Yorkshire and the Humber · Baseline population 812,000
Official ONS figures for the region Leeds sits in, published May to July 2026. These are measured statistics, separate from the structural exposure score above — they are reported here as context and are not an input to it. Region-level data is the finest geography ONS publishes monthly; it is not a Leeds-only figure.
UK average for the same period: unemployment 4.9%, employment 75.1%, inactivity 20.9%. Yorkshire and the Humber ranks 3 of 12 UK regions and countries by unemployment rate.
145,445 people in Yorkshire and the Humber were claiming unemployment-related benefits in August 2026, 5.3% of economically active residents, up 2,800 on August 2025. Among 16–24 year olds the count was 33,400, up 2,975 on the year. Not seasonally adjusted; this is an administrative count, not a survey estimate.
2,398,145 people were on a payroll in Yorkshire and the Humber in August 2026, down 9,824 (0.41%) on the year, against 0.48% down across the UK as a whole. This is an administrative count of actual payroll records, not a survey estimate. The most recent month is a provisional flash estimate and is routinely revised.
The two UK-wide measures of employees disagree. HMRC payroll (RTI) records show payrolled employees down 101,000 over the year to July 2026, and down 144,849 on the year to 30.2 million in August 2026. Over the comparable period the Labour Force Survey shows employees up 111,000 to 29.8 million. ONS attributes much of the survey's rise to improved response rather than to hiring, and states that RTI currently provides the most reliable measure of employees. We surface both rather than quietly picking one. ONS publishes research linking RTI to the LFS on 28 September 2026.
The survey side is UK-wide; ONS publishes no regional split of this comparison. The payroll side is parsed from the ONS reference table (rtisasep2026.xlsx). Source: ONS Labour market overview, UK: September 2026, released 15 September 2026. Next release 20 October 2026.
Sources. ONS Labour market overview, UK (time series API); ONS Labour market in the regions of the UK (Table 2, annual changes); ONS Annual Population Survey, via NOMIS (variables, percentages); Claimant count by sex and age (Universal Credit + JSA), ONS/DWP via NOMIS; Earnings and employment from PAYE Real Time Information, seasonally adjusted (Table 7, payrolled employees by NUTS1 region). Retrieved 2026-09-25. LFS and APS outputs moved from “official statistics in development” to “official statistics” on 11 August 2026 and ONS is not seeking reaccreditation; survey-based detail should be read with that in mind. Contains public sector information licensed under the Open Government Licence v3.0.
Reported UK redundancy events from the CopeCheck Layoffs tracker. These are UK-wide, not Leeds events — the tracker records the country a layoff was reported in, not the town or city, so nothing here is attributed to Leeds.
| Reported | Company | Roles | Sector | Source |
|---|---|---|---|---|
| 2026-08 | University of Essex | 400 | Education | www.bbc.co.uk |
| 2026-08 | Nothing | 100 | Technology | Google Alerts RSS |
| 2026-08 | Travelport | 57 | Technology | Google Alerts RSS |
| 2026-08 | TSB | 130 | Banking | bmmagazine.co.uk |
| 2026-08 | Diageo | 38 | Food & Beverage | drinks-intel.com |
| 2026-08 | WPP | 1,267 | Advertising | gbnews.com |
| 2026-08 | Supermassive Games | 75 | Technology | Google Alerts RSS |
| 2026-08 | RNLI | 75 | Charity/Non-profit | Google Alerts RSS |
74 UK events recorded for 2026, of which 11 are attributed by their source at least partly to AI or automation. Counts are individual reported events, not a national total. The upstream tracker withholds aggregates because source claims may be syndicated, aggregate, planned, aliased or overlapping. These are UK-wide and are not attributed to this city. Compiled from layoffs.fyi, TrueUp, Crunchbase, TechCrunch, and news reports. Snapshot taken 2026-10-06.
Model-generated scenario commentary, not reported fact. Specific claims about future employer or council behaviour are hypothetical; use the sector table, not the prose, as the reproducible input to the score.
Leeds has built the largest financial and professional services cluster outside London — financial services at 10.4%, professional and scientific at 7.8%, admin at 10.2% — and that cluster is simultaneously the city's greatest strength and its most specific AI vulnerability. Legal services, accounting, insurance, and banking back-offices are exactly the sectors where AI is demonstrating the most disruptive capability right now. Leeds isn't a future case study in AI displacement — it's a current one, playing out in real time in the law firms and financial institutions along Wellington Street. Health at 10.1% and education at 9.2% provide genuine resilience, and the city's cultural renaissance (the new arena, the revitalised waterfront) creates quality-of-life appeal. But the professional services jobs that pay the mortgages on those waterfront apartments are the ones most immediately in the firing line.
Leeds bet big on being London's back office, and now the front office is automating. The city's financial services cluster — the one that every economic development document leads with, the one that justifies the glass towers on the waterfront — is built on exactly the kind of analytical, process-driven, document-heavy work that LLMs handle better than a junior associate billing £200 an hour. Financial services at 10.4%, admin at 10.2%, professional at 7.8% — over 28% of the workforce does the kind of thinking-on-paper that AI is already learning to replicate. The legal sector is particularly exposed: contract review, due diligence, compliance checking — these aren't abstract future scenarios, they're things AI does today, and the law firms on Park Row know it even if they're not saying it publicly. The Trinity Centre looks great and is full of shops that exist mainly because the professional services workers buy lunch. What happens to the lunch economy when the professional services economy shrinks? Leeds has spent twenty years becoming a serious financial centre. It's about to discover that serious financial centres are the ones AI disrupts first, because that's where the highest-value automation targets are.
Leeds should position its financial services cluster as an AI-in-finance innovation hub rather than waiting to be disrupted by one. The city has the talent, the institutional density, and the professional services infrastructure to become the UK's centre for legal tech, fintech, and AI-driven financial services — if it moves fast. Partner the universities (Leeds, Leeds Beckett) with the major employers on fast-track programmes to retrain financial services workers as AI operators, compliance technologists, and algorithmic auditors. The legal sector specifically needs a transition strategy: the law firms need to adopt AI and retain staff in new roles, not just cut headcount. The broader economy needs diversification: health-tech, the creative sector around the Call Lane/Meanwood axis, and the Channel 4 presence should all be actively scaled. Don't wait for displacement to drive transition — by then the talent will have left for London or Manchester.
They'll host a fintech conference at the Royal Armouries, give out awards with names like 'Innovator of the Year,' and congratulate themselves on Leeds' 'digital transformation' while the actual transformation happens in the law firms and banks cutting headcount. The legal sector will discover what every other knowledge industry discovers: AI doesn't replace the best people, it replaces the middle, and the middle is where most of the employment is. Someone will announce a 'Leeds Legal Innovation Centre' in a converted warehouse near the Calls, staffed by three people and a chatbot. Channel 4's Leeds presence will be celebrated for another decade as proof of the city's 'creative economy' despite employing about as many people as a large Wetherspoons. The universities will produce excellent AI research that gets cited globally and commercialised elsewhere. Trinity will get another food hall. The waterfront will get more glass. And the actual financial services workers — the ones who do the processing, the checking, the reviewing, the filing — will discover that their employers' AI strategy is 'your job, but without you.' Ask again in five years.
Historical employment share by broad sector, with CopeCheck's heuristic exposure weight and contribution to the relative score. Higher weights contribute more; they are not probabilities.
| Sector | Employment % | Exposure Weight | Exposure Tier | Contribution |
|---|---|---|---|---|
| Financial & Insurance Services | 10.4% | 0.75 | high | 7.8 |
| Administrative & Support Services | 10.2% | 0.85 | high | 8.67 |
| Human Health & Social Work | 10.1% | 0.18 | low | 1.82 |
| Retail | 9.2% | 0.8 | high | 7.36 |
| Education | 9.2% | 0.15 | low | 1.38 |
| Professional, Scientific & Technical | 7.8% | 0.3 | low | 2.34 |
| Manufacturing | 6.8% | 0.82 | high | 5.58 |
| Accommodation & Food Services | 6.2% | 0.48 | medium | 2.98 |
| Information & Communication | 5.8% | 0.5 | medium | 2.9 |
| Transport & Storage | 5.4% | 0.78 | high | 4.21 |
| Public Administration & Defence | 5.2% | 0.22 | low | 1.14 |
| Construction | 4.6% | 0.28 | low | 1.29 |
| Wholesale | 4.4% | 0.55 | medium | 2.42 |
| Arts, Entertainment & Recreation | 3.0% | 0.2 | low | 0.6 |
| Real Estate | 1.4% | 0.4 | medium | 0.56 |
| Agriculture, Forestry & Fishing | 0.3% | 0.25 | low | 0.07 |
The vulnerability score is a weighted average of Leeds's sector employment shares. Each broad sector carries a CopeCheck heuristic exposure weight (0.0–1.0). Frey & Osborne's study estimated occupation-level susceptibility; it did not publish these sector weights. The bundled city files also lack record-level source dates and ledgers, so this is a historical structural comparison rather than an official current statistic. The weighted average is normalised to a 0–100 scale. A score of 72.8 means Leeds's workforce is significantly concentrated in higher-weighted sectors compared with other United Kingdom cities in this dataset; it does not state a 72.8% probability of displacement.