Employee resistance and change fatigue are cited as reasons why AI initiatives stall, but Julie A. Stone argues that explanation doesn’t tell the whole story.
According to Stone, Group Vice President and Chief Learning Officer at TTEC, the issue is rarely that employees are resistant to change itself. More often, they are responding to unclear priorities, disconnected initiatives, inconsistent leadership, and being asked to change how they work without understanding why it matters or how it connects to outcomes they care about. That distinction is important because the diagnosis shapes the response. Organizations that treat AI adoption as primarily a tech challenge with a mandate to use it everywhere are getting surprised by low engagement, uneven adoption, and underwhelming business impact.
Julie A. Stone, Group Vice President and Chief Learning Officer at TTEC, has led workforce transformation and AI adoption across The Hartford, Prudential Financial, eBay, and TTEC over two decades. “AI transformation isn’t primarily a tech challenge,” Stone states. “Like all others, it’s an organizational capability challenge.”
Align Before You Accelerate
Most organizations launch AI initiatives before they have answered the questions that determine whether those initiatives can succeed. What business outcomes is AI supposed to impact? How do we expect AI to change the work? Speed it up, reduce handoffs, or improve accuracy? How will we know if it is working? Are all leaders aligned on what success looks like? Stone is clear about what happens when those answers are not in place before the initiative launches. “If those answers aren’t clear, more tech isn’t going to give you results.”
To help leaders assess whether their organizations are truly prepared, Stone developed the CLOSE System—a model built around five interconnected variables: Culture, Leadership, Operating Model, Strategy, and Enablement. Together, caring for these elements determine what Stone calls change velocity: an organization’s ability to absorb, adapt to, and continuously improve through change.
Design for Front-Line Value, Not Executive Vision
Executive enthusiasm is necessary but not sufficient to produce workforce adoption. People change how they work when doing so makes their work demonstrably better, not because leadership is excited about a new platform or because a communications campaign explained the strategy. “People don’t change because leadership is excited about it,” Stone observes. “They change when it helps them do their work better.”
Achieving adoption requires both how you will communicate it and how you expect employees to change how they work. It needs to be useful for the employees who have to live inside it every day. When workers see immediate value in their daily tasks, adoption follows without the need for sustained pressure from above. When they don’t, adoption becomes a compliance exercise that eventually fades.
Building AI initiatives around front-line value requires understanding, at a granular level, where the friction points and bottlenecks exist in current work before deployment begins. That understanding is not always available upfront, which leads directly to the third capability that separates organizations that scale AI from those that stall.
Treat Unexpected Signals as Evidence, Not Failure
Work redesign in the age of AI cannot be fully predicted before it begins. Hidden process steps that nobody documented, adjacent processes from other teams that must also change, new skills employees need that were never anticipated – these are not signs that the initiative is failing. They are signals about what needs to change next. Stone’s framing reorients how leaders should read the inevitable surprises of AI deployment. “These aren’t failures. They’re signals that tell you how you need to change next.”
A successful AI implementation doesn’t need a perfect roadmap from day one. It requires a culture that learns, adapts, and redesigns work as new information emerges. That capacity, what Stone calls change velocity, is the real competitive advantage in the age of AI. The goal is not adoption. It is better work. Keeping that distinction clear and building an AI strategy around it are the keys to sustaining momentum long after the initial initiative launches.
Follow Julie A. Stone on LinkedIn for more insights on AI adoption, workforce transformation, and building the organizational capability that turns technology investment into lasting business performance.
