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Will AI take this job?

The measured answer for Sales and Lettings Managers. Measured August 2026.

Sales and Lettings Managers

40

Business and Management

AI IMPACT
AI EXPOSURE TODAY40
20-YEAR SCENARIO
NOT A FORECAST
78%
THE DOOR · ENTRY DIFFICULTY TODAYC
STARTING PAY£25,000 - £30,000
REGULATORY MOATThe law requires human oversight. That human could be you.

AI exposure today for Sales and Lettings Managers is 40 on our index, an estimate rather than a measurement. Our Careermash 20-year scenario reaches 78%, a scenario, not a forecast. The door in is grade C today on our entry difficulty index. Moat: regulatory - The law requires human oversight. That human could be you.

AI EXPOSURE TODAY is a Careermash index from 0 to 100 of how exposed this job's everyday tasks are to AI today. We estimate it by matching published research on how people use AI to this job, usually by keyword or subject area, and we lower it where the work is protected (for example by physical, personal or legal requirements). It is not a direct measurement of how much people in this job use AI, and it is not a prediction that the job disappears.

GUT CHECK

That exposure number: how does it compare to what you expected?

Where it sits

Out of 1,840 scored careers, 1,543 are less exposed than Sales and Lettings Managers, 246 are more exposed, and 50 score exactly the same.

less exposed · same score (including this career) · more exposed

Within a tied score, order is alphabetical and means nothing: the measurement supports no finer ranking than the number itself.

Why we say this

The AI exposure today index draws on published AI-usage research (Anthropic and OpenAI, 2026), matched to each job by keyword or subject area. The entry difficulty grades (THE DOOR) are a Careermash rule of thumb built from our AI exposure index and the job's moat class. They describe today only and are not forecasts. Human necessity categories are based on OpenAI's AI Jobs Transition Framework. CareerMash maps these to UK careers and may apply UK-specific adjustments. The moat classification records what structurally protects a job: hands-on work, legal accountability, or people wanting a real person - and where we have no record, the card says so instead of guessing. Scorecard verdicts are Careermash editorial judgment and we own our conclusions.

The ledger

Every change to this measurement or to any wording that carries a claim is recorded here, permanently. Nothing is edited or deleted.

  • 2026-10-01Correction to our own wording. The entry difficulty grades, job security grades and scenario figures are Careermash rules of thumb, not forecasts, and we no longer attribute them to OpenAI's framework. We have stopped showing the Number of jobs rows, future entry difficulty grades and job security (today and future) until a proper outlook model is ready. The "AI used for" figure is now called AI exposure today, because it is an index, not a direct measurement of use in each job. No score changed.
  • 2026-08-29Provenance stated in full: exposure scores blend published AI-usage research (Anthropic 2026 observed usage; OpenAI "The AI Jobs Transition Framework", Richmond 2026, CC BY 4.0) with our own UK reviews of labour-market moats (statutory licensing and legal protections, which differ from the US), robotics evolution (whether AI alone or AI with robotics could take on the work, and when), and relevant scientific developments. Earlier citations named Anthropic alone for the exposure figure; no score changed, only the credit.
  • 2026-08-29Moat lines rewritten to assert only what the engine classification means (hands-on / protected by law / people want a real person / no special protection). No scores changed.
  • 2026-08-19Correction: careers without a moat record stopped showing a confident "NO MOAT" claim and now show "moat not yet classified". Exposure scores were and remain real for every career.
  • 2026-08-16First public measurement: AI exposure per occupation from Anthropic 2026 observed-usage research; door grades and 20-year horizons from OpenAI "The AI Jobs Transition Framework" (Richmond 2026, CC BY 4.0).
Show your parent → the same numbers, explained for the kitchen table
FOR MACHINES & CITATIONSThis answer as data: card.json · ai.md. Data CC BY 4.0 - cite as "Careermash, Measured August 2026" with a link to this page.