorials can teach. They learned that their taste survived the process. Their judgment was still intact. The output reflected them, not some generic AI average. The tool hadn’t replaced them. It had cleared the decks for the part of the work that only they could do.
Harvard Business School research following BCG consultants using generative AI identified three distinct modes of working: those who fused with AI across their workflow and gained fluency, those who divided labour strategically and deepened their own domain expertise, and those who handed over control entirely and gained very little from either direction. I talk about this a bit over here. The difference wasn’t the tool. It was the degree to which people brought themselves to the process.
Automation buys you back the hours that were going on execution, so you can spend them on the things that actually require you.
The thinking.
The relationships.
The creative decisions.
The weird left-turn that turns out to be the best idea in the room.
But you have to be willing to build the first workflow. Even if it’s clunky. Even if you file your nails a bit first.
The procrastination is understandable. The psychology behind it is well-documented and, honestly, quite human. None of us loves the moment when we might discover something we do well could be done without us. But the evidence is pretty clear on what happens on the other side of that discomfort: you come out knowing more about what you actually bring to the table, not less.
That’s worth an afternoon of mild existential unease.
Go build the thing. 🦾
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