Impact of AI on North East occupations

Estimating the exposue of occupations to artificial intelligence

The data on this page is sourced from the Department for Education report produced by the Unit for Future Skills and published in November 2023 "The Impact of AI on Jobs and Training".

This report adapts research conducted in the United States on the exposure of tasks to developments in Artificial Intelligence (AI), including large language models (LLMs). It maps these tasks to specific job roles to understand the relative exposure of these roles to AI. 

Exposure in the DFE study refers to the fact that the task and job in question will be changed by AI, not if this task will be augmented or automated. To start answering this question the Unit for Future Skills also included some data from the International Labour Organization on augmentation and automation. This additional data, however, covers a much smaller proportion of roles than the DFE data.

To apply the Unit for Future Skills analysis specifically to the North East we have used data from the 2021 Annual Population Survey on the number working in different occupations across the region. 2021 is the most recent data we can use because the analysis relies on the 2010 version of SOC codes. It applies to the North East region not just the North East LEP area. 

Reading the DFE report alongside our analysis will help readers understand the context and implications of the data. 
 

Overall exposure to artificial intelligence in the North East

Analysing the data by region suggests that the North East is less exposed to Artificial Intelligence than other regions. Only 24.1% of jobs in the North East were in the most exposed quartile of occupations, compared to 44.1% in London and 29.1% in England excluding London. 

The analysis suggests the development of large language models is likely to have a bigger impact on the North East than AI overall. 26.2% of jobs in the North East were in the top 25% most exposed occupations by LLMs in the North East. 

The supplementary data from the International Labor Organization on augmentation and automation implies that the region is less likely to see positive augmentation than elsewhere. Only 9.5% of roles in the North East were classed as likely to be augmented, more than the proportion that may see automation, but lower than the 13.6% of jobs likely to see augmentation in London and 11.7% in England excluding London. The North East also has a higher proportion of roles that are resistant to automation because they involve a high degree of variability and soft skills. 

Despite these headlines suggesting the North East will be less affected than other regions, this still leaves many workers in the region who are likely to see their tasks change due to AI. The 24.1% in the top exposure quartile in the region is equivalent to 284,000 workers, while for LLM the equivalent figure is 308,300.
 

Impact on specific occupations in the North East

The North East overall has fewer people working in occupations with relatively high exposure to AI compared to other regions, but there are particular occupations that are both more prevalent in the region than elsewhere and have high AI exposure scores. 

For example, the proportion of residents working in national government administrative occupations is three times the national average, while the proportion of telephone salespersona is more than twice the national average. These are both occupations with relatively high AI exposures scores (telephone salespersons especially in relation to large language models)

In terms of the largest occupations by total employment, the four largest occupation groups in the North East have negative exposure scores, meaning they are not especially exposed to AI due to the soft skills, variability or practical nature of the role.

Nurses are the most common occupation with a positive exposure score, although it is relatively low. Other administrative professionals, secondary education teaching professional, and national government administrators are the next three largest occupation groups in the North East and they all have positive exposure scores.