TL;DR

A joint study from the Open Data Institute and Nortal reveals that whilst many UK councils are piloting AI for demand forecasting and service improvement, most lack the data infrastructure required. The report identifies three distinct types of AI readiness and calls for councils to treat data standards as strategic assets.

Building AI on Weak Foundations

Local authorities across the UK are experimenting with artificial intelligence to forecast demand, reduce costs, and improve public services. However, new research suggests many are building on unstable ground. The joint study from the Open Data Institute (ODI) and technology company Nortal analysed ten council cases from Dorset to Leeds, finding that most datasets remain fundamentally unsuitable for algorithmic applications.

The report, Insights from UK councils on standards, readiness and reform to modernise public data for AI, draws on interviews with council leaders, technical teams, and programme partners. Its central finding is stark: councils making tangible progress share one common trait—they approach data standards and infrastructure as strategic assets rather than technical afterthoughts.

Three Paths to AI Readiness

The research identifies three complementary forms of readiness, each requiring different data conditions. Search readiness relies on structured, discoverable data with canonical identifiers and clear metadata. Machine learning readiness depends on reproducible datasets, transparent lineage, and bias documentation. Generative AI readiness requires context-rich content, segmentation, and APIs that allow models to retrieve and reason across datasets.

Professor Elena Simperl, director of research at the ODI, explained: “What works for search might not work for predictive modelling, and what works for predictive modelling won’t suit generative AI. Our framework moves the debate from asking whether councils are AI-ready to asking ready for what purpose.”

Looking Forward

The report’s authors argue councils must move beyond pilots and invest in data quality, interoperability, and governance as shared foundations. Familiar barriers persist across most authorities: inconsistent identifiers, missing metadata, and limited API access. These shortcomings not only slow innovation but make it difficult to audit AI outputs or retrain models as circumstances change. As Priit Liivak, Chief Government Technology Officer at Nortal, noted: “AI success starts with the data architecture, not the applications.”

Source: Think Digital Partners