The short answer
AI fluency is the ability to use AI in your real work, regularly and with judgment. You know when to use it, you give it what it needs, you check what comes back and you own the result. AI literacy comes first. It means understanding what AI is, where it goes wrong and which rules apply. The framework I suggest has four levels (Literate, Experimenting, Fluent and Redesigning), with observable behaviors for employees, managers, senior leaders and HR. It uses the 4D competencies (Delegation, Description, Discernment and Diligence) as the skills to look for. Assess people with a short work sample, then move them up one level at a time on their own tasks. For most AI use, the EU AI Act asks organizations to support literacy, not fluency.
See the four levels- Literacy Understanding AI, its limits and the rules.
- Fluency Using AI in recurring work, with judgment.
- EU AI Act Measures that support literacy. No certificate.
The four-level model on this page is my own proposal. It builds on the 4D competencies by Rick Dakan, Joseph Feller and Anthropic, and on my work training leaders and teams. Facts checked 1 November 2026.
What is the difference between AI literacy and AI fluency?
AI literacy has a legal definition. The EU AI Act describes it as “skills, knowledge and understanding that allow providers, deployers and affected persons, taking into account their respective rights and obligations in the context of this Regulation, to make an informed deployment of AI systems, as well as to gain awareness about the opportunities and risks of AI and possible harm it can cause” (Article 3(56)).
AI fluency has no legal definition. The 4D framework, developed by professors Rick Dakan and Joseph Feller with Anthropic, describes it as interacting with AI “in ways that are effective, efficient, ethical and safe” (AI Fluency Framework).
My working definition is shorter. AI fluency is using AI in your real work, regularly and with judgment. Think of a foreign language. Literacy is being able to read the menu. Fluency is ordering, asking a follow-up question and noticing when the waiter brings the wrong dish.
| AI literacy | AI fluency | |
|---|---|---|
| Question it answers | Do you understand AI, its risks and the rules? | Do you use AI well in your own work? |
| Example | Knows not to paste a candidate’s CV into a public chatbot | Drafts interview questions from the role profile in the approved tool, then cuts the two that test the wrong thing |
| Built by | Training, reading and clear rules | Practice on real tasks, feedback and habits |
| EU AI Act | Organizations that use AI must take measures to support it | Not required |
What is the 4D AI fluency framework?
Search for an AI fluency framework and most of what you find is about the 4D framework. It names four competencies, and the levels further down use them.
- Delegation. Setting the goal and deciding whether, when and how to use AI. A recruiter lets AI draft interview questions and keeps the shortlist decision.
- Description. Describing the goal so the AI produces something useful. You give the role profile, the audience and a good earlier example, not one line.
- Discernment. Judging whether the output is any good. You check every figure in an AI summary against the report.
- Diligence. Taking responsibility for what you do with AI and how. You keep employee data out of tools that aren’t approved and stand behind the final text.
The 4Ds describe good AI use but not who needs to get how far. The four levels below add that.
An AI fluency framework in four levels
The framework I suggest is a ladder from literacy to fluency. Level 1 is knowledge, and a session can teach it. Levels 2 to 4 are about what people do with AI in their work, and that only changes with practice.
Literate
Understands what generative AI does, that it can be confidently wrong, which tools are approved and which data must never go into them. May not use AI much yet.
Sounds like “I know we’re allowed to. I haven’t found a use.”
Experimenting
Uses AI now and then for one-off tasks, like a draft email or a summary. Prompts are short, the first answer gets used or thrown away, and the results feel random.
Sounds like “Sometimes it’s brilliant, sometimes it’s useless.”
Fluent
Uses AI every week on at least three recurring tasks. Decides what to hand over and what to keep, gives context and examples, checks facts and figures before sharing, and knows when AI saved time and when it didn’t.
Sounds like “I start every monthly report this way, then I check the figures against the source.”
Redesigning
Changes how a recurring task works for the whole team. Builds shared prompts, assistants or agent workflows, documents them, measures the effect and teaches others. Also decides where AI should stay out.
Sounds like “This is how our team does it now, this is who checks what, and this is what we measure.”
People sit at different levels for different tasks, and level 4 isn’t the goal for everyone. I think most people get most of the value at level 3.
What does each level look like by role?
These are the behaviors I would look for. You can see them, which makes them easier to assess than attitudes.
| Level | Employee | Manager | Senior leader | HR |
|---|---|---|---|---|
| 1 Literate | Knows the approved tools, which data stays out and typical errors like made-up facts. | Can answer the team’s questions about what’s allowed. | Knows what the tools can do, the main risks and the legal frame. | Also knows the rules for personal data and AI in decisions about people. |
| 2 Experimenting | Has tried AI on a few tasks, mostly drafts and summaries. | Uses AI for notes and emails but doesn’t talk about it. | Uses AI now and then, or has someone else do it. | Uses AI for job ads and policy drafts. |
| 3 Fluent | Uses AI weekly on at least three recurring tasks and checks before sharing. | Uses AI to prepare one-on-ones and plans, keeps employee data out of unapproved tools and asks the team about AI. | Uses AI for analysis and decision papers, and says so openly. | Uses AI weekly in HR work and checks AI-written text about people for bias. |
| 4 Redesigning | Has rebuilt a team workflow with AI and taught colleagues. | Has changed a team process with the team and follows up the results. | Decides where AI should change the business, funds process redesign and asks for results. | Runs the system behind fluency, from target levels to assessment and AI champions. |
Which level should each role aim for?
- Everyone who uses AI at work. Level 1 as the floor. In my reading, it comes close to what the European Commission lists as the minimum to consider in an Article 4 program.
- Most knowledge workers. Level 3 on the tasks where AI helps.
- Managers and senior leaders. Level 3 in their own work, before anyone asks them to coach others. Teams copy what their managers do.
- HR and one or two people per team. Level 4 on the processes they own. These people become your AI champions, and the guide to AI enablement shows how to run a program for them.
Where do HR teams get stuck?
The AI & HR Barometern 2026 is a survey of 329 HR professionals in Sweden by humAIn Academy, which I co-run. The respondents chose to take part and are probably more interested in AI than HR as a whole, so the numbers describe them, not employees in general.
Personal use is high, but on its own it doesn’t change how the organization works. Even among daily users, 61 percent say their organization’s AI use in HR is still at individual tests or pilots, the same share as in the whole group. Only 33 percent say AI is a recurring part of at least one HR process, and 5 percent measure the effect with defined KPIs. My reading is that many have reached level 2 or 3 in their own work, while level 4 is still ahead for most organizations. That step needs a process owner, protected time and a before-and-after measure (see from fluent to redesigning).
Data from AI & HR Barometern 2026 (humAIn Academy, 8 June to 24 September 2026). The split for daily users and the 25 and 68 percent figures come from the raw responses. The rest are in State of AI in HR.
How do you put the framework to work?
Five steps, in this order.
- Set a target level per role. Start from the list above and adjust it to the tasks where AI helps in your organization. The data on which jobs AI is changing most shows where to look first.
- Take a baseline. Send the self-check to everyone and run the work sample with five to ten people per role.
- Pick one group. Choose the group with the biggest gap between target and baseline, and plan one level up for them.
- Measure again after three months. Run the work sample again, with the same task.
- Write down what you did. Use the record further down. In the EU it also documents your Article 4 measures.
How do you assess AI fluency?
Use at least two of the three methods below. Course completions and usage data only show attendance and activity.
A five-question self-check
| Question | Points to level 1 or 2 | Points to level 3 or 4 |
|---|---|---|
| How often did you use AI at work in the last two weeks? | Never, or a few times | Several times a week |
| Which task do you now always start with AI? | None yet | Names one or more, and why |
| What did you do the last time AI got something wrong? | Didn’t notice, or gave up | Checked the source and corrected it |
| What do you never put into an AI tool at work? | Not sure | A clear answer that matches your rules |
| Has AI changed how you or your team work? | No | Yes for me (3), or for the team (4) |
Count the answers that match the right-hand column. Zero or one points to level 1 or 2. Two to four points to level 2, on the way to 3. All five points to level 3, or level 4 if AI has changed how the whole team works. A “not sure” on the fourth question means level 1 isn’t in place yet, whatever the rest says.
Then confirm with the work sample, because confidence and skill aren’t the same thing. In the barometer, 68 percent rate their confidence in using AI in HR safely and effectively at 4 or 5 out of 5, while 25 percent have had formal training on law, risk or the AI Act.
A 30-minute work sample
Give the person the approved tool and a real, low-risk task from their own job. An employee could turn an email thread into an action list, a manager could prepare a one-on-one from last month’s notes, a senior leader could draft a decision paper from a report, and HR could write a job ad from a role description. Hide one detail in the material that shouldn’t go into the tool, like a made-up employee’s sick leave, to see how they handle data.
Allow five minutes for the brief and twenty for the task, with the person thinking aloud. In the last five minutes, ask what they kept from the AI and why, how they know the result is right, and what they would do differently. Then score the four competencies.
| Competency | Level 2 signs | Level 3 and up |
|---|---|---|
| Delegation (what goes to AI) | Hands over everything, or nothing | Keeps judgment calls and decisions about people |
| Description (how they instruct it) | One short line | Context, audience, an example and the format they want |
| Discernment (how they check) | Uses the first answer | Checks facts and figures against the source and rewrites weak parts |
| Diligence (data and responsibility) | Pastes in the sensitive detail | Keeps sensitive data out and stands behind the result |
Watch the checking closely. In Anthropic’s study of 9,830 Claude.ai conversations from January 2026, users visibly checked facts in 8.7 percent of them, and less often when Claude produced an artifact like a document or code (Anthropic, 2026). The sample likely skews toward early adopters and checks outside the chat aren’t counted, so treat it as a warning sign rather than a benchmark.
What managers can observe
In a one-on-one, ask “Show me the last thing you did with AI.” Follow up with “What did you check, and how?” and “Which part did you keep for yourself?” The answers show Discernment and Delegation without a test. Over a month the manager sees whether use grows and whether the team’s work changes.
How do you move people up a level?
One level at a time, on their own tasks. In my view a single training day can’t take someone from level 1 to level 4, because each step needs practice in between. If someone holds back because they worry about what AI means for their job, I don’t think more training is the answer. Start with the conversation guide in AI anxiety at work. If a whole team stalls, the ADKAR model in an AI rollout helps you find the step that’s missing, whether people don’t want to, don’t know how or can’t make it work on a busy day.
From literate to experimenting
- Give people an approved tool and one page of rules on which data stays out, like the one in the guide to shadow AI.
- Run a hands-on session where people bring their own tasks, and ask everyone to leave with three tasks to try in the coming week. The work-sample tasks above make good starters.
From experimenting to fluent
- Pick two or three recurring tasks per person and agree to use AI on them for four weeks.
- Teach the check. Compare facts and figures with the source, ask the AI what it is unsure of, and read the result as if a colleague wrote it.
- Collect prompts that work in a shared team document, with the context people added.
- Have managers ask about it in one-on-ones. The AI literacy playbook covers how to prepare managers for that role.
From fluent to redesigning
- Choose one recurring process with a clear owner, and give that person protected time.
- Map the steps. Decide which steps AI does, which a person checks and which stay human.
- Measure before and after, for example time spent, quality and how much has to be redone.
- Document the new way of working and teach it, for example with this four-week pilot plan.
Leaders go first. If the leadership team is at level 2, it’s hard to ask the rest of the organization for level 3. A hands-on AI workshop for the leadership team is one way to start there. For HR teams, the guide to AI upskilling for HR applies the same idea to HR tasks.
Does the EU AI Act require AI fluency?
No. If your organization uses AI in the EU, the AI Act requires measures that support AI literacy. The Digital Omnibus on AI, Regulation (EU) 2026/1744, rewrote Article 4 from 27 July 2026. Providers and deployers of AI systems now “shall take measures to support the development of AI literacy of their staff and other persons dealing with the operation and use of AI systems on their behalf”. The paragraph ends with “This obligation does not require providers or deployers to guarantee any specific level of AI literacy of any individual” (EUR-Lex).
- Everyday use counts. The European Commission says even staff who use ChatGPT to write advertising text or translate are covered (European Commission).
- Not only employees. The Commission says “other persons” can be contractors, service providers or clients, like a recruitment agency using AI on your behalf.
- Adapt to roles. Article 4 asks you to take people’s knowledge and experience into account, along with the context and the people the AI is used on. A recruiter who uses AI on candidates needs more than someone drafting internal emails.
- No certificate. An internal record of training and other initiatives is fine, according to the Commission.
Article 4 has applied since 2 February 2025, and according to the Commission the rules on supervision and enforcement apply from 2 August 2026. AI used in recruitment and other employment decisions is a separate question. Those systems usually fall under the high-risk rules (Annex III), which after the omnibus apply from 2 December 2027 (National Law Review), and the Commission notes that staff who oversee high-risk AI must be trained for it. This is my reading as a practitioner, so check with a lawyer before you rely on it. My AI governance framework covers who decides on high-risk systems like these and what the Act asks of you as a deployer.
A simple record of your measures
A level model makes Article 4 practical. Copy this format into a spreadsheet, with one row per role or group. The rows are examples.
| Role or group | AI tools | Target level | Measures | Date | Next review |
|---|---|---|---|---|---|
| Everyone with AI access | Approved chat assistant | 1 | Intro session on AI, typical errors and our data rules | Fill in | Yearly |
| Managers | Approved chat assistant | 3 | Hands-on session on management tasks | Fill in | After three months |
| Recruiters | AI features in the recruitment system | 3 | Training on candidate data, bias and human decisions | Fill in | Every six months |
| HR team | Chat assistant and HR system | 4 on two HR processes | Process redesign, measured before and after | Fill in | Quarterly |
| Contractors and agencies using our AI tools | The tools they use for us | 1 | Our data rules in the contract, plus the intro session | Fill in | When the contract is renewed |
How does this compare with Anthropic, Zapier and Microsoft?
| Framework | What it is | Best for |
|---|---|---|
| AI Fluency Framework (Dakan, Feller and Anthropic) | The 4Ds above, without levels. Taught in a free course and released under CC BY-NC-SA 4.0 | A shared language for good AI use |
| Zapier AI fluency rubric | Four levels (Unacceptable, Capable, Adoptive and Transformative). Capable has been the minimum for new hires since May 2025, and the March 2026 version raised what Capable requires | Hiring, and one bar for the whole company |
| Microsoft Learn, AI Fluency | Seven beginner modules, from AI basics and responsible AI to Microsoft Copilot, written with philanthropic work in mind | A literacy baseline for people new to AI |
| The ladder on this page | Four levels with behaviors for employees, managers, senior leaders and HR, plus assessment and a record for Article 4 | Targets per role, training plans and documentation |
I’d use the 4Ds as the vocabulary and a role-based ladder like this one to decide who needs to get where. If you want AI fluency in hiring, read Zapier’s rubric (Zapier, 2026), but go easy on a label like “Unacceptable” for current staff. Zapier itself says that level usually reflects limited exposure, uncertainty about expectations or valid concerns about risk, which is a job for management first.
Frequently asked questions
What is AI fluency?
AI fluency is the ability to use AI in your real work, regularly and with judgment. A fluent person knows when to use AI, gives it the context it needs, checks what comes back and owns the result. It builds on AI literacy.
What is an AI fluency framework?
An AI fluency framework describes what good use of AI looks like, so an organization can set targets, assess people and plan training. A common one is the 4D framework by Rick Dakan, Joseph Feller and Anthropic, which names four competencies. The framework on this page adds four levels (Literate, Experimenting, Fluent and Redesigning), with observable behaviors for employees, managers, senior leaders and HR.
How do you measure AI fluency in employees?
Combine at least two methods. A five-question self-check shows how people see themselves, and a 30-minute work sample on a real task shows how they instruct AI, check the output and handle data. Managers add what they observe in one-on-ones over a month.
Should AI fluency be part of performance reviews?
Not at first. Use the levels for development goals while people get tools, training and time to practice. If the levels affect ratings too early, I’d expect people to stop answering the self-check honestly. Once the support is in place, a target level can become part of the role. Judge it on the work sample and on results, never on how many prompts someone sends.
What AI fluency level should managers have?
I recommend level 3 in their own work, which means using AI every week on real management tasks and checking the output. Managers set the norm for their teams, and it is hard to coach a habit you don’t have.
Sources
- EUR-Lex: AI Act, Regulation (EU) 2024/1689
- EUR-Lex: Digital Omnibus on AI, Regulation (EU) 2026/1744
- European Commission: AI literacy questions and answers (updated 20 October 2026)
- National Law Review: EU Digital Omnibus on AI enters into force (July 2026)
- Dakan, Feller and Anthropic: The AI Fluency Framework (PDF)
- Anthropic Academy: AI Fluency course
- Anthropic: The AI Fluency Index (February 2026)
- Zapier: AI fluency rubric (February 2026)
- Zapier: Raising the AI fluency bar for every hire (March 2026)
- Microsoft Learn: AI Fluency learning path
- State of AI in HR (AI & HR Barometern 2026)
Want help moving your teams up a level?
I run hands-on AI sessions where leaders and teams work on their own tasks, and change programs for organizations that want to go from literacy to fluency. 13 leadership teams rated my sessions 4.97 of 5 on average.