Your employees are using AI, but how well are they using it? A usage dashboard can show who's logging in, and a confidence survey can tell you how comfortable people feel. Neither gives you a clear picture of whether employees can apply AI effectively, evaluate its outputs, or make sound decisions about when to use it.
That's where an AI fluency assessment can help. By evaluating employees' applied knowledge and judgment, organizations can identify capability gaps and make more informed decisions about training.
But assessments vary considerably in what they measure and how they report results. This guide explains what to look for, which questions to ask providers, and how to turn assessment findings into a practical training plan.
What to look for in an AI fluency assessment
An effective AI fluency assessment should help you understand what employees know about AI, how they apply that knowledge, and where they need additional support.
When evaluating an AI fluency assessment, look for three things:
- Applied knowledge and judgment: Does the assessment evaluate how employees approach common AI tasks, assess outputs, and make responsible-use decisions, rather than simply testing their familiarity with AI concepts or vocabulary?
- Meaningful results across your workforce: Does the assessment reveal common strengths and skill gaps, so you can prioritize training? Look for a separate safety check that identifies whether employees meet foundational AI safety expectations, rather than relying on an overall fluency score alone.
- A clear path from assessment to training: Will the findings help you prioritize what employees need to learn and tailor training accordingly?
The goal isn't to produce a score for its own sake. It's to establish a baseline of AI capabilities that helps you make better decisions about employee development.
The following nine questions will help you evaluate whether an assessment can deliver that.
9 questions to ask when evaluating an AI fluency assessment
1. Does the assessment measure what employees know about AI or how they use it?
Knowing AI concepts isn't the same as understanding how to apply them. Look for an assessment that evaluates employees' judgment in practical situations, not just their familiarity with terminology. Can they recognize when AI is appropriate for a task? Do they know how to evaluate an output or identify when something needs further review?
Ask to see examples of the questions and what they're designed to measure. The goal is to understand how employees approach AI decisions, not simply whether they've used an AI tool before.
2. Are the questions relevant to how employees work?
An assessment doesn't need to recreate every employee's job, but its questions should reflect decisions people encounter when using AI at work.
Ask whether the assessment covers practical situations, such as selecting an appropriate task for AI, providing useful context, or evaluating a response. Find out whether it tests knowledge through multiple-choice questions, open-ended responses, or a combination of approaches.
3. How are employees' skills evaluated?
A score is only helpful if you understand what it represents. Ask how the provider evaluates responses and what distinguishes one proficiency level from another.
For example, does a higher score indicate that someone can recognize an inaccurate AI response, explain how to verify it, or apply that judgment to a more complex situation? The provider should be able to explain its scoring approach and show what different results mean.
4. Does the assessment evaluate responsible AI use?
Knowing how to use AI effectively also means knowing when and how to use it responsibly.
Look for questions that test employees' judgment around data privacy, output accuracy, and appropriate human oversight. Rather than checking whether employees can recall a policy, the assessment should help reveal whether they understand how to apply responsible-use principles to everyday decisions.
5. Can it assess employees across different roles and skill levels?
An organization-wide assessment needs to work for employees with different responsibilities and levels of AI experience.
Ask whether the assessment provides a common baseline across functions and seniority levels, and how the provider interprets differences in results. You should be able to identify where employees need support without assuming that technical expertise automatically translates to stronger AI fluency.
6. What will we learn about our organization as a whole?
Individual scores are useful, but leaders also need to understand patterns across teams.
Ask what the organization-level report includes. Can you identify common strengths and capability gaps? Can you compare results across functions or groups to understand where training should be prioritized? Does the provider offer benchmarks that show how your organization's AI fluency compares with others?
The findings should give you a clearer picture of your workforce's AI capabilities, not just an overall average score.
7. Will the results tell us what training employees need?
An assessment should help you decide what to do next.
Ask the provider how results translate into training recommendations. For example, employees who struggle to evaluate AI outputs may need more practice with verification, while employees who understand the fundamentals may be ready to build repeatable workflows.
The goal is to use the findings to tailor training to employees' needs rather than giving everyone the same program.
8. Can we measure improvement over time?
An initial assessment establishes a starting point. To understand whether employees are developing stronger capabilities, you'll need a way to evaluate progress.
Ask whether employees can be reassessed after training and how results can be compared over time. Find out what changes in scores would indicate and how the provider recommends evaluating whether new skills are being applied at work.
9. How will we know whether stronger AI fluency is improving work?
Higher assessment scores are encouraging, but they're not the same as better business outcomes.
Ask how the provider recommends connecting assessment findings to changes in employees' work. Depending on your goals, that might include improvements in output quality, more effective workflows, or greater confidence in handling complex AI-assisted tasks.
The assessment doesn't need to measure every business outcome itself. It should help you identify which capabilities to develop and where to look for evidence that training is making a difference.
How to turn AI fluency assessment results into better training
An AI fluency assessment is most valuable when the results inform what employees learn next. Rather than giving everyone the same training, organizations can use the findings to identify shared capability gaps and tailor learning to different teams and skill levels.
Match training to employees' needs
If employees struggle to evaluate AI-generated information, training can focus on verification and critical thinking. If they understand the fundamentals but have difficulty identifying opportunities to use AI, they may benefit from hands-on practice with their own workflows.
More experienced employees may be ready to build repeatable workflows or agents that improve how their teams work.
Identify potential AI champions
Beyond skills, an assessment can also reveal employees' mindset toward AI. This can help organizations identify potential AI champions: people who are motivated to experiment, share what they're learning, and support colleagues.
Combined with fluency results, mindset data can help leaders identify employees who could help others build their AI capabilities.
Look beyond training
Assessment results can also reveal where training alone won't solve the problem. Employees may understand how to use AI but lack access to approved tools, clear guidance on what's allowed, or opportunities to apply their skills.
Those findings can help leaders identify where changes to policies, tools, or team practices are needed alongside training.
Measure progress over time
Once employees have had time to apply what they've learned, organizations can reassess their capabilities and look for changes in how work gets done. That might mean stronger output quality, more effective workflows, or improvements in relevant team metrics.
Assessment scores show whether capabilities are developing; evidence from everyday work helps determine whether those capabilities are making a difference.
How Clarinet's AI Fluency Assessment works
Clarinet's AI Fluency Assessment helps organizations understand how effectively, safely, and strategically employees work with AI.
The assessment takes approximately 20 minutes and uses 25 questions to evaluate applied AI knowledge and judgment. Rather than relying solely on self-reported confidence or usage frequency, it examines how employees approach practical AI decisions.
Alongside fluency results, the assessment includes a mindset score and a separate Safety Check. Together, these help organizations understand employees' capabilities, identify potential AI champions, and see where additional safety training may be needed.
The assessment is designed to work across roles, seniority levels, and AI platforms, giving leaders a shared view of employee capabilities. Results can help organizations identify common strengths and gaps, understand where different groups need support, and prioritize training accordingly.
At Clarinet, we use these findings to help organizations make more informed decisions about AI enablement. The assessment can provide a starting point for customized training, with the goal of helping employees build skills they can apply in their daily work.
Explore Clarinet's AI Fluency Assessment.
FAQs
How long should an AI fluency assessment take?
Give employees enough time to respond to realistic work scenarios without making the assessment burdensome. Clarinet’s AI Fluency Assessment takes about 20 minutes.
Do technical and non-technical employees need different benchmarks?
A shared set of questions can establish whether everyone recognizes privacy risks and checks AI output. Technical roles may also need separate tasks that reflect their work. Ask how a provider separates role-specific skill from the company-wide baseline.
What should “safety” cover?
Look for questions about protecting sensitive information, checking claims before using them, and knowing when a person must review or make a decision. Ask what a strong answer looks like in a scenario relevant to your employees.
How often should you reassess?
Reassess after employees have completed training and had time to apply it at work. Choose the interval based on when you can reasonably expect behavior to change, and compare results alongside work quality rather than treating a higher score as the outcome.
How is an AI fluency assessment different from a knowledge quiz?
A knowledge quiz checks whether employees understand AI concepts or can recall rules. An AI fluency assessment should go further by evaluating how employees apply that knowledge to practical decisions. Ask providers what capabilities their assessment measures and how the results help identify training priorities.
