Beyond Automation: Redesigning Jobs with LLMs to Enhance Productivity

Andrew Ledingham, Michael Hollins, Matthew Lyon, David Gillespie, Umar Yunis-Guerra, Jamie Siviter, David Duncan and Oliver P. Hauser

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Abstract: The adoption of generative artificial intelligence (AI) is predicted to lead to fundamental shifts in the labour market, resulting in displacement or augmentation of AI-exposed roles. To investigate the impact of AI across a large organisation, we assessed AI exposure at the task level within roles at the UK Civil Service (UKCS). Using a novel dataset of UKCS job adverts, covering 193,497 vacancies over 6 years, our large language model (LLM)-driven analysis estimated AI exposure scores of 1,542,411 tasks. By aggregating AI exposure scores for tasks within each role, we calculated the mean and variance of job-level exposure to AI, highlighting the heterogeneous impacts of AI, even for seemingly identical jobs. We then use an LLM to redesign jobs, focusing on task automation, task optimisation, and task reallocation. We find that the redesign process leads to tasks where humans have comparative advantage over AI, including strategic leadership, complex problem resolution, and stakeholder management. Overall, automation and augmentation are expected to have nuanced effects across all levels of the organisational hierarchy. Most economic value of AI is expected to arise from productivity gains rather than role displacement. We contribute to the automation, augmentation and productivity debates as well as advance our understanding of job redesign in the age of AI.

Date: 2025-12
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