1,800 professionals show a massive AI fluency gap
The Perception vs. Reality Gap
The most striking finding is the confidence mismatch. The least AI-fluent professionals tended to overestimate their scores by about 40 points, while the top performers actually underestimated their own abilities by 27 points. When you look at the average score across the entire group, it sits at 48—which lands squarely in the "Developing" tier. It seems most people are using LLMs regularly, but they're doing it blindly without any systematic prompt engineering or a structured AI workflow.
Who is actually fluent?
The breakdown by role was unexpected. Product managers actually outscored engineers, with a score of 59.2 compared to 53.7. This suggests that applied judgment—knowing how to steer the model toward a business outcome—is currently more prevalent than deep technical knowledge of how the models function. On the flip side, HR professionals, who are often the ones utilizing AI to filter candidates and make hiring decisions, consistently scored the lowest in AI fluency.
The failure of corporate training
The data also exposes why most company-wide AI training fails. These programs usually assume a baseline level of knowledge across the organization, but the actual starting points vary by as much as 5x within a single team. In one specific instance, the gap between the least and most fluent employee in the same department was 82 points (one scoring 15 and another scoring 97), despite them having the same tools and the same training budget.
Two out of three people claim they are "good" with AI, yet they fail to even reach a proficient level on the assessment. This suggests that "AI fluency" has become a buzzword that people attach to their LinkedIn profiles without actually mastering the mechanics of LLM interaction. If you're trying to build a real-world AI deployment strategy for a team, you can't treat the staff as a monolith; the skill variance is simply too high.
For those who want to dig into the actual math and logic behind these scores, the methodology is documented here:
https://aisa.to/methodology