AI threatens half of UK office jobs by 2035, but trades like electricians stay resilient, new analysis finds
A new UK workforce forecast commissioned by Electricians London 247 shows that artificial intelligence could eliminate up to half of all routine office and customer-service roles by 2035, while skilled manual trades such as electricians, builders and mechanics are projected to remain the most secure occupations.
The model is based on official ONS employment data and projections aligned with research from the World Economic Forum and McKinsey & Company. It simulates how AI adoption could reshape Britain’s workforce from 2021 to 2035 under two different scenarios.
“AI will replace spreadsheets long before it replaces spanners,” said a spokesperson for Electricians London 247. “The UK urgently needs to invest in skilled trades and technical apprenticeships. Jobs that involve safety-critical, on-site decision-making will still need people long after AI takes over the admin.”
Why This Study Is Different
Unlike most global AI job forecasts that focus on how many positions could be automated, this analysis measures how many Britons will still be employed in each occupation by 2035.
It combines official ONS labour data with the government’s Skills Imperative 2035 baseline, adjusting for realistic AI adoption rates across sectors. This approach separates natural job growth from automation-driven displacement, revealing which roles shrink, which survive, and which grow as the UK economy adapts to AI.
Key findings (2025 to 2035)
| Occupation Group | Men (%) | Women (%) | Total (%) | Notes |
|---|---|---|---|---|
| Office & Administrative | −48 % | −52 % | ≈ −50 % | Fastest decline from automation of clerical tasks |
| Sales & Customer Service | −31 % | −38 % | ≈ −35 % | Retail and call-centre roles increasingly automated |
| Media & Creative | −23 % | −27 % | ≈ −25 % | Generative-AI tools reshape design and content workflows |
| Health, Care & Education | −9 % | −14 % | ≈ −12 % | Remain largely human-led, supported by AI tools |
| Construction & Electrical Trades | −12 % | −17 % | ≈ −14 % | Least exposed; on-site manual and safety-critical work persists |
| All Occupations (Weighted Average) | −19.3 % (≈ 3.3 m jobs) | −21.8 % (≈ 3.4 m jobs) | ≈ −20.6 % (≈ 6.7 m jobs) | Net employment decline to 2035 |
- Women’s employment declines by around 21.8 per cent, equal to about 3.4 million jobs. Men’s employment declines by 19.3 per cent, equal to about 3.3 million jobs.
- Women’s share of the total UK workforce slips slightly, from 47.8 per cent to 47.0 per cent.
- Office and administrative jobs shrink by roughly 50 per cent, or about 1.6 million posts, the steepest fall of any category.
- Sales and customer-service roles contract by about 35 per cent, or 1.1 million jobs.
- Media and creative jobs decline by around 25 per cent as generative-AI tools begin automating design and content workflows.
- Health, care and education sectors stay comparatively stable, falling by 6 to 16 per cent.
- Skilled construction and electrical trades fall only 10 to 19 per cent, confirming physical, on-site work as the least exposed segment.
- Across all occupations, about 60 per cent of total UK work hours could be automated by 2035 in the faster-adoption scenario.
The data also highlights a strong South-East concentration of resilient trades, with London retaining a stable base in construction, maintenance and electrical work that helps offset white-collar losses.
Interactive data visualisation
An interactive racing-bar chart (2010 to 2035) illustrates how each occupation group rises or falls over time. Clerical and customer-service jobs collapse after 2026, while electricians, care workers and teachers remain steady.
View the full animation and download the dataset:
Two scenarios modelled
- Conservative (WEF-aligned): slower AI adoption from 0.12 to 0.56, displacement elasticity δ = 0.7.
- Accelerated (McKinsey-aligned): faster AI adoption from 0.12 to 0.85, δ = 0.8, calibrated to McKinsey’s estimate that 60 per cent of work hours could be automated by 2035.
- Both scenarios use the same exposure and weighting so differences reflect only the speed of adoption.
Method overview
Employment is calculated using:
Employmenty=Baseliney×(1−δ×Exposure×Ay×wocc)Employment_y = Baseline_y \times (1 - δ \times Exposure \times A_y \times w_{occ})
- Baseline: ONS APS (2010 to 2025) and Skills Imperative 2035 projection
- Exposure: OECD and ILO task indices mapped to UK SOC 2020
- A ᵧ: yearly AI adoption rate
- w (occ): occupation-specific adoption weight
Weights reflect faster adoption in back-office work (1.3) and slower adoption in skilled trades (0.8).
View full methodology and datasets.
Why Electricians London 247 commissioned the study
As a South London electrical firm investing in apprenticeships and technical training, Electricians London 247 commissioned this forecast to identify which sectors and skills will define the next generation of UK work. The analysis was developed with independent economists and data specialists to ensure robust, evidence-based projections.
About Electricians London 247
Electricians London 247 is an accredited, South London-based NICEIC contractor delivering residential and commercial electrical services across the capital. The company’s apprenticeship and upskilling initiatives aim to secure London’s future pipeline of certified electricians and technical professionals.
Media enquiries
press@electricianslondon247.co.uk | Phone: 020 4600 5848
Methodology: electricianslondon247.co.uk/press/methodology-uk-jobs-2010-2035-with-ai-adoption-overlays