For the first time in business history, we are witnessing a fundamental 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 completely changes how companies adopt innovation. In the past, new technology moved from top leaders down to workers. Now, it moves 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?
The COO’s arena is where AI can bring some of its most clear benefits—but also where people may resist change the most.
“There’s often resistance to AI-driven change. It’s very natural because AI drives fear, essentially fear of job loss or redundancies because there’s so much automation happening.”
—Bilal Zaidi, Senior Director at Publicis Sapient
The old idea that change management comes at the end of transformation is wrong. We often think people just need good instructions to accept change. The truth is that change management must be part of transformation from the beginning, like the tempo set for the entire orchestra.
This means including the human side of change when redesigning processes:
When teams redesign work processes without considering how people will adapt, they make a serious mistake.
Companies often dream of quick transformation—replacing old systems with new AI solutions overnight. But this ignores how people actually handle change. Real change requires careful planning: 90-day cycles of gradual improvement rather than sudden overhauls, test teams with different perspectives and attitudes, and ways to gather feedback about how people feel, not just about technical results.
Success stories serve as proof that others have safely tried new ways of working. Each story helps reduce the fear of those considering similar changes, making new approaches seem less strange over time.
Operational leaders often have a strong attachment to traditional measurements—the familiar numbers presented in quarterly meetings. These old measurements can actually block real transformation. Traditional productivity measures show how well old processes work, not how new capabilities are developing.
The real return on investment isn’t just about lower costs or higher revenue. It’s about whether your organization can respond to market changes in weeks instead of months, creating advantages that grow stronger over time.
A better approach measures:
The biggest challenge for operational leaders is creating service delivery that is neither all-human nor all-machine, but a partnership where each does what they do best. This means more than just reassigning tasks—it means rethinking work itself. Identify what machines do efficiently and what humans do with unique judgment, then design systems where these abilities strengthen each other.
This partnership requires operational and technology leaders to work closely together. The COO and CIO become architects of evolution, making sure that data systems and operational processes develop together rather than separately.
Bottom line: When implementing AI in operations, focusing only on the technology while ignoring how people feel about the changes will create perfect systems that your teams will quietly refuse to use.
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.