6 Lessons for Successful AI Adoption
6 Lessons for Successful AI Adoption
Rolling out AI—like Microsoft Copilot—isn’t a system upgrade; it’s a mindset shift and an enterprise behavior change. After helping a large utilities company introduce a Microsoft Copilot chatbot to 25,000 employees, transformation and change leader, Eleanor Roe, distilled a repeatable AI playbook.
1. Think in Use Cases, Not Features
When people can see how technology applies to their day-to-day work, adoption and proficiency skyrocket.
Instead of talking about what Copilot can do, show people what it does with real examples pulled from their world. AI is only as powerful as the use cases and work cases you design for it, so we mined employee interactions and ticket data to surface the top 25 use cases people faced most often, then turned them into a living “see-yourself-in-it” library.
It helped people see the chatbot as a conversational partner, not just a tech feature.
2. Build Peer-to-Peer Champions and Advocacy
Top-down communication is important, but peer influence can be equally and sometimes more effective in driving behavior change.
Roe focused on empowering mid-level managers and early adopters to be AI champions. They modeled what “good” looked like: sharing feedback, helping colleagues troubleshoot, and spreading enthusiasm in their teams.
3. Make it Experimental and Everyday
AI tools shouldn’t feel like a separate system. They should become part of how work gets done and the new way of working.
Normalize daily experimentation: “try it, play with it, see what it can do for you.” Over time, that normalizes AI as a regular part of work.
Think of Copilot and chatbots as coworkers - use it for first drafts, summaries, search, and intake triage.
4. Build the Change Muscle
As consultants, our goal isn’t just to manage this change, it’s to help organizations handle the next one even better. That means building the internal change muscle: standing up change networks, embedding continuous learning and feedback loops, and giving employees ownership of the process. Change shouldn’t feel like something being done to people, it should feel like something they’re driving.
Codify a simple scale/kill decision cadence to avoid “pilot purgatory,” and publish a lightweight playbook so teams can rinse-and-repeat.
5. Set Realistic Expectations
AI adoption is a behavioral journey, not a one-time rollout. It takes time to build comfort and confidence.
In Prosci ADKAR terms, invest disproportionately in Desire (leader-modeled use, job-impact clarity) and Knowledge (micro-learning, role-specific prompts, demos, side-by-side practice).
Frame it as an ongoing learning journey, not a go-live event. Encourage leaders to celebrate small wins, spotlight success stories, and track adoption over time.
6. Measure and Reinforce
Just like any change effort, what gets measured gets maintained. Keep a close eye on adoption, celebrate the small wins loudly, and make sure momentum doesn’t fade after launch.
Tie every initiative to measurable business outcomes; if ROI isn’t showing, sunset it and redirect energy.
Final Thoughts
AI-driven change is unlike any other. It’s ambiguous, fast-moving, and deeply personal. But the fundamentals of good change management still apply, we just have to adapt them.
Start with real use cases. Build champions. Encourage experimentation. And above all, help people see AI as an opportunity, not a threat.
Because at the end of the day, the success of AI in any organization doesn’t depend on algorithms—it depends on people.
Contact ChangeStaffing for support with AI change readiness and adoption within your organization.
Thank you to Eleanor Roe for her thought leadership and for collaborating with us on this blog.
Written by Kylette Harrison