The Empathy Gap in AI Transformation

I’m on a motorcycle, about sixty miles from home, and I’m getting quieter and quieter inside my own head.

This was not long after I got my first big Harley. I’m on my first long-distance ride – three days, nine hundred miles – and I’ve been anticipating it for weeks with a feeling I can only describe as a healthy respect for the unknown. I’ve got plenty of miles on the bike. I know how to ride. I learned faster than most because I already knew how to drive a manual transmission, which gave me a head start on the mechanics. But I’ve never done this – the long haul, managing luggage, memorizing routes, watching the gas gauge, navigating unfamiliar roads for hours at a stretch. And as the departure date got closer, I noticed something unfamiliar creeping into my thinking: genuine self-doubt.

That was new for me. I’ve run massive ERP implementations, led M&A integrations, built eCommerce platforms from scratch, navigated organizational transformations that affected thousands of people. In those situations, I might be concerned, maybe cautious, but never truly uncertain. I’ve seen enough to know what’s coming, and that experience creates a kind of calm that’s hard to fake and easy to take for granted.

But on that bike, heading into a weekend I couldn’t fully prepare for, I didn’t have any of that. I had theory, some practice, and a community of experienced riders who made it all look absurdly easy. And I had a knot in my stomach that no amount of rational self-talk could untie.

I’m telling you this because that knot – that specific, personal, hard-to-articulate feeling of being out of your depth in something that actually matters to you – is exactly what your team is feeling right now about AI. And if you’ve been through enough technology transformations to feel calm about this one, there’s a very good chance you’ve forgotten what that knot feels like. That forgetting is the empathy gap, and it’s quietly undermining your ability to lead your team through the change you’re planning.

How Can They Make It Look So Easy?

One of the things that stuck with me from that ride was how the experienced motorcyclists I encountered made everything look effortless. Navigating an 800-pound Harley through a tight parking lot, leaning into curves on mountain roads, backing into a spot at a gas station like it was nothing. I’d been riding for a year and I still felt like I was negotiating with the bike every time I made a turn. These folks looked like the bike was part of them.

Call me Danny Duck Walk.

That feeling – watching someone do easily what you’re struggling with – is one of the most corrosive emotions in a professional setting, and AI is producing it at scale. Your team watches a demo where someone builds a workflow in five minutes that used to take a week. A new hire picks up an AI tool on day one and starts producing output that a ten-year veteran couldn’t match for speed. A consultant walks in, connects an API, and automates a process that three people used to manage full-time. And every person watching thinks some version of the same thing: how can they make it look so easy? And if it’s that easy, what does that say about the work I’ve been doing?

With ERP, the fear was mostly about learning curves. “I’ll have to learn a new system, and it’s going to be hard for a while, but eventually I’ll get there.” That’s uncomfortable but manageable. With AI, there’s an extra layer that ERP never had – the nagging suspicion that the tool doesn’t just change how you do the work, it might eliminate the need for you to do it at all. That’s not a learning curve. That’s an existential question, and most people aren’t going to raise it in a team meeting. They’re going to sit with it quietly, let it color every interaction they have with the new technology, and gradually either disengage or resist.

And here’s the thing you might miss if you’re the experienced leader in the room: you probably are making it look easy. Not the AI specifically, but the change. You’ve been through enough transitions to know the pattern – things are chaotic at first, then they stabilize, then everyone wonders what the fuss was about. You know this because you’ve seen it play out multiple times. So you project calm confidence, which is exactly the right leadership instinct in most situations.

But to the person on your team who’s watching their familiar work get automated and wondering what their role looks like in six months, your calm confidence doesn’t feel reassuring. It feels like you don’t understand what they’re going through. Because from where they’re sitting, you’re the experienced rider gliding through the parking lot while they’re still duck-walking their bike and trying not to drop it.

The Memory Problem

Here’s the thing about experience: it replaces feelings with competence. That’s its whole job. The first time you led a major system implementation, you probably felt some version of what your team is feeling now – uncertainty about the outcome, anxiety about your own ability to handle what’s coming, maybe even some quiet dread about all the things that could go wrong. But you got through it. And then you got through the next one, and the next, and somewhere along the way the uncertainty got replaced by pattern recognition. You stopped feeling the change and started managing it.

That’s a good thing. You can’t lead effectively if you’re paralyzed by the same anxiety your team is experiencing. But it comes with a cost that most leaders don’t recognize: you lose access to the emotional reality of what change feels like from the inside. Not intellectually – you can still describe the feelings. You can list them: excitement, frustration, self-doubt, tedium, second-guessing, dread. But there’s a difference between knowing the list and feeling the knot in your stomach, and that difference is the empathy gap.

I didn’t fully appreciate this until I was on that motorcycle. I’d spent years leading teams through transformational change – big ERP rollouts, acquisitions, complete process redesigns – and I thought I understood what my teams were going through. I could articulate it. I could plan for it. I built change management into my project timelines because I knew, intellectually, that people need time to adjust.

But I hadn’t felt it in years. The motorcycle ride put me back in the seat of genuine uncertainty, and it was startling how unfamiliar that feeling had become. The dread before departure. The frustration when my GPS failed on day one because I hadn’t tested it properly¹Sound familiar? We always make tradeoff decisions between time, features, and cost without fully understanding the risk. The quiet self-doubt of watching experienced riders do effortlessly what I was struggling with. All of it was a vivid reminder of something I’d let fade into abstraction.

And that’s exactly what happens with leaders and AI transformation. You know your team is going through a difficult transition. You’ve accounted for it in your planning. You’ve scheduled training, built in extra time for adoption, maybe even hired a change management consultant. But none of that addresses the feeling – the specific, personal, hard-to-shake worry that the world you’ve built your career in is shifting, and you’re not sure you can shift with it. If you can’t connect with that feeling, all your planning comes across as competent but tone-deaf. And tone-deaf leadership, however well-intentioned, breeds exactly the kind of quiet resistance that kills AI initiatives from the inside.

What the Empathy Gap Actually Costs You

In the previous article, I talked about the difference between people who know their jobs and people who truly understand them, and why that gap determines how much value your organization can extract from AI. I suggested some ways to assess where your team stands – the “teach it” test, “what if” questions, paying attention to how people respond when something breaks.

All of those approaches require one thing from your team: vulnerability. You’re asking people to reveal, in real time, the limits of their own understanding. To say “I don’t actually know why we do it this way” out loud, in front of colleagues and leadership, during a period when the subtext of every conversation about AI is some of these jobs might go away.

Nobody does that in a room where they don’t feel safe. And they won’t feel safe if the person leading the conversation doesn’t demonstrate genuine understanding of what they’re going through.

This is where the empathy gap gets expensive. Without empathy, you get compliance instead of engagement. Your team will attend the training, sit through the demos, nod along in the meetings. They’ll fill out the adoption surveys and tell you things are going fine. And behind the scenes, they’ll find ways to keep doing things the old way, work around the new tools, and quietly ensure that the AI initiative never quite delivers on its promises. I’ve watched this pattern play out in ERP implementations, process redesigns, and organizational restructurings for decades. The technology changes but the resistance pattern doesn’t, and it almost always traces back to a leadership team that managed the project competently without ever connecting with the people living inside it.

The more aggressive version looks like active sabotage – not dramatic, not obvious, just a steady drip of missed deadlines, unanswered questions, and convenient obstacles that slow the initiative until leadership loses patience and moves on to the next priority. But honestly, the passive version is worse, because it’s invisible until the results don’t materialize and nobody can explain why.

And then there’s the version that hurts the most: the good people who leave. The ones who are smart enough to see where things are headed, capable enough to land somewhere else, and disengaged enough to decide it’s not worth staying to fight through a transition led by someone who doesn’t seem to understand what they’re going through. You don’t lose your worst people in these moments. You lose your best ones, quietly, and by the time you notice, the damage is done.

Put Yourself Through Something Unfamiliar

So here’s my advice, and I’ll admit it sounds a little unconventional for a business article: go find your motorcycle ride.

I don’t mean literally (though I’d recommend it). I mean find something that puts you genuinely out of your depth – not a leadership retreat, not a team-building exercise, not something curated to be safely challenging. Find something where you don’t have pattern recognition to fall back on. Where you can’t manage the situation because you don’t know enough to manage it. Where the only option is to be a beginner and sit with the discomfort of not knowing what comes next.

For some people, that’s learning to play a musical instrument (bagpipes!). For others, it’s training for a physical challenge that’s well beyond their current ability (half-marathon!). It could be learning to use the AI tools yourself before you ask your team to – and I mean really learning, not watching a demo and nodding along, but sitting down with a blank prompt and trying to make something useful happen without anyone showing you how²If you want to practice the hard conversations about AI transformation before having them with your team, that’s one of the things JazzAI was built for. The specifics don’t matter nearly as much as the experience of being bad at something that matters to you.

What you’re doing is refreshing your emotional memory. You’re reminding yourself what it feels like to watch other people do easily what you’re struggling with, to wonder if you’re ever going to get it, to sit with the gap between where you are and where you want to be. That feeling doesn’t go away just because you’ve read about it or because you remember having it once. It fades, and it needs to be renewed, because empathy isn’t a concept you can store – it’s a muscle that atrophies without use.

When I got back from that motorcycle trip, I was a different kind of leader. Not because I’d learned some grand lesson about change management, but because the feelings were fresh. I could sit in a room with a team that was nervous about an upcoming system change and actually feel what they were feeling, not just understand it intellectually. I was more patient with the questions that seemed obvious. I was more attuned to the body language of someone who was struggling but wouldn’t say so. I was better at creating space for people to admit what they didn’t know, because I’d just spent three days admitting it to myself.

That’s not soft-skills fluff. That’s the difference between a team that goes through the motions of adoption and a team that actually engages with the change. And with AI, where the stakes are higher and the timeline is compressed, that difference determines whether your investment pays off or joins the long list of technology initiatives that looked great in the demo and disappeared into organizational indifference.

Where This Goes Next

So far in this series, we’ve covered a lot of ground that isn’t about technology. Start with the change, not the tool. Understand the gap between people who know their jobs and people who truly understand them. And now, lead with empathy for what the transition actually feels like from the inside.

These are mindset pieces, and I make no apology for that. You can have the best AI strategy in the world, but if you haven’t thought about the people who have to live inside the change, the strategy is just a document. The Building Blocks framework gives you a map for where change needs to happen across your business – Operations, Customer Connections, Product Intelligence, Data Mastery, and Team Dynamics. But the map is only useful if your team trusts you enough to follow it.

In the next article, we’ll shift from mindset to action. Specifically, how do you get your team to think strategically about AI – not just react to it, not just learn the tools, but actually develop the kind of thinking that connects technology to business outcomes? That’s a skill most organizations never explicitly teach, and it’s the bridge between understanding the change and actually making it happen.

If you’re working through how to lead AI into your business and want practical frameworks – not vendor hype – join our mailing list for the rest of this series and more.