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Making AI tools available is only the first step. They deliver value when employees know how to use them, are willing to try them, and can apply them to everyday work. For leaders asking, “What does success look like for our employees?”, the answer should focus on capability and work outcomes—not just who has logged in.
Why access to AI does not guarantee adoption
A tool can be deployed without becoming part of how people work. Angela Stopper and Belle Walker draw a distinction between rollout—making tools available and accessible—and adoption, which requires employees to have the capability and willingness to use them. Integration is a further step: AI becomes part of routine work.
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This distinction helps explain why access alone is a weak measure of progress. Employees may not know which tasks suit AI, may lack the skills to use it effectively, or may be unsure whether experimenting is acceptable. Stopper and Walker summarize their practical framework as “access, ability, and appetite.” It is a useful way to diagnose an initiative, not a validated universal model or proof that any one factor causes success.
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Access: can employees reach the tools?
Check whether the relevant employees can actually use the AI tools available to them. Access is a prerequisite, but activations and logins show availability or activity—not whether AI is improving work.
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Ability: can employees use AI effectively?
Define observable expectations for proficiency, then identify the skills needed in the work areas most likely to change. The expectations should fit the tasks and roles involved; Stopper and Walker do not offer a universal benchmark. Work with technical experts to find knowledge gaps and build learning pathways tailored to function and seniority rather than assuming one training session suits everyone.
Appetite: are employees willing to use it?
Willingness is shaped by more than individual enthusiasm. Employees need room to experiment, opportunities to learn from peers, and signals from managers that thoughtful use is welcome. Invite employees into planning and listen to their concerns instead of treating adoption as a message to be delivered after the decision is made.
Measure whether AI is changing the work
Pair usage information with measures tied to the work itself. Stopper and Walker recommend looking at efficiency, quality, and error rates, alongside clear expectations for what employees should be able to do. Choose measures that fit the work being changed and compare them over time; the authors do not prescribe standard targets or report a controlled test showing a particular improvement.
| What to compare | What it tells you |
|---|---|
| Logins and activations | Whether employees are accessing or activating tools; this does not establish effective use. |
| Efficiency, quality, and error rates | Whether the work is changing in outcomes that matter to the organization. |
Start with work areas expected to change most, and make proficiency expectations concrete enough that managers and employees can recognize progress. Keep the measures connected to the task: a high login count cannot stand in for better-quality output or fewer errors.
Build learning around roles and real work
Effective learning should connect AI skills to the decisions and tasks employees face, not just explain features. Stopper and Walker recommend identifying knowledge gaps with technical experts and tailoring learning by function and seniority.
- Find early adopters at different levels of the organization and help them share practical examples with colleagues.
- Support peer learning with train-the-trainer help, manager role-modeling, and incentives where appropriate.
- Include individual contributors in planning. The authors anticipate that, as agentic AI enters more functions, breaking work into tasks and deciding what to delegate to an AI agent will become useful skills.
- Provide safe opportunities to experiment, so employees can learn what works in their context.
Make change a shared effort
Training cannot substitute for employee involvement or a workplace that supports the behaviors leaders want. The authors recommend listening to employees, educating them, co-creating approaches, and reinforcing desired behaviors through varied forms of recognition and incentives. Managers also influence whether employees see AI use as an expected part of work or as a risky side project.
Stopper and Walker illustrate this with an organization’s performance-evaluation redesign, which they say began eight years before their article and took two years. Their account describes gathering employee groups, listening, educating, and co-creating a system that retained goal accomplishment while adding job mastery, collaboration, continuous improvement, and belonging and inclusion. They say this groundwork preceded a formal technology launch and characterize the culture change as successful. It is an author-reported example, not an independently evaluated or controlled AI deployment.
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AI tools change quickly, so Stopper and Walker caution against assuming a complete, static governance framework will remain suitable. Organizations still need clear expectations for employees, but should revisit policies as tools and uses evolve. That makes ongoing communication and learning part of implementation rather than a one-time pre-launch exercise.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical diagnostic for an AI rollout
- Identify the work to change. Choose the functions and tasks where AI is expected to affect day-to-day work.
- Check access and readiness separately. Confirm who can use the tools, what skills those roles need, and what might discourage employees from trying them.
- Set observable proficiency expectations. Define what effective use looks like for each relevant role without imposing a generic benchmark.
- Build role-aware learning. Work with technical experts and employees to address gaps by function and seniority; use early adopters to share visible examples.
- Track work outcomes. Review appropriate measures of efficiency, quality, and errors alongside tool activity.
- Adjust the environment. Invite employee feedback, make experimentation safe, recognize desired behaviors, and update guidance as tools evolve.
The authors’ central point is that AI adoption is a people and work-design challenge as much as a technology rollout. As they put it, “Adopting artificial intelligence is not about the tools; it’s about your people.”
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