To distinguish inability, reluctance and lack of opportunity in AI adoption, examine a specific task and the conditions in which someone tried it. Look separately at what they can do, what their working environment permits, and why they judge the use worthwhile or unwelcome. Low usage alone cannot tell you which explanation fits.
For a manager investigating employee resistance to AI, the useful outcome is a supported explanation of the obstacle. “Sam cannot check the draft without help” points toward practice. “Sam can check it, but customer notes are not approved for this tool” points toward a permission decision. Both may be true.
The people and change guide explains how to arrange support. This article focuses on the evidence you need before choosing it.
Define the task before judging the person
“Inability” is too broad unless you say what someone cannot yet do. Opening a chatbot, supplying useful context and noticing an invented commitment are different skills. Someone can be confident with the first two and struggle with the third.
The COM-B framework distinguishes capability, opportunity and motivation. Capability includes knowledge and skills; opportunity concerns external conditions; motivation includes habits, feelings and conscious judgments. These interact. Susan Michie and colleagues developed the framework through a synthesis of behavior-change frameworks, not an enterprise AI trial. Use the original COM-B paper as a lens for questions, rather than a test that assigns employees to three groups.
Suppose Sam Taylor coordinates appointments at a regional maintenance company. The proposed use is to draft a customer follow-up from a technician's job note approved for the tool. The message should explain what work was completed and the agreed next step, without inventing another repair or appointment.
That task gives the conversation clear boundaries:
- The input is the technician's note, in a form permitted for the tool.
- The output is a follow-up message Sam can check before sending.
- The quality check compares completed work and next steps with the note.
- The observation includes preparation, checking and corrections, not just generating a draft.
Ask about this work before discussing enthusiasm for AI in general. A person's judgment about customer messages need not predict their judgment about another use.
Compare what each signal could mean
Treat the first explanation as something to investigate. An unanswered training invitation could reflect workload, prior knowledge or a belief that the class will not help. It does not establish an inability to learn.
A small qualitative study illustrates the ambiguity. Riya Sahni and Lydia Chilton interviewed ten experienced Microsoft 365 Copilot users. Several reported bypassing formal training while learning through experimentation or colleagues; time pressure also mattered. These were selected active users, not a representative sample of employees avoiding AI. The finding cautions against equating training attendance with capability. The February 2025 preprint, sections 4.3 and 4.4.
For Sam's follow-up task, compare the observation with a plausible alternative before deciding what it means.
| What you observe | Possible explanation | What still needs checking |
|---|---|---|
Sam misses an appointment the draft invented. | A gap in checking this kind of output. | Was the source note clear and available? Does Sam know the required standard? |
Sam completes a practice message with a colleague. | The task may be achievable with support. | Did the colleague choose the input, suggest corrections or do the difficult checking? |
Sam can check a practice message but avoids real customer notes. | Permission or access may be unresolved. | Is the relevant data actually approved for this tool and account? |
Sam returns to the existing message template. | The familiar process may be easier or more useful. | How much preparation and correction did the AI attempt require? |
Sam declines another trial after seeing an error. | There may be a concern about reliability or consequences. | What happened, and what evidence would make another attempt worth considering? |
The same observation can support more than one explanation. Record the blocked step and the evidence, rather than describing someone as incapable or resistant.
Watch a work sample without taking over
Use a permitted practice note and explain what you are trying to learn together. A supported session should help locate the difficulty, not quietly become a demonstration of the helper's skill.
In Sam's session, follow the task far enough to see the judgment it requires:
- Ask Sam to explain the intended message. Establish what the customer needs to know and which details must come from the technician's note.
- Observe preparation and checking. Notice whether Sam can supply permitted context, compare the draft with the note and leave missing information unresolved.
- Make help visible. If a colleague spots the invented appointment or supplies a better instruction, record that intervention. Do not count it as independent performance.
- Ask what differs in the normal shift. A quiet practice session may provide time, accessible records and immediate help that everyday work does not.
If Sam cannot identify the unsupported appointment even with a clear note and an explained standard, targeted checking practice is a reasonable next step. If Sam identifies it immediately but cannot use the real notes, more checking practice will not resolve permission.
A single session is still limited evidence. An unfamiliar example, an observer or a helpful prompt can change how someone performs. Look for repeatability on another appropriate task before making a broader claim about ability.
Take the reason for declining seriously
Reluctance can persist after a skills gap is resolved. Someone may understand the workflow and still object to its consequences, distrust its output or prefer a process that already works. Ask what the concern is about before deciding how to respond.
In a July 2026 r/careerguidance discussion, a Reddit user described managers repeatedly encouraging AI use. The user said an attempt to obtain statistics produced errors, requiring the work to be redone. In a follow-up comment, they said they discovered the problem on the day of the presentation. They also raised environmental concerns and wanted to be able to explain their own reasoning.
The account is unverified, and the employer's size is unknown. Its useful detail is that the objection had several parts: an unsuccessful attempt, a wish to understand the work, and a concern about using the technology. Fixing one would not necessarily answer the others.
For a comparable concern in your team, separate the questions that require different responses:
- An output problem needs examination of the input, result and checking effort. Another trial is useful only if something relevant can change.
- An unclear rule needs an answer from the person responsible for data use or workflow approval.
- A concern about workload or role changes needs an honest account of the organization's decisions and remaining uncertainty.
- A judgment that the task is better without AI needs a fair comparison against the required outcome, including rework.
Do not promise a better result or an unchanged role simply to obtain another attempt. Where the tool cannot meet the task's requirements, changing or ending that use can be the right response.
Check whether the explanation survives the next attempt
Once you have a plausible explanation, agree a change that addresses it and inspect what happens when the work recurs. Keep the conclusion proportionate to the evidence.
Suppose Sam now checks a practice follow-up independently. The manager has also clarified permission for the relevant notes and arranged time within the shift. If the next attempt works, you have evidence that Sam can complete the task under those combined conditions. You cannot tell from that result alone how much each change contributed.
You do not need to withhold necessary support to isolate a cause. Instead, record what changed and what remains uncertain. If only the checking practice has changed, a limited record would say: “Sam found the unsupported appointment without prompting on the second practice note. Permission for real customer notes remains unresolved.” That is more useful than “Sam is now confident with AI.”
At the next real opportunity, distinguish three outcomes:
- The obstacle changed. Sam could access the permitted note or complete the checking without help.
- The task improved or remained acceptable. The message met its standard, with preparation and correction effort included.
- The opportunity actually occurred. There was a relevant appointment follow-up to prepare during the observation period.
If no relevant job occurred, absence of use tells you little. If the practice happened only because other work was postponed, make that time arrangement explicit. For recurring-use reporting, distinguish activation, retention and abandonment before interpreting a quiet week as rejection.
The aim is an explanation you can revise as the work changes. A person may need practice today, access tomorrow and a better reason to continue after seeing the results.
Questions about AI adoption barriers
Does low AI usage mean an employee is resistant?
Low AI usage does not establish resistance. The employee may lack permission, time, a relevant task or the skills to check the output. They may also have tried the tool and judged that it made the work worse. Ask about a specific recent opportunity and inspect any resulting work before choosing a response.
For an appointment coordinator, a week without relevant follow-ups is different from a week of rejected drafts. Use the evidence comparison table to keep those explanations separate.
How can a manager distinguish a skills gap from lack of opportunity?
Examine a permitted task with clear inputs and an agreed quality standard, noting exactly where help is needed. Then compare the session's conditions with ordinary work. Someone who can check a practice message independently but lacks approved access to real customer notes has an unresolved opportunity problem; someone who cannot recognize an invented appointment may also need checking practice.
One successful session is not proof of routine ability. Review how to observe a work sample, then look for another appropriate attempt with the necessary conditions in place.
Can inability, reluctance and lack of opportunity occur together?
Yes. An employee may need help checking an AI output, lack time to practise and worry about what happens when the tool makes a mistake. These conditions can reinforce one another. Avoid forcing the person into a single category.
Record each supported obstacle, who can address it and what will change. If several conditions change together, report the combined result without claiming that one intervention caused it. The follow-up guidance explains how to keep that conclusion limited and useful.
When is declining an AI task a reasonable outcome?
Declining an AI use can be reasonable when the inputs are not permitted, the output cannot meet the required standard, or preparation and correction outweigh its usefulness. A concern about consequences also deserves an answer, even when the person can operate the tool.
Inspect the task and the stated reason together. Resolve a missing permission or test a specific improvement where appropriate; do not keep repeating an unchanged trial that fails the same requirement. Separate output problems, rules and concerns so the next response addresses the actual issue.
