THE KEY ANSWER
Adoption grows when the tool helps complete a specific task within an existing process, and the user understands its limitations. This requires exercises on real-world examples, easy error reporting, and time to change habits.
First, check where the value is disappearing
Talk to people who tried the tool but didn't come back. Ask them to show you their last task. The reason might be inconvenient logging in, the need to copy data, or results that are too long. "People don't like change" is a convenient diagnosis that often bypasses the actual product problem.
Look at the final result. If the user has to manually re-edit everything, the savings shown in the presentation disappear. If they don't understand what data generated the answer, they will spend extra time verifying it. Note the obstacles and assign them to the interface, quality, process, or communication. Each group requires a different intervention.
Context and references: Stack Overflow Developer Survey 2025: AI
Train on tasks that will come back tomorrow
Training on dozens of features rarely helps with Monday's queue of issues. Choose two or three daily tasks and perform them together with the team. Show a good result, an error, and how to fix it. The user should know when to trust the result, when to check the source, and when to abandon automation.
Prepare short instructions close to the point of work. A hint next to the form is better than a lengthy document that has to be searched for. Ensure the possibility of safe testing without contacting the client. A person learning the tool should not have to worry about accidentally sending an unapproved message or modifying the wrong order.
Don't measure success by the number of logins
Logging in indicates entry into the application, not value. Observe completed tasks, utilization of results, review time, and returns to manual processing. Also ask how users utilize the time saved. A high number of generated responses may indicate that failed attempts are constantly being repeated.
Look at differences between roles. A beginner may need an explanation of the rules, while an expert needs a quick preview of changes. Do not impose an identical path on both groups. Together, determine which actions are mandatory due to the process and where the user can choose their own way of working. Forcing the use of a tool is not proof of its usefulness.
Build a feedback loop
Designate a person to collect feedback and show what was done with it. A report should include the task context and the reason for the problem, without unnecessarily disclosing data. Regularly revisit recurring errors. If the team reports the same problem for a month, another training session is unlikely to rebuild trust.
Communicate changes in terms of effect: "now the price source is visible" or "you can correct a single field." Allow time for implementation and include it in the work plan. Discuss openly how tasks will change. Do not promise arbitrary productivity increases and do not shift responsibility for system limitations onto users.
WHERE TO START
Bring this into your project.
- Observe the task from start to finish.
- Practice with examples appropriate for the specific role.
- Measure utilized results and necessary corrections.
- Show users what changes resulted from their feedback.
Choose one thing your process is missing today. It's a useful topic for your first conversation with the team.
QUESTIONS AND ANSWERS
Frequently asked questions.
Do you need to train everyone at the same time?
No. You can start with a small group representing different levels of experience. It is important that the pilot does not include only technology enthusiasts, as their behavior may not reflect the rest of the team.
What to do when experts prefer the current process?
Check their reasons on a real task. An expert may notice the cost of control that is invisible in simple metrics. Adapt the tool to their needs and compare the results, rather than treating mere reluctance as a problem to be eliminated.
Sources and context
- Stack Overflow Developer Survey 2025: AI ↗
The Stack Overflow survey separates the use of AI from trust in its results. A survey of developers should not be treated as a measure of adoption in every industry.
Prepared by the ALGOV team. Current as of September 8, 2026. Examples describe possible scenarios, not results from client projects. How we create our guides.
