Interview an AI adoption leader through a small piece of the work they will actually own. Give candidates the same relevant materials and clear conditions, ask them to explain their choices, and record evidence against criteria agreed before the interview. A confident conversation about AI strategy is useful context, but it does not show how someone checks an output or helps a colleague use it.
This approach fits a company of 100 or more employees hiring an internal adoption lead who will support everyday work. Start by agreeing what the AI adoption manager will own. A role responsible for model engineering needs a different exercise from one responsible for practice, communication and coordination.
Make the task, permitted help and evaluation criteria clear before asking someone to demonstrate their skill. The interview should reveal job-relevant judgment without making the candidate guess what the panel wants.
Choose one responsibility to examine
Write down a recurring responsibility the person must handle on entry. For an adoption lead, that might be helping account managers check AI-generated customer summaries before an internal handover. Define the finished output and the decisions the lead can make, then choose a small task that exposes those decisions.
The U.S. Office of Personnel Management's work-sample guidance describes exercises that mirror actual job activities. It also sets a useful limit: work samples suit competencies expected on entry and may be inappropriate for activities that will be taught after selection. This is general assessment guidance, rather than a validated interview for an AI adoption role.
If your company will teach its chosen tool during onboarding, do not silently make familiarity with that tool the hiring test. Supply a short orientation, or use a prepared output that lets candidates demonstrate transferable checking and coaching skills. The AI enablement skills guide helps distinguish capabilities from software names.
Before inviting candidates, ask someone who knows the work to try the task. Check whether the materials contain enough context, whether the output can be examined in the time allowed, and whether the criteria describe observable actions. Revise a task that mainly rewards knowing your company's shorthand.
Tell candidates what the exercise requires
A February 2026 Reddit discussion shows how unclear expectations can complicate a practical assessment. A Reddit user who described themselves as a web developer said a take-home task asked them to identify their own work and AI-generated work. They assumed a balance mattered, then reported receiving feedback that other submissions had delivered more functionality through AI. Replies disagreed about quality and quantity, while one urged asking the employer what it expected.
The employer's reasoning was not independently verified, and a software-engineering assignment differs from an adoption interview. The useful warning is the uncertainty itself. Disclosure of AI use does not tell a candidate which output or reasoning the panel will value.
Send a short exercise brief covering:
- Purpose and output. Name the responsibility being examined and what candidates should produce or explain.
- Materials and access. Provide the same synthetic or approved anonymized inputs and any required tool access. Avoid making candidates buy a subscription.
- Permitted help. Explain whether AI, reference material and clarification questions are allowed, and how candidates should describe that help.
- Time and preparation. State the expected preparation and exercise time, and provide a contact for discussing access needs or adjustments.
- Criteria and limits. Explain what the panel will observe and which company knowledge candidates are not expected to have.
Use a simulated task with a bounded output. Asking someone to produce your company-wide rollout plan adds unpaid production work and makes the assessment harder to compare. If you need a larger assignment, reconsider its scope and discuss the commitment with the candidate before they begin.
Observe a check and a coaching conversation
Suppose a commercial printing company asks Jamie Lee, a candidate for its adoption lead role, to help account managers review customer-order summaries. Jamie receives three short customer notes, the current approved job sheet and a prepared AI summary. The job sheet records the specifications authorized for production.
One customer note requests a different paper finish; another asks for extra copies. Neither change has yet been approved. The summary presents both as settled instructions. Jamie's task is to prepare a short internal handover that distinguishes approved specifications from requests needing confirmation, then explain the check to an interviewer acting as an account manager.
The packet includes a short explanation of the company's approval rule. Candidates do not need prior knowledge of printing operations to understand that a request is different from an authorized specification. No live customer data or production system is involved.
Use a simple sequence, with timing set after you have tried the task yourself:
A bounded practical interview
Read and clarify
Review the supplied packet and ask about missing context.
Check and explain
Compare the summary with the sources and identify what can enter the handover.
Teach one check
Help the interviewer trace one summary statement to its supporting record.
Discuss a complication
Explain the response if a colleague finds a mismatch after the handover.
Keep the evidence
Retain the candidate's output and the panel's specific observations.
Notice how Jamie handles uncertainty. Do they identify the two requested changes, preserve the approved specification and ask who can confirm the changes? Can they teach a repeatable check without taking over the colleague's whole task? The useful observation is the reasoning and action, not how polished the handover looks.
Avoid changing the difficulty halfway through because one candidate seems confident. Use the same core complication and agreed clarification rules. Record any additional help you supplied so the panel can interpret the resulting evidence.
Ask consistent questions about prior work
The exercise gives you a narrow observation. Follow it with questions about work the candidate has already done, especially responsibilities that the simulation cannot reveal. OPM's structured-interview guidance recommends predetermined questions in the same order and common standards for assessing answers about job-related behavior.
For an internal adoption role, useful prompts include:
- Describe a specific occasion when a colleague could not use the method you taught. What did you change, and what happened afterward?
- Tell us about an output you rejected or corrected. What source or check informed that decision?
- Describe a disagreement about introducing a tool. What was your responsibility, what did others decide, and what remained unresolved?
Ask for the situation, the candidate's own actions and the outcome. A consultant describing a client's program should clarify what they personally contributed. An internal champion should have room to describe relevant work without having held a formal AI title.
Keep the same core questions and use clarification to understand an answer. Do not reward a detailed story merely because it names familiar tools. Ask what changed in the work and what evidence the candidate used to reach that conclusion.
Compare observations before discussing impressions
Give each panel member time to record their own evaluation before discussing the candidate. The OPM practical guide, published in September 2008, describes independent panel evaluations supported by behavioral examples, followed by discussion of differences. The principle is useful here: an opinion should have a traceable basis in the interview.
Use criteria specific to the responsibility you chose. For the printing exercise, an observation sheet might look like this:
| Criterion | Evidence to record | Follow-up if evidence is incomplete |
|---|---|---|
Separate requests from approvals | Which source Jamie used and which changes remained unresolved | Ask Jamie to trace one disputed instruction |
Explain a usable check | How the colleague was helped to verify a summary line | Ask the interviewer to try the check back |
Respect decision boundaries | Who Jamie said must confirm the changed specifications | Clarify the supplied approval rule |
Respond to a mismatch | The proposed correction and who would be informed | Ask what must happen before the handover is reused |
This sheet is a starting point for designing your interview. Agree what acceptable evidence looks like, try the exercise and refine it with people who know the job. It is not a validated score or a universal hiring threshold.
Separate a demonstrated gap from evidence you never obtained. A failed tool connection, ambiguous packet or interrupted session can prevent observation. Record the condition and arrange a proportionate follow-up rather than converting missing evidence into a confident judgment. If panel members disagree, compare the behavior each saw and the criterion each applied.
The hiring decision still belongs within your organization's full selection process, including the role's other requirements and relevant experience. One successful simulation does not establish that a person can sustain adoption across departments.
Your next step is to prepare one task packet and one observation sheet, then test them with a colleague. Fix the conditions before asking candidates to work under them.
Questions about interviewing AI adoption leaders
Should an AI adoption leader have to pass a coding test?
An AI adoption leader needs a coding test only when coding is a real responsibility required on entry. An internal lead hired to teach colleagues, check outputs and coordinate practice needs an exercise that examines that work. Define the role's responsibilities before borrowing a technical interview from an engineering role.
Should candidates be allowed to use AI in the exercise?
Allow AI when using it is part of the responsibility you are examining, and explain that rule in advance. Give candidates consistent access and ask them to describe what they accepted, changed or rejected. If tool access would distract from the judgment being assessed, provide a prepared AI output to review. The panel should understand the candidate's contribution and checks in either format.
How long should an interview work sample take?
There is no universal duration for an AI adoption work sample. Choose a small task, try it with a colleague and set a time expectation that permits the required reading, checking and explanation. Tell candidates about preparation beforehand. A short supplied summary exercise is easier to bound than a request for a complete company rollout plan; discuss adjustments or a larger commitment explicitly.
What should the panel do when it cannot assess a criterion?
An interview panel should record what was not observed and why, then decide what focused follow-up would supply the missing evidence. If an unclear approval rule prevented the candidate from resolving a print-order summary, clarify that rule and revisit the relevant decision. Keep this separate from a task failure demonstrated under clear conditions. The observation sheet provides a place to plan that follow-up.
