# AI for bureaucratic productivity: Measuring the potential of AI to help automate 143 million UK government transactions

arXiv: https://arxiv.org/abs/2403.14712 · HTML: https://mlsanity.com/paper/2403.14712/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 48 references that are in the map.
- First posted on arXiv 18 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models](https://mlsanity.com/paper/2303.10130/) (2023)
- [The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence](https://mlsanity.com/paper/2201.04200/) (2022)
- [Generative AI is already widespread in the public sector](https://mlsanity.com/paper/2401.01291/) (2024)
- [Artificial intelligence in government: Concepts, standards, and a unified framework](https://mlsanity.com/paper/2210.17218/) (2022)
- [A multidomain relational framework to guide institutional AI research and adoption](https://mlsanity.com/paper/2303.10106/) (2023)

## Built on by (most cited first, 4 of 4)

- [Oversight Structures for Agentic AI in Public-Sector Organizations](https://mlsanity.com/paper/2506.04836/) (2025)
- [Agent Benchmarks Fail Public Sector Requirements](https://mlsanity.com/paper/2601.20617/) (2026)
- [Artificial Intelligence in Government: Why People Feel They Lose Control](https://mlsanity.com/paper/2505.01085/) (2025)
- [A Technical Typology of AI Systems in Public Administration](https://mlsanity.com/paper/2606.31755/) (2026)

## Abstract (arXiv)

There is currently considerable excitement within government about the potential of artificial intelligence to improve public service productivity through the automation of complex but repetitive bureaucratic tasks, freeing up the time of skilled staff. Here, we explore the size of this opportunity, by mapping out the scale of citizen-facing bureaucratic decision-making procedures within UK central government, and measuring their potential for AI-driven automation. We estimate that UK central government conducts approximately one billion citizen-facing transactions per year in the provision of around 400 services, of which approximately 143 million are complex repetitive transactions. We estimate that 84% of these complex transactions are highly automatable, representing a huge potential opportunity: saving even an average of just one minute per complex transaction would save the equivalent of approximately 1,200 person-years of work every year. We also develop a model to estimate the volume of transactions a government service undertakes, providing a way for government to avoid conducting time consuming transaction volume measurements. Finally, we find that there is high turnover in the types of services government provide, meaning that automation efforts should focus on general procedures rather than services themselves which are likely to evolve over time. Overall, our work presents a novel perspective on the structure and functioning of modern government, and how it might evolve in the age of artificial intelligence.