Civil Service: Artificial Intelligence Productivity Gains Debate

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Department: Department for Science, Innovation & Technology

Civil Service: Artificial Intelligence Productivity Gains

Baroness Winterton of Doncaster Excerpts
Monday 1st September 2025

(2 days ago)

Lords Chamber
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Lord Vallance of Balham Portrait Lord Vallance of Balham (Lab)
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The figure of £573 million is, of course, forward spend, so that is not what has been spent; it is a commitment over the next few years. It is important that we measure this. Guidance on how to measure the impact of AI tools was issued last year; there is a rigorous process for doing that. On the 26 minutes that has been picked up by the Copilot study, it is of course a general AI tool. Much greater savings come with specific uses in specific areas, which will not be general across the Civil Service.

The noble Lord is quite right to point out that time saving is not productivity, but what we do know from studies elsewhere and across business is that, when you get those time savings, about half of it goes on core tasks, about a quarter is on other strategic and creative work and about 25% goes on enhanced well-being. That is what we might expect as a result. There is a lot to do to make sure that we implement this properly across the Civil Service.

Baroness Winterton of Doncaster Portrait Baroness Winterton of Doncaster (Lab)
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My Lords, would my noble friend the Minister also consider assessing productivity gains from basing civil servants in the regions rather than in London? In my view, as an ex-regional Minister for Yorkshire and the Humber, that would be an effective way of ensuring that the Government were focused on reducing regional inequalities and encouraging regional growth.

Lord Vallance of Balham Portrait Lord Vallance of Balham (Lab)
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I thank my noble friend for the question. I am not going to try to add the regional Civil Service to my brief, but I will say that this is exactly the sort of area where AI is rather good at working out whether there is a benefit, because it can spot patterns that are difficult to spot individually or by human endeavour. This is a place where you could begin to see what the real impacts are and what drives success rates in the regions.