Change Management
There’s a pattern to how organizations respond when disruptive technology becomes openly available to everyone at once, and it plays out the same way every time. Employees experiment first. Leadership notices later. Then comes the push for control, and that’s precisely where things go wrong.
The instinct to bring order to something that feels unpredictable is understandable, but the way that instinct typically gets expressed inside organizations is through restriction and process, and that’s where it backfires. When employees sense that experimentation is being watched and evaluated rather than supported, they don’t stop experimenting. They stop reporting. The behavior doesn’t go away. It just goes quiet. Leadership interprets the silence as compliance and assumes the situation is under control, when in reality the organization is adapting in ways that are completely invisible to the people responsible for guiding it. That’s the illusion of control, and it’s far more dangerous than the uncertainty it was meant to resolve.
Today, leaders are no longer being judged solely on the performance they deliver. They’re increasingly being judged on whether their organizations can interpret and respond to what’s emerging in real time. Getting this right matters not just for navigating what’s changing today but for building organizations that can keep pace with a world where the rate of change is only going to accelerate.
Getting this wrong has never been more costly, because falling behind used to be recoverable. Increasingly, it isn’t.
What Past Technology Adoption Cycles Teach Us About AI
I’ve seen this dynamic before, long before AI. In the early days of social media, the consequences of getting it wrong were significant and in many cases lasting:
- Confidential information made its way into public posts
- Internal conflicts played out in front of customers, competitors, and the press
- Employees spoke on behalf of their companies without authorization, creating legal and reputational exposure that leadership had no framework to manage
- Competitors who paid attention could piece together product roadmaps, hiring plans, and strategic priorities from what employees were sharing openly
- Top talent gravitated toward organizations that trusted them with new tools and away from those that responded with restriction and suspicion
- Employee advocacy, which turned out to be one of the most powerful and credible marketing channels available, was entirely forfeited by companies that shut social media down rather than learning how to work with it
- Customers were having conversations about these brands, whether the companies participated or not, and the organizations that chose not to engage lost the ability to shape their own narrative
- When customer complaints went unanswered publicly, the reputational damage compounded in ways that were very difficult to reverse
- Companies that moved too slowly lost ground that took years to recover, while early movers built audiences, customer relationships, and brand equity that became genuine competitive advantages
It all started the same way. Employees began experimenting on their own. They tested what worked, shared ideas informally, and found ways to make their work more effective. Leadership entered later, once the impact became visible, and then made the mistake of trying to control what they should have been trying to understand.
The more leadership tried to control social media from the top, the less they understood how it was actually being used. Many companies went further and tried to ban it outright, blocking access on company devices and prohibiting its use during work hours. It didn’t work. Employees used their personal phones. They posted on their lunch breaks. They found workarounds because the tools were useful, and usefulness always wins. The most valuable insights were coming from the edges of the organization, but those insights were often missed or filtered out because they didn’t align with the initial plan.
Adoption followed usefulness, not hierarchy, and the organizations that ignored that reality ended up playing catch-up for years. The same dynamic is already well underway with AI. Employees are using it to draft emails, build presentations, write and review code, research competitors, summarize documents, and shortcut processes that used to take days. They’re doing it on personal devices, through free tools, and often without any awareness of what data they’re feeding into those systems. The organizations that respond with restrictions aren’t slowing any of that down. They’re just making sure they’re the last to know about it.
The Problem With Trying to Govern What You Don’t Yet Understand
Eventually, leadership notices, concern sets in, and the instinct is to introduce structure through governance, policies, and training. That instinct isn’t wrong. The problem is the timing and the assumption behind it. Training has its place, but with AI it’s fighting a losing battle from the start. The technology is evolving faster than any curriculum can track, and the range of ways people are applying it across different roles, industries, and workflows is too vast for any training program to anticipate. Training can only teach what’s already known, and with AI the most important discoveries are still being made. The early stages of experimentation are where real use cases actually emerge. That process often feels messy, but it’s where genuine understanding is formed, where the impact on workflows, decisions, and roles begins to take shape, and where the organization starts to build a real picture of what AI means in practice, not in theory. People don’t learn new things by being handed a rulebook. They learn by trying, failing, adjusting, and trying again. That’s exactly why experimentation isn’t optional. It’s the only mechanism that actually works, and it’s already happening inside your organization, whether leadership has blessed it or not.
The breakdown happens when leadership steps in and tries to bring order too quickly. By the time governance begins, behavior is already forming. People have already decided how the technology fits into their work, and workflows have already started to shift. When leaders respond by narrowing the focus to consistency and control, they often reduce their own visibility into what’s actually happening.
Here is what that looks like in practice. A policy gets issued restricting which AI tools employees can use. A governance committee forms to approve use cases. Reporting requirements are introduced. On paper, the organization looks like it’s managing AI responsibly. In reality, employees have already moved on. They’re using tools that weren’t on the approved list, solving problems in ways that never made it into a report, and making workflow decisions that leadership has no line of sight into. The organization isn’t in more control. It just thinks it is, and that gap between perception and reality is exactly where competitive advantage quietly walks out the door.
The More You Control, the Less You See
When governance tightens, the signals that matter most start disappearing. The employee who figured out how to cut a three-hour process down to twenty minutes using an AI tool they found on their own stops mentioning it because it wasn’t on the approved list. The team that has been quietly testing a new way to handle customer inquiries stops sharing results because nobody asked and it didn’t go through the right channel. The manager who noticed that half the department is using AI to draft reports stays quiet because the last conversation about AI ended with a policy memo. Conversations become more cautious, experimentation moves out of view, and the organization starts to look aligned on the surface while something very different is happening underneath.
This is where top-down change breaks. The model assumes a level of stability and predictability that no longer exists. It assumes there’s enough time to understand, plan, and then execute in sequence. In reality, organizations are operating inside continuous change. Technology is evolving, workflows are shifting, and decisions are being made in real-time across the business. You’re not managing a single transformation. You’re operating within a system that’s constantly adapting.
The Answer Is an Adaptable Culture
The only way forward is to build a culture that’s designed to adapt. An adaptable culture isn’t reactive. It’s intentional. It’s a culture that’s always learning, always experimenting, and always adjusting as new information emerges. It creates the conditions for people to surface what’s working and what isn’t, and it allows those insights to move across the organization before decisions are locked in.
This is where trust becomes essential. If people don’t feel comfortable sharing what they’re trying, what’s working, or where something is breaking down, leadership loses access to the most important signals in the system. Without those signals, decisions are made on partial information, and the gap between strategy and execution widens over time.
Integration Is What Makes It Work
Systems create structure and consistency, and they’re still necessary. What’s changing is the need for those systems to be integrated. Integrated systems connect people, workflows, decisions, and technology, but they don’t stop at the edges of the organization. They also connect leadership to how customers are changing, how competition is shifting, how the market is responding, and how the broader landscape is evolving in real time.
They allow insights to move across the organization instead of staying trapped within individual teams, and they keep the organization oriented to what’s happening outside it just as much as what’s happening within it. When those connections are working, something important happens. Opportunities that would have gone unnoticed begin to surface. Threats that would have caught the organization off guard become visible early enough to act on. Innovation stops being something that has to be forced through a process and starts emerging naturally from people who are close to the work and connected to what’s changing around them. They keep the feedback loop intact so that signals lead to awareness, awareness leads to adjustment, and adjustment leads to better outcomes. Control breaks that loop. Integration strengthens it.
Leaders who want to navigate this well need to focus on understanding before control. That means creating visibility into where change is already happening, listening to how people are using new tools, and identifying what’s actually working inside the business. From there, they can connect insights, align teams, and design processes that reflect reality rather than assumptions.
The Companies That Win Will Be the Ones That Adapt
The pattern of adoption won’t change. New technologies will continue to emerge, employees will continue to experiment, and leadership will continue to engage once the impact becomes visible. The companies that come out ahead won’t be the ones that try to control this pattern. They’ll be the ones who understand it while it’s still forming and shape it with intention. In a world where the future is less predictable, the most valuable organizations won’t be defined by how stable they are today. They’ll be defined by how well they can adapt tomorrow.
If you’re navigating AI adoption and something feels off, it’s worth asking whether the problem is actually the technology or whether it’s a lack of visibility into what’s really happening inside your organization. That’s the question I’ve worked through with leadership teams for the past 2 decades. We look at where information is flowing, where it’s getting stuck, and what it would take to build a system that learns and adapts as change unfolds rather than one that keeps trying to catch up to it.
If that conversation is one you’re ready to have, I’d like to hear what you’re seeing inside your organization right now. Share it in the comments or reach out directly. The most important work always starts with an honest look at what’s actually there.
