5 Things the Top 2% of AI Users Do Differently (2026 Data)

Out of 412 professionals who completed a measured AI fluency assessment, exactly 7 reached the Expert tier (92+ out of 100). That is 1.7%.

We compared their skill profiles against the general population — dimension by dimension, criterion by criterion — and found five consistent differences. None of them are "they use AI more."

Full data: The State of AI Fluency 2026.

1. They Understand How AI Actually Works

The biggest gap between Experts and the general population is not in how much they use AI. It is in how well they understand it.

Technical Understanding — the dimension measuring whether you can explain core concepts (tokens, context windows, temperature, model architectures) and apply them to real decisions — shows a 43-point gap. That is nearly double the average professional's score.

This understanding shapes everything downstream: Experts choose the right tool because they understand the trade-offs. They design better prompts because they understand what happens to their input inside the model. They predict failure because they have a mental model of how the system works.

44% of professionals cannot explain AI fundamentals at a functional level. Experts can — and that single difference cascades across every other skill.

2. They Are More Cautious, Not Less

This is the most counterintuitive finding in the data. Safety & Responsibility is tied with Technical Understanding for the largest expert-vs-population gap — 43 points (88.1 vs 45.0).

The most capable AI users are the most careful. Not because caution slows them down, but because understanding how AI works makes them aware of how it fails. You cannot be cautious about risks you cannot see.

What does an 88 look like in practice? These professionals:

• Adjust their verification effort based on what is at stake
• Think about downstream impact before sharing AI output
• Have personal policies about what data they will and will not share with AI tools
• Can articulate specific AI failure modes they have personally encountered

The average professional (scoring ~45) is aware of the obvious risks — hallucinations, bias — mostly from headlines. They do not systematically adjust their behaviour based on stakes. This gap is documented in depth in The State of AI Literacy 2026.

3. They Have Built Something With AI

AISA classifies every professional into one of ten AI skill personas. The data reveals a cliff between the Enthusiast (average score 53, 24% of professionals) and the Builder (average score 73, 16% of professionals).

That 20-point gap — the largest persona-to-persona jump in the dataset — represents the transition from using AI eagerly to building with it deliberately. Builders have created something that did not exist before: a custom tool, an automation, a system. That creative act forces deeper understanding.

Every Expert in our data has built something. None are pure consumers of off-the-shelf AI tools.

The reverse pattern is also instructive: talking about building is not the same as building. "My team uses AI agents" caps personal proficiency at 4 out of 10 in the AISA rubric. Experts demonstrate what they created, not what their organisation deployed.

4. They Verify Differently

20% of all professionals have no systematic process for verifying AI output. They accept what AI gives them, or check sporadically based on gut feeling.

Experts do not just verify more — they verify differently. The distinction shows up in the Output Evaluation criterion (T1):

• Average professional (5.5/10): Checks sometimes, catches factual errors when they stand out, but has no consistent methodology
• Expert (9+/10): Verification is baked into the workflow architecture — automated checks, cross-referencing with primary sources, human-in-the-loop gates at decision points

The shift is from "I should probably check this" to "I have designed a system that makes checking automatic." That is the difference between a habit and an architecture.

5. They Know What AI Cannot Do — From Experience

Limitation Awareness (T2) separates professionals who have heard that AI can be wrong from those who know from experience how and when it goes wrong.

Across all assessments, 36% of professionals know AI's limitations only from headlines — hallucination, bias — not from anything they have personally tested or experienced. Only 17% score Proficient or above, meaning they can predict failure before it happens and adjust their approach preemptively.

Every Expert in the data has encountered specific failure modes in their own work. They do not trust AI less — they trust it more precisely. They know exactly where it is reliable, where it is unreliable, and what to do when it fails in the middle of a workflow.

The Persona Nobody Has Reached

AISA has ten personas. The tenth — The Oracle — represents principles-first understanding of how AI systems behave: training data, architecture, inference, fine-tuning at a systems level.

In 412 assessments, zero professionals have been classified as an Oracle. It is the only persona that remains unclaimed. What that means: even the best professionals in our data are sophisticated users and builders of AI — not yet operating at the level of understanding how AI systems behave from first principles.

How to Move Toward the Top

Based on what measurably separates the top 2%:

1. Build something. Do not just use ChatGPT — connect it to something. Use the API, create a custom GPT, automate a workflow with Zapier AI or n8n. The act of building forces understanding that reading cannot replicate.
2. Learn one failure mode from experience. Pick an AI task you do regularly and deliberately try to make it fail. Feed it contradictory instructions. Ask it about something you know well enough to verify. Document what goes wrong and why.
3. Add friction before sharing. Before using AI output for anything that matters, ask: "What could go wrong if this is wrong?" That single question puts you ahead of a significant portion of the workforce.
4. Study how models work. Not at research level — at practitioner level. AISApedia covers 147 AI concepts from foundational to advanced. The AI Coach personalises the sequence based on your assessment results.

Frequently Asked Questions

What percentage of AI users are considered experts?

Only 1.7% of professionals reach Expert-level AI fluency (score 92+ out of 100), based on 412 measured assessments. The Expert tier requires principle-level mastery — understanding AI at a systems level, not just using it effectively.

What skills do expert AI users have that average users lack?

The two largest skill gaps between Experts and the average professional are Technical Understanding (+43 points) and Safety & Responsibility (+43 points). Experts understand how AI works and are more cautious about how they use it — the population's weakest dimensions are exactly where Experts pull furthest ahead.

How can I improve my AI skills quickly?

The fastest path is building something with AI (not just using it), learning specific AI failure modes from personal experience, and studying how models work at a practitioner level. The three weakest skills across all assessments are AI Fundamentals, Tool Landscape, and AI Safety — starting with any of these gives the highest return.

Is there an AI skill level above Expert?

Yes — The Oracle persona represents principles-level understanding of AI systems: training, inference, fine-tuning, and architecture. In 412 assessments, zero professionals have reached this level. It remains the only unclaimed persona.

---

Related reading: What Is a Good AI Score? — the full benchmark table and percentiles. AI Skills by Job Role — how roles compare across all dimensions. 5 Best Free AI Courses with Certificates — where to start building your skills.