Here is a phrase that needs to be retired: "resistant to change." We throw it around dismissively, as if skepticism in the face of new technology is a character flaw. But what if it’s not? What if resistance is the predictable, rational response to uncertainty—and understanding that pattern is the real competitive advantage?
I’ve spent time deconstructing historical technology cycles—electricity, PCs, the internet, cloud computing. The pattern that emerged wasn't about people being backward-thinking. It was far more interesting: smart people were making rational decisions based on incomplete information, and they were systematically wrong about the exact same thing.
Clifford Stoll was a brilliant man. In 1995, he famously wrote: "How come my local mall does more business in an afternoon than the entire internet handles in a month?"
He wasn't wrong about the internet in 1995. It was slow, clunky, and objectively terrible for commerce. His mistake was assuming the trajectory was linear—that marginal improvements would eventually make it viable. He couldn't conceive of exponential improvement.
This mistake repeats across every transformative shift. When BlackBerry executives defended their physical keyboard against the iPhone, they weren't being stubborn. The iPhone's digital keyboard did suck in 2007. They focused on what the iPhone couldn’t do that year, missing entirely what it would do by 2009.
We are seeing this play out with AI today. "AI hallucinates, so it can't be trusted." That is true today. The error is assuming it stays true tomorrow.
Through studying these shifts, I’ve identified eight patterns that show up like laws of physics. They aren't bugs; they are features of how organizations work.
As a technology leader, your job isn't to "overcome" resistance—it's to bridge the gap between today’s reality and tomorrow’s trajectory. Here is how you lead through the noise:
1. Move from "chatbot" to "infrastructure"
Right now, the "mental model" for AI is a box that you chat with. Honestly? That's a toy, not a tool. To get your organization to take AI seriously, you must move the conversation toward agentic workflows and data pipelines. If you treat AI as a fancy search engine, your team will treat it as an optional perk. If you build it into the plumbing of your software delivery lifecycle or your customer support routing, it becomes inevitable.
2. Solve the "expertise" UX problem
Let’s be honest: AI is currently mystifying and not user-friendly. It requires "prompt engineering"—a temporary bandage for a UI gap. As a leader, you must abstract that complexity away. Your team shouldn't have to learn how to "talk" to a model; the model should be embedded in the tools they already use. If the UX requires a 10-page manual, you haven't solved the adoption problem; you've just created a new one.
3. Build "trust guards" before features
Your legal and security teams are the "organizational antibodies." Don't fight them; arm them. Instead of pitching the latest LLM feature, pitch the governance framework. Utilize the "sandbox" where people can fail safely. When you lead with data privacy, PII masking, and audit trails, you remove the rational reasons for "no."
Every technological innovation follows a four-phase cycle:
We are entering phase 2. The companies that understand this cycle and move decisively will own the next decade.
The temptation for smart leaders is to wait for the noise to settle—to wait for the hallucinations to hit zero and the standards to be written in stone.
But there is a fine line between calculated waiting and stagnation.
History shows us that by the time a technology feels "safe," the competitive advantage has already been harvested by those who moved during the uncertainty. The goal isn't to ignore your skepticism; it’s to use it as a compass. If you can identify exactly why your organization is resisting, you have found the roadmap for your next deployment.
So, let’s retire the phrase "resistant to change" and replace it with "rational observation."
When your team points out a flaw in the tech, listen to them—they are usually right about the present. But as a leader, your responsibility is to be right about the future. Your job is to respect the skepticism, solve for the trust gap, and build the mental models that turn "clunky tech" or "exciting new toys" into "essential infrastructure."
Stay rational. Stay smart. But whatever you do, don't stay complacent. The window of Phase 2 is open. Use your skepticism to build a better foundation than your competitors, but don't let it keep you on the sidelines. The advantage doesn't go to the loudest evangelist or the most stubborn skeptic—it goes to the leader who acknowledges the "keyboard" is missing and starts building the touchscreen anyway.