[Paper Review] AI for bureaucratic productivity: Measuring the potential of AI to help automate 143 million UK government transactions

Vincent J. Straub, Youmna Hashem+6 more|arXiv (Cornell University)|Mar 18, 2024

Stock Market Forecasting MethodsDecision Sciences3 citations

TL;DR

This study maps 377 UK central government services and estimates 143 million complex, repetitive citizen-facing transactions annually, finding 84% are highly automatable via AI; automating just one minute per transaction could save 1,200 person-years of work yearly, highlighting a major opportunity for bureaucratic productivity gains through AI-driven automation.

ABSTRACT

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.

Motivation & Objective

• To measure the scale and structure of citizen-facing bureaucratic transactions in UK central government.
• To estimate the potential for AI-driven automation of these transactions, focusing on complex, repetitive procedures.
• To develop a model for estimating transaction volumes without direct measurement, enabling scalable assessment across services.
• To identify that automation should target general procedural workflows rather than specific services due to high service turnover.
• To provide a data-driven framework for governments to prioritize AI investment in high-impact, automatable administrative processes.

Proposed method

• Mapped 377 UK central government services using public data sources, service directories, and digital service platforms.
• Identified and quantified 143 million complex, repetitive transactions annually across 201 services involving data exchange and decision-making.
• Applied the AST (Automatability Score Threshold) index to classify tasks as highly automatable based on routine nature and cognitive complexity.
• Used the PGenAI rubric to assess generative AI readiness of tasks, focusing on natural language and document processing.
• Developed a predictive model to estimate transaction volumes for services with incomplete data, using service characteristics and historical patterns.
• Assessed task-level automatability using the ISCO-08 task classification system, adjusted for public sector relevance.

Experimental results

Research questions

• RQ1 What is the total volume of complex, repetitive citizen-facing transactions conducted by UK central government annually?
• RQ2 To what extent are these transactions automatable using current AI technologies, particularly generative AI?
• RQ3 How can governments estimate transaction volumes without conducting time-consuming manual counts?
• RQ4 What is the potential productivity gain from automating these transactions, and how should AI investments be prioritized?
• RQ5 How does service turnover affect the longevity and scalability of automation initiatives?

Key findings

• UK central government conducts approximately one billion citizen-facing transactions annually across 377 services.
• Of these, 143 million are complex, repetitive transactions involving data exchange and decision-making, spread across 201 services.
• 84% of these 143 million complex transactions are classified as highly automatable using current AI technologies.
• Automating just one minute per transaction would save the equivalent of 1,200 person-years of work annually.
• A predictive model was developed to estimate transaction volumes for services with incomplete data, reducing reliance on manual measurement.
• High service turnover implies that automation should target general procedural workflows rather than specific services to ensure long-term relevance.