How we calculate AI job exposure
The AI Task Exposure Score is a transparent, published heuristic — not a black box and not a prediction of job loss. Here is exactly how it works, what sources it rests on, and what it cannot know.
Last reviewed 2026-06-14
We score tasks, not people
A job is a bundle of tasks. We estimate exposure at the task level and roll it up — so the result shows which parts of the work are exposed, not a verdict on you.
Why job titles are imperfect
The same title means different work at different companies. A title is a starting point; pasting your actual responsibilities (a future feature) will always be more accurate.
Why high exposure is not guaranteed replacement
Exposure measures how well AI fits a task — not whether it will be adopted, allowed, trusted, or cheaper than a person. Regulation, cost, culture, and accountability all slow real-world replacement.
How we map titles to occupations
We normalise the title (removing seniority words, expanding abbreviations) and match it to the closest O*NET / ESCO occupation and its alternate labels, then attach a confidence level to the match.
How we classify tasks
AI can already do most of this task.
AI speeds this up but you stay in the loop.
Needs human judgment; AI only supports.
Depends on accountability and trust AI cannot hold.
How we calculate the score
Each occupation is rated 0–100 on eight factors. Five raise exposure; three lower it. The weighted result is normalised to 0–100.
Can the task happen mostly through software, documents, spreadsheets, code, or screens?
Does it involve reading, writing, summarising, explaining, coding, or communicating information?
Is it pattern-based, rules-based, template-based, or frequently repeated?
Can today's generative AI perform, draft, classify, retrieve, or accelerate it?
Do usage studies show AI actually being used for similar activities?
Does it need ethics, high-stakes decisions, taste, or ownership of outcomes?
Does it need physical presence, dexterity, machinery, or fieldwork?
Does it rely on persuasion, emotion, conflict resolution, or trust?
The raw range (−30 to +80) is mapped onto 0–100. We also derive sub-scores — automation, augmentation, human-moat strength, junior-role pressure, seniority protection, and reskilling urgency — from the same factors.
Confidence levels
High — an exact O*NET/ESCO match, many task statements, and several sources agreeing.
Medium — a partial title match or a related occupation used as a proxy.
Low — an emerging or non-standard title; the result is directional and should be read as such.
Source hierarchy
Official occupational datasets and institutional research. Used to set scores.
Government labour data for outlook, wages, and descriptions.
Reputable analysis used for framing, never as the sole basis for a score.
Used for colour and context — never for scoring.
We do not use random blogs, social posts, or influencer predictions as scoring sources.
Limitations
- AI capability and adoption change quickly; every score is a point-in-time estimate, not a forecast.
- Adoption varies by country, industry, company size, regulation, cost, and trust.
- A job title can mean very different work in different organisations.
- High exposure does not equal unemployment — it indicates where AI can assist or accelerate.
- This tool is guidance, not career, legal, or financial advice.
Update policy
We review scores as major occupational datasets refresh and as new AI labour-market research is published. Each result page shows when it was last updated. When the underlying evidence changes enough to move a score or a recommendation, we revise the page and the date.