DSIT - Revelio Labs

Key Details

Description

This procurement is a proportionate, time-limited 12-month data access arrangement designed to meet an urgent evidence need for DSIT/AI Security Institute. The requirement is for access to an existing commercial workforce intelligence dataset, rather than consultancy or bespoke analytical services, and will enable DSIT to test, validate and strengthen the evidence base on AI adoption and labour market impacts. The arrangement is capped at £80,000, with no extension options or open-ended spend commitment, and will be managed within existing programme budgets and governance. DSIT's AI Security Institute is looking to direct award Revelio Labs for workforce intelligence data to support DSIT's research programme on AI adoption and its impact on the labour market, with a particular focus on skill dynamics, workforce composition, and career trajectories. Objectives: 1.  Understand how AI adoption reshapes skill demand and supply at the firm level. By acquiring granular, firm-level workforce data (job postings, employee profiles, skill taxonomies, and workforce flows), DSIT's AI & the Future of Work Unit will measure how skill composition evolves at firms actively adopting AI compared to those that are not. 2.  Provide an empirical benchmark for AI exposure measurement. Workforce intelligence data enables DSIT to identify which skills actually co-occur with AI adoption at the firm level, providing ground-truth validation of theoretical classifications and improving future measurement frameworks. 3.  Replicate and extend published research on early-career labour market outcomes. To credibly replicate, validate, and extend these estimates for UK policy purposes, DSIT requires access to comparable data of equivalent granularity and structure. 4.  Inform UK workforce and education policy. The evidence generated will directly support policy advice on (examples): which skills retraining programmes should prioritise; where skill gaps are emerging; how curricula should adapt to changing employer expectations; and whether skill dynamics can serve as leading indicators of broader labour market disruption. 5.  Establish early warning capability. By tracking skill shifts that may precede larger employment effects, DSIT aims to build an analytical early warning system that identifies workforce risks before they become visible in aggregate official statistics. The Arrangement is intended to be entered into by the Secretary of State for Science, Innovation and Technology. It is anticipated that the rights and liabilities of the Secretary of State for Science, Innovation and Technology, including those arising under this Arrangement once executed, will in due course transfer to the Secretary of State for the Cabinet Office pursuant to an Order in Council (secondary legislation) made under section 2 of the Ministers of the Crown Act 1975.