For the first time in business history, we are witnessing a dramatic shift: regular employees are adopting new AI technology faster than the companies they work for. This isn’t just another technology that needs a quick fix—it fundamentally changes how organizations approach technology adoption. Traditionally, new technology flowed from top leadership down to employees. Now, it’s moving from everyday workers up to leadership. The center of change has shifted from the boardroom to employee chat channels and personal accounts.
Only 9 percent of companies report being fully prepared culturally for AI integration—a figure that inspires approximately the same confidence as a paper umbrella in a hurricane.
“Individuals—human beings both in and outside of business—are adopting AI quicker than can be embraced at the enterprise level. As leaders, we’ve realized we’ve got a vulnerability here.”
—Toby Boudreaux, Global Vice President of Data Engineering at Publicis Sapient
So how does the C-suite lead change management when adoption speeds have already left organizational readiness in the dust?
When you look at your organization’s data systems, it’s like exploring layers of history. You’ll find old mainframe computers running COBOL, middle-aged client-server systems, and newer cloud systems. Each layer represents a different era in your company’s technology history, containing both valuable information and difficult challenges.
While you’ve been building official security systems and rules, your employees may have started their own unofficial AI revolution. Your security team might be using free AI tools to detect threats. Your help desk might solve problems using AI assistants that aren’t officially approved. This isn’t just people bringing their own devices to work—it’s people bringing their own AI. This creates much bigger risks than just connecting personal phones to the company network.
“Many organizations face a digital archaeology challenge, with valuable data fragmented across generations of systems, from mainframes to cloud platforms.”
—Sheldon Montiero, EVP and Chief Product Officer at Publicis Sapient
Traditional data governance with strict rules and perfect data requirements doesn’t work well in the AI era. Smart CIOs know that waiting for “perfect data” before starting with AI is like refusing to build a house until there’s no dust at the construction site.
Instead of creating rules that mainly restrict, build frameworks that help people use data properly. Create different quality levels for different types of data with clear guidelines, use automated systems to find problems early, and form teams where risk managers and innovators work together instead of against each other.
When a retail company put AI interfaces on top of their old inventory systems, they weren’t just fixing old technology. They were extending the life of their existing systems while preparing for future replacements. AI can serve both as a temporary solution and as the final goal.
The most valuable AI projects aren’t always the exciting ones that get media attention. Often, they’re the ones that make old systems work better when they can’t be replaced immediately. Find where data moves between old and new systems, then use AI to help these different systems communicate better.
The help desk worker who used to reset passwords now designs conversations for AI chatbots that handle these tasks automatically. This isn’t just adding technology to existing jobs—it’s transforming roles. Technical teams move from doing repetitive tasks to recognizing patterns and handling special cases.
This change requires looking at current service tasks not just for efficiency but for automation possibilities. Identify which patterns AI can handle reliably and retrain technical teams to supervise rather than do the work directly. This creates both faster solutions and more interesting human work focused on complex judgments.
“Legacy IT infrastructure is so massive and so embedded in large organizations over decades… you cannot just uproot it and throw it away. This has huge change management ramifications because you’re essentially changing everything about how the organization functions under the hood.”
—Bilal Zaidi, Senior Director at Publicis Sapient
One global manufacturing company successfully created a balanced AI model. They combined central platforms for company-wide capabilities with team-specific resources. They discovered that innovation works best not with complete freedom or strict control, but with a balance between these extremes.
This balanced approach means creating shared platforms for common needs (like document processing or conversational interfaces), setting up safe testing environments with appropriate rules for team-specific needs, and maintaining just enough consistency to enable teamwork without stifling experimentation.
The biggest change for CIOs may be in their mindset—moving from controlling infrastructure to orchestrating experiences across many different AI capabilities. Instead of approving every tool, you establish safe boundaries within which teams can experiment. Instead of managing each deployment, you monitor usage patterns to identify potential risks or opportunities.
This transition means creating self-service AI resource catalogs with easy-to-use portals, simplifying approval processes to remove unnecessary barriers, and implementing monitoring systems that can detect risks without creating restrictions that push innovation underground.
Bottom line: Your biggest job isn’t just adding AI to your current systems but rebuilding your technology to support both the AI tools you approve and the AI tools your employees are already using without permission.
The executive suite now faces a profound choice: attempt to control a revolution already in progress or become its most thoughtful enablers, creating frameworks that channel its energy rather than contain it.
The C-suite’s value lies both in a decent understanding of AI capabilities (which will continuously evolve beyond any static comprehension) as well as in creating the organizational conditions where both humans and machines can continuously learn together.
What connects all successful AI transformations is humility—the recognition that no leader, regardless of title, fully comprehends the end state toward which we’re collectively evolving. The organizations that thrive won’t be those with the most advanced AI strategies on paper, but those that have reconstructed themselves, in difficult ways, to adapt continuously as AI capabilities expand in directions we cannot yet imagine.
The question isn’t whether your organization will transform—it’s whether that transformation will happen coherently, with intentional guidance from the C-suite, or haphazardly through a thousand unconnected adaptations.
The AI revolution won’t wait for your carefully orchestrated change management plan. It’s already happening, with or without your permission.