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Telford and Wrekin, UK · POP — · EMP 85,493 · DATA GRADE B

Telford and Wrekin, UK

30.9 /100

ILO GenAI exposure index via empirical SOC2020↔ISCO crosswalk · 3-digit data — own scale, not comparable with US/Canada

#90 of 125

more exposed than 29% of UK areas (England & Wales)

Secondary measures

Scenario: if replacement-level AI arrives in 2030

2027 2035

Figure 1. Modeled displacement under the median preset (diffusion k=0.8, ceiling 0.75, automation share 0.45, friction lag 1.5y, attrition 3%/y). Solid: positions eliminated. The gap between gross and layoffs is natural attrition — speed of diffusion, not depth of exposure, determines layoffs. This is a scenario, not a forecast: adjust every assumption.

Where the losses land — and your assumptions

Positions eliminated by 2035 per occupation group, under the arrival year selected above. Drag any multiplier if you think we're wrong about a group — your model, your numbers. Multipliers scale that group's task exposure (×0 = immune, ×2 = double).

Table 3. Group exposure = employment-weighted mean task exposure (Eloundou β over the group's local occupations). Bars use the same scenario engine as Figure 1 (median preset).

Most exposed local occupations

OccupationJobsMedian wageExposure [range]
Sales Assistants and Retail Cashiers 4,236
37.5
Information Technology Professionals 2,454
51.2
Other Administrative Occupations 2,060
58.9
Caring Personal Services 4,830
20.6
Sales, Marketing and Related Associate Professionals 2,046
47.3
Functional Managers and Directors 1,983
41.1
Administrative Occupations: Finance 1,456
53.6
Road Transport Drivers 3,150
24.3
Teaching and other Educational Professionals 2,433
30.1
Secretarial and Related Occupations 1,354
53.1

Table 2. Ranked by exposure × local employment. Bands on the 0–100 occupation scale.