How we measure
Every one of the 1,840careers on this site carries a number: an AI exposure today index, our estimate of how exposed that job's everyday tasks are to AI. This page is the full account of where that number comes from, what keeps it honest, and how it relates to the official UK index.
What goes into the number
The score is a blend, not a single source. Published AI-usage research provides the foundation (Anthropic's 2026 labour market research and OpenAI 2026), and we match it to each job by keyword or subject area, so it is an index, not a direct measurement of use in each job. Our entry difficulty grades, job security grades and 5, 10 and 20 year scenario figures are Careermash rules of thumb built from the exposure index and each job's moat class. They are not forecasts. On top of that sit our own UK reviews, because the UK labour market is not the US labour market: a review of structural protections (statutory licensing, reserved legal activities and other legal protections that shape who may do what work here), a per-career assessment of robotics evolution (whether AI alone, or AI with robotics, could take on the work, and when), and relevant scientific developments.
The result is an estimate of today, not a prediction. When the landscape moves, the index moves with it: the score is maintained to constantly reflect the developing picture across AI, technology, robotics and scientific research, and every revision is recorded permanently in the open change ledger, career by career. Nothing is silently re-stated. The ledger currently holds 5 dated entries, including corrections to our own earlier statements, because a measurement you cannot audit is just an opinion.
How this relates to the official UK index
The Department for Education published an AI Occupational Exposure index in November 2023, and Skills England's 2026 skills assessments build on the same methodology. It is serious work and we encourage anyone comparing sources to read it. It answers a different question from ours, and both answers are useful.
The official index measures ability overlap: it maps occupations to the abilities they rely on, and scores how related those abilities are to what AI systems have demonstrated. It produces a relative ranking of occupations against each other (a standardised score, so a negative value still means some exposure, and there are no official Low or High bands). Our score is an exposure index informed by task usage: published research on how people use AI, matched to each job by keyword or subject area, and lowered where the UK's own structural protections apply.
Across a matched sample of occupations the two approaches order the labour market in strong agreement: the trades, hands-on and physical work sit at the low end of both, and software, finance and marketing work sit high on both. Where they differ most is the relational, licensed and caring professions: an abilities lens sees high overlap with what AI can demonstrate, while a usage-and-protection lens observes that the day-to-day work of, say, a psychologist or a social worker still runs through a person, and that UK licensing and accountability structures shape how that changes. The official report itself is careful on this point: it measures exposure rather than distinguishing augmentation from substitution. Our moat axis is one attempt to add exactly that distinction, and we publish it as our own editorial classification with an honest "not yet classified" state where we have made no judgment.
In short: the official index is a valuable relative map of ability exposure. Ours is a living estimate of present-day exposure, kept current as AI, robotics and science move, with every change on the record. Read together, they are more useful than either alone.
Read further
Questions about the method, or spotted something we should look at again? The ledger exists because we expect to be checked. Contact us via the schools page and we will answer.