AI Skills Ranked by Job Role: Where Does Yours Stand? (2026 Data)

Who is actually good at AI? Not who thinks they are good — who demonstrates it when measured?

We scored 1,017 professionals across 11 AI skills in real conversational assessments (not surveys) and broke the results down by role. The rankings are not what LinkedIn discourse would predict.

Full data: The State of AI Literacy 2026.

The AI Skills Leaderboard by Role

Every score above is from a measured assessment — professionals demonstrated their skills in a 25-minute conversation, scored against the AISA Rubric by an independent evaluator. Self-reported confidence had no bearing on the score.

6 Findings That Matter

1. Product Managers Lead — and It Makes Sense

PMs score highest at 59.7 — the only role whose average breaks into the Proficient tier. They sit at the intersection of strategy, user needs, and technical constraints: the exact combination that builds well-rounded AI fluency. They do not just use AI; they evaluate its trade-offs.

2. Engineers Are Good at AI — But Not at AI Safety

Engineers rank third overall (55.0) with the second-highest Technical Understanding (51.6). But their Safety score (43.8) is 11 points below their overall average.

They understand how the tools work. They are less careful about how they deploy them. This matters because engineers are often the ones building AI into products that other people use. A safety blindspot in the engineering team propagates downstream.

3. Marketing Has the Worst AI Safety Score of Any Role

At 37.2, Marketing & Content professionals have the lowest Safety & Responsibility score in the dataset — 15 points below their overall average (52.4). They use AI frequently and get real value from it. They are the least aware of the risks: data boundaries, bias in generated content, downstream impact of AI-written material.

If your marketing team is using AI to generate customer-facing content — and statistically, they are — this is worth knowing.

4. Researchers Know About AI But Cannot Use It

Research & Academia scores a surprising 42.6 — well below the overall average (51.7) — despite being professional knowledge workers. Their Technical Understanding (33.4) is among the lowest of any role.

The explanation: knowing about AI (reading papers, understanding concepts academically) is not the same as knowing how to use AI. The AISA assessment measures demonstrated proficiency, not theoretical awareness. Researchers who study AI may not be the ones building workflows with it.

5. Students Are the Least AI-Literate Group

Students average 37.3 with a Safety score of 30.0 and Technical Understanding of 29.9. Every dimension is below average. The generation that grew up with AI understands it least.

This is a pipeline problem. If students entering the workforce in 2026–2027 average 37 on measured AI literacy, organisations are inheriting the AI skills gap, not resolving it.

6. Operations Has a Hidden Strength

Operations & Management has the lowest overall score among non-student roles (43.0) and the weakest Technical Understanding (30.9). But their Safety score (45.4) is above Engineering's (43.8). They may not understand the technology, but they think about risk and process — a foundation that technical training can build on.

What This Means for Hiring

If you are evaluating candidates for AI-adjacent roles, two signals matter more than the overall score:

Safety awareness relative to usage. A candidate who uses AI extensively but scores low on Safety is a risk multiplier. They will deploy AI confidently in situations that require caution. The role breakdown shows this is most acute in Marketing (15-point safety gap) and Engineering (11-point gap).

Technical understanding relative to workflow integration. A candidate with high Workflow but low Technical Understanding is efficient today and fragile tomorrow. When the tools change — and they will — they will not adapt. This pattern is most common in Design & Creative (39.6 technical vs 47.3 overall).

The strongest candidates have balanced profiles: they use AI effectively and understand how it works and know when to be careful. That combination is rarer than the overall score suggests — most professionals are lopsided.

Where to Improve if Your Role Scores Low

The most common growth areas across all 1,017 assessments:

The AI Coach personalises this path — daily lessons via WhatsApp, starting from wherever your assessment identified the biggest gap.

Frequently Asked Questions

Which job role has the best AI skills?

Product managers score highest among major roles (59.7 out of 100), based on 1,017 measured assessments. They are the only role whose average reaches the Proficient tier. Engineering ranks third (55.0), behind Executive & Leadership (55.8).

Do engineers have good AI skills?

Engineers score above average on AI fluency (55.0) and have the second-highest Technical Understanding (51.6). However, their Safety & Responsibility score (43.8) is 11 points below their overall average — a meaningful gap that suggests understanding how AI works has not translated into proportional caution about how it is deployed.

What AI skills do non-technical roles need?

Non-technical roles (Marketing, Operations, Education) score lowest on Technical Understanding (31–45 out of 100) and highest on Workflow (relative to their other dimensions). The data suggests focusing on AI fundamentals, AI safety, and limitation awareness — understanding the tool, not just operating it. The AISA assessment identifies specific gaps per person.

Are students AI-literate?

Students average 37.3 on measured AI literacy — the lowest of any role category — with a Safety score of 30.0. Despite growing up with AI tools, this cohort demonstrates the least understanding of how AI works and how to use it responsibly. Organisations hiring recent graduates should consider measuring AI readiness as part of onboarding.

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Related reading: 44% of AI Users Can't Explain How AI Works — the full literacy gap analysis. What the Top 2% Do Differently — the Expert vs average breakdown. AI Readiness Assessment Tools Compared — how to measure your team.