How to help people adopt AI at work

AI change management helps people make a useful, supported change in how they work. For an adoption lead or team manager, that starts with understanding a recent attempt: what someone tried, what happened, and what would make the next attempt worthwhile.

A person who cannot judge an AI-generated answer needs different support from someone who can judge it but has no time to practise. Someone who has tried the tool and found the result worse deserves a conversation about the task itself. If an earlier rollout broke a promise, rebuild trust with commitments employees can inspect.

If you already have a technology change plan, review which practices carry over and what needs adapting for AI. Keep useful support arrangements while revisiting output checks and ownership after launch.

This guide focuses on those conversations and the decisions that follow. If your organization is still choosing which work AI should support, start with the enterprise AI adoption strategy guide.

Start with the last attempt

“Why aren't people using AI?” invites a broad opinion. “Show me the last time you tried it for this task” gives you something to investigate together.

Ask the employee to walk through the work, using an approved example that can be shared safely. Follow the attempt far enough to see the checking and handoff, then ask:

1. What were you trying to finish? Establish the task and what a good result would have looked like.
2. Where did the attempt become difficult? Look for missing access, confusing instructions, poor output, extra checking, or an interrupted practice session.
3. What did you do next? Returning to the old process may reveal a reasonable workaround.
4. What would need to change before you tried again? Separate something the employee can learn from something another person must resolve.

If they have never tried, ask what has prevented a first attempt. Do not invent a skills gap from a missing login.

In a public discussion in Reddit's r/Training, one participant gave a direct reason for holding back.

That response leaves several possibilities open. The task might be unsuitable. The person might need a better example or a way to check the result. The existing process might already be efficient. Ask to examine the work before deciding which explanation fits.

Choose support that fits the obstacle

The COM-B framework offers a useful lens for this conversation: behavior depends on capability, opportunity, and motivation. Capability includes knowledge and skills; opportunity concerns the conditions around someone; motivation includes habits and feelings as well as deliberate intentions. Susan Michie and colleagues developed the framework by synthesizing behavior-change frameworks. It was not a trial of enterprise AI adoption. The original COM-B paper.

Use those categories to broaden your questions. They are not labels for employees, and several conditions can matter at once. Someone may understand a task well, lack approved access to the necessary information, and worry about being judged for asking for help.

Consider a furniture retailer's customer-service team drafting replies about delayed deliveries. These illustrative comments connect a recognizable obstacle to an investigation. Confirm the explanation with the employee before choosing an action.

When the explanation is unclear, compare the evidence for a skills gap, reluctance or missing opportunity before choosing support. A successful coached attempt and low routine usage can tell different stories.

When the obstacle sits upstream of employee practice, use COM-B to examine the person arranging training, advice or approvals. Follow the enabling action through to what colleagues receive.

A useful intervention changes the condition that is getting in the way. A workshop can help someone learn to check an answer. It cannot grant missing access or settle an unresolved staffing decision.

Make practice possible in the working week

Manager support is associated with more frequent AI use, although that does not establish that encouragement alone causes adoption.

Make support visible in decisions employees can recognize. Agree which task they will practise, which approved material they can use, who can help, and what can wait while they learn. An invitation to experiment is hard to act on when all existing deadlines remain equally urgent.

In the same r/Training discussion, another Reddit user described a team that spends a few weeks each year researching tools individually, then teaching each other what they learned. The team decides together whether to change its process. The commenter also described needing to remind colleagues about tools they had forgotten.

That is one team's self-report, with no stated employer or headcount. The useful detail is the shared decision after exploration, followed by reminders when the work calls for the tool. Your team may need a different cadence.

Use the employee AI literacy baseline to choose what people should be able to explain and demonstrate. For a next practice session, make these arrangements explicit:

• A real task. Use something the participant expects to do again.
• A checked example. Include the inputs, the output, and the corrections that mattered.
• Available help. Name a person and a realistic way to reach them.
• Time within the workload. Agree what is being postponed or reduced and who can approve the change.
• A place to keep the learning. Save the useful example where colleagues can find it during the task.

For support spanning several teams, define what an AI adoption manager should deliver. Keep the role's coordination work distinct from the decisions that remain with managers, technical owners and sponsors.

For a recurring team review, use the weekly AI adoption routine for managers. It includes preparation, an agenda and follow-up around one real example.

For individual expectations, decide how AI use should count in performance reviews. Assess finished work, judgment and learning with the support conditions made explicit.

A champion can demonstrate a practice and help colleagues interpret an output. When choosing someone for that role, look for coaching and judgment alongside personal AI skill. Estimate protected time from the responsibilities involved and agree a route to specialist support. Problems requiring access, policy, or workload decisions still need the person with authority to resolve them.

To see whether that help travels beyond the champion, follow a practice into colleagues’ checked work and later reuse.

For staff without a personal desk or office hours, test AI access, practice and support on each shift. Include the device handover and off-hours help in that check.

Make concerns answerable

Some concerns involve the meaning of using AI at work. Will asking for help look like a lack of skill? Will time saved become a higher workload? What does the organization intend to do with usage data?

A field experiment by David Almog illustrates one reason to take evaluation concerns seriously. US freelancers completing an image-categorization task relied less on AI recommendations when their reliance was visible to an evaluator who could influence a contract extension. Their task accuracy also fell. The working paper recruited 450 workers; one withdrew their data. This was a short, specific task, not a study of sustained chatbot adoption inside a company. Almog's paper, version dated August 24, 2026.

The result does not tell you what your employees believe. It gives you a reason to ask how the surrounding incentives affect an attempt.

Managers should be able to explain:

• What good work means. Describe the output standard and the checking that remains the employee's responsibility.
• How use will be interpreted. Clarify what usage information is collected and how it actually informs decisions.
• What remains undecided. Name an unresolved question, its decision owner, and when an answer will be revisited.
• How concerns reach that owner. Offer a route that does not depend on raising a sensitive question in a public demonstration.

Avoid assurances you cannot make. If the future of a role is undecided, promising that nothing will change undermines the conversation. Record the uncertainty and get the responsible leader to address it.

Leave with a small action record

A good conversation should end with a change someone can make and a way to find out whether it helped. Keep the record about the work and the unresolved condition. Personal concerns do not all belong in a shared team document.

Copy these fields into a note:

• Task: What recurring work are we discussing?
• Observed obstacle: What happened in the recent attempt?
• Change: What will be different next time?
• Owner: Who can make that change, and by when?
• Next attempt: When will the task recur, and what support will be available?
• Review: What will we inspect before deciding to keep, adjust, or stop?

Choose the review point around the next meaningful repetition. A daily task and a monthly task need different observation windows. Review the employee's account alongside the finished output and the effort needed to check it.

Look for a specific change: access now works, the person can identify errors independently, or the agreed practice time actually happened. Then consider whether the finished task improved. The strategy guide's measurement section explains how to include checking and rework.

Keep what helps, adjust what remains awkward, and stop a use that cannot meet the task's needs. Share the useful learning with the next person facing the same condition.

Questions about helping people adopt AI

What is AI change management?

AI change management is the work of helping people make and sustain a useful change in how they use AI at work. It includes explaining the purpose, understanding concerns, arranging time and support, and acting on what employees learn from real tasks. The change is only useful if the resulting work meets its required standard; completing training or buying licenses does not establish that.

Begin with a recurring task and a recent attempt. Agree what needs to change, who can make that change, and what you will inspect the next time the task occurs. Use the six-field action record to turn the conversation into a practical commitment.

Why do employees resist AI adoption?

Employees may hold back from AI because they lack approved access, time to practise, the skills to check an output, or confidence that it will help their work. They may also have reasonable concerns about performance evaluation or future workload. Nonuse alone does not tell you which explanation applies, and several conditions can matter at once.

Ask the employee to walk through their last attempt, including checking and any work passed to someone else. For example, a furniture retailer's service adviser who rejects a draft containing an unconfirmed delivery date may be protecting the customer from a false promise. Inspect that problem before prescribing encouragement or more training. The obstacle and support table helps identify who can act on what you find.

Is AI training enough?

AI training can address a knowledge or skill gap, such as learning how to check a generated reply against its source records. It cannot by itself grant missing permissions, make an unsuitable output useful, or change conflicting priorities. If employees understand the tool but cannot get the information or time they need, another class leaves those conditions unresolved.

Identify the specific obstacle, assign it to someone with authority to change it, and review the next attempt after that change. The retail purchasing example shows why an earlier handoff of warehouse notes can matter more than another training session. When workload is the obstacle, use the manager's guide to making time for AI learning to agree what moves or receives support.

How can managers support AI adoption?

Managers can support AI adoption by agreeing a useful task, making practice time available within the workload, and ensuring someone can help assess the result. Explain the quality standard and how AI use will be interpreted. Give employees a way to raise concerns, and be clear about which questions remain undecided rather than making promises you cannot keep.

At the next meaningful repetition of the task, review the output, checking effort and employee's experience together. Resolve the decisions you own and take access, policy or other dependencies to the responsible person. The weekly AI adoption routine for managers provides a practical agenda and follow-up structure; the practice arrangements in this guide explain what to set up beforehand.

How do you know the support worked?

Check both whether the original obstacle changed and whether the employee can repeat the task with an acceptable result. If the problem was missing delivery notes, establish that the notes arrived in time, then inspect the resulting summary and the effort needed to check it. Resolving access or making practice possible is progress, but it does not by itself prove that AI improved the finished work.

Compare similar tasks using the same quality standard, including checking, corrections and effort passed to other people. Use the complete-task measurement guidance to avoid counting only a faster draft. Higher usage alone is insufficient evidence, and stopping a use that cannot meet the task's needs can be a sound outcome. Record whether to keep, adjust or stop at the agreed review point.