Up to 8 million UK jobs already at risk from AI without intervention

An analysis of the impact of generative artificial intelligence (AI) on the UK labour market uncovers a ‘distinct sliding doors moment’, with possibilities for huge job disruption in future or significant GDP gains, depending on government policy. The report from think tank the IPPR claims to identify two key stages of generative AI adoption: the first wave, which is here and now, and a second wave in which companies will integrate existing AI technologies further and more deeply into their processes.

The analysis of 22,000 tasks in the UK economy, covering every type of job, finds that 11 per cent of tasks done by workers are already exposed to in the first wave. It identifies ‘routine cognitive’ tasks (such as database management) and ‘organisational and strategic’ tasks (such as scheduling or inventory management) as most exposed to generative AI, which can both read and create text, software code and data.

However, this could increase to AI doing 59 per cent of tasks in the second wave. This would also impact non-routine cognitive tasks (such as creating and maintaining databases) and would affect increasingly higher earning jobs.

It says that back-office, entry level and part time jobs are at the highest risk of being disrupted during the first wave. These include secretarial, customer service and administrative roles.

Women are more likely to be in such jobs, which means they will be among the most affected, the report says. Young people are also at high risk as firms hire fewer people for entry-level jobs and introduce AI technologies instead. In addition, those on medium and low wages are most exposed to being replaced by AI.

A recent report from Robert Half mirrors many of these claims.

The claims are disputed by Stefano Bensi, general manager at SoftBank Robotics EMEA, which specialises in collaborative robots for the hospitality industry. “Rather than take away jobs, robotic solutions can provide real life support to hotel operations teams in many areas,” he says. “For example, with the hospitality industry constantly being struck with labour shortages, collaborative robots (cobots) offer much-needed assistance as a solution that is designed to perform safely and effectively alongside staff, customers, and guests. Robotic cleaning machines can help you quickly address labour concerns, increase efficiency, and maintain a high standard of cleaning or service. In a fast paced and ever-changing hospitality landscape, technology is advancing in industry to improve processes, increase guest experience, and ultimately make things easier. Throughout the hotel and restaurant industry as an example, IT, E-commerce and other technological solutions are being introduced at speed and are now a familiar sight for guests in front of house operations.”

IPPR has modelled three illustrative scenarios for the potential impact of the second wave of AI adoption on the labour market, depending on policy choices:

  • Worst case scenario – full displacement: all jobs at risk are replaced by AI, with 7.9 million job losses and no GDP gains
  • Central scenario: 4.4 million jobs disappear, but with economic gains of 6.3 per cent of GDP (£144bn per year)
  • Best case scenario – full augmentation: all jobs at risk are augmented to adapt to AI, instead of replaced, leading to no job losses and an economic boost of 13 per cent to GDP (£306bn per year)

IPPR has also modelled three scenarios for the potential impact of “here and now” generative AI on the labour market:

  • Worst case scenario – full displacement: 1.5 million jobs are lost, with no GDP gains
  • Central scenario: 545,000 jobs are lost, with GDP gains of 3.1 per cent (£64bn per year)
  • Best case scenario – full augmentation: no jobs are lost, with GDP gains of 4 per cent (£92bn per year)

Additionally, wage gains for workers could be huge – more than 30 per cent in some cases – but they could also be nil.

Deployment of AI could also free up labour to fill gaps related to unaddressed social needs. For instance, workers could be re-allocated to social care and mental health services which are currently under-resourced.

The modelling shows that there is no single predetermined path for how AI implementation will play out in the labour market. It also urges intervention to ensure that the economic gains are widely spread, rather than accruing to only a few.

Without government action and with companies left to their own devices, the worst-case scenario is a real possibility, IPPR says.

IPPR recommends the government develops a job-centric industrial strategy for AI that encourages job transitions and ensures that the fruits of automation are shared widely across the economy. This should include:

  1. Supporting green jobs, as green jobs are less exposed to automation than non-green jobs
  2. Fiscal policy measures, such as tax incentives or subsidies to encourage job-augmentation over full displacement
  3. Regulatory change, to ensure human responsibility of key issues, such as with health