A practical guide for evaluating AI proficiency.

Assessing AI skills requires different methods than assessing traditional knowledge. Proficiency is about judgment and output quality, not memorization or tool familiarity. This guide covers practical considerations for evaluators: HR professionals, hiring managers, trainers, and team leads.

1. Define the purpose before choosing a method

Assessment methods should match your goal. Hiring for an AI-intensive role requires demonstrated proficiency through work samples or tested scenarios. Gauging training needs for an existing team may only need self-assessment plus spot-checking. Certifying external candidates demands standardized grading and verification.

Be clear about what the result will be used for. A pass/fail screening decision needs different rigor than a development conversation.

2. Choose practical tasks over trivia

Multiple-choice questions about model parameters or release timelines test memory, not skill. Practical tasks — “revise this email using AI,” “research this topic and summarize key findings,” “identify what is wrong with this AI-generated report” — reveal how someone actually works.

Use scenarios that mirror real job tasks. A support role should be assessed on customer communication tasks; a data analyst on research and synthesis; a marketer on content creation and editing. Generic scenarios work when role fit does not matter, but role-specific tasks produce more honest signals.

3. Allow AI use during the assessment

Forbidding AI during an AI proficiency assessment tests the wrong skill. Real AI work involves USING the tools — knowing when to trust output, how to refine prompts, and when to stop and do the task manually instead.

Grade the quality of finished work and the judgment behind it, not whether someone can complete the task unaided. If you ban AI use, you measure pre-AI capability, not AI proficiency.

4. Use rubrics, not impressions

Consistent grading requires clear criteria written down before reading answers. A rubric states what foundational, developing, and strong responses look like for each task. Without one, grading drifts between candidates and whoever reads first sets an invisible bar for everyone else.

Emeri publishes its band definitions and competency descriptions; adapt them or write your own. The AI Skills Matrix maps behaviors to proficiency levels as a starting reference.

5. Calibrate for role, not just skill level

A marketing professional and a software engineer face different AI tasks daily. Assessing both with engineering-focused scenarios underrates the marketer; using marketing scenarios does the reverse. Either calibrate questions to each role or use generic scenarios and accept that relevance varies.

Role calibration does not mean lowering the bar — it means asking fair questions. An E3 grade in marketing and an E3 in engineering should both represent distinguished proficiency, measured through role-appropriate tasks.

6. Handle privacy and consent carefully

If assessment involves submitting text to AI tools, ensure data privacy. Do not require candidates to paste confidential work into public AI services. Use enterprise AI accounts with data protection agreements, or provide sanitized sample data for scenarios.

Be transparent about how results will be used. A private development assessment is different from a hiring screen that becomes part of someone’s employment record. Informed consent matters.

7. Interpret results within limits

An AI proficiency assessment shows how someone worked on the tasks you gave them, on the day they took it. It does not predict every future scenario, measure domain expertise, or assess overall job performance. A high score means they demonstrated strong AI proficiency in that context — not that they will excel at every task forever.

Do not over-index on a single number. A six-competency profile (like Emeri provides) shows where someone is strong and where they need support. Use results as one input among many.

8. Reassess carefully, not frequently

AI proficiency develops with practice, but reassessing every month creates assessment fatigue and teaches people to game the test instead of learning the skill. Reassess when there is a reason: after targeted training, when someone moves to a more AI-intensive role, or if initial results were borderline and you want to confirm progress.

Frequent retakes also risk invalidating results if questions leak. If reassessment is planned, ensure fresh scenarios or accept that familiarity inflates scores.

9. Consider using a standardized assessment

Building an assessment that aims for fair, consistent decisions requires writing scenarios, developing rubrics, piloting questions, and monitoring grading across people and cohorts. Organizations should weigh that ongoing work against using an existing assessment.

Standardized assessments like Emeri handle grading, calibration, and verification so you can focus on interpreting results and acting on them. The methodology and framework are published in full, so you know what is being measured and how.

Related resources

The AI Skills Gap Analysis page describes how to use assessment results to identify capability gaps and prioritize training. The Teams page covers options for assessing groups.