Data Foundations for AI in 2027: Testable Predictions for UK and Irish Organisations

Around a third of UK businesses now use artificial intelligence, but only one in ten of those uses it extensively. This outlook argues that the state of organisations’ records is one reason. Unstructured, legacy and paper records limit how far AI can move from pilot to production, and how well its use can be audited. Official surveys rank unclear use cases, cost and skills as bigger barriers, and the paper says so. It sets out five predictions for the end of 2027, drawing on data from the Office for National Statistics and the Central Statistics Office in Ireland, a BMJ investigation into paper records in National Health Service trusts, a Bank of England and Financial Conduct Authority survey, audit reports, and current UK, Irish and EU regulation. Each prediction names its data source, its threshold and the result that would prove it wrong. The coding rules for one analysis of public sector tender notices are registered in advance. A scored review will follow by December 2028.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-29
DOI
https://doi.org/10.5281/zenodo.23768050
Primary Topic
E-Government and Public Services
Type
article
Field-Weighted Citation Impact
0.00
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article

Data Foundations for AI in 2027: Testable Predictions for UK and Irish Organisations

Damien Andrews
4 links
Zenodo (CERN European Organization for Nuclear Research)
E-Government and Public Services
article

Data Foundations for AI in 2027: Testable Predictions for UK and Irish Organisations

Damien Andrews
article en

Abstract

Around a third of UK businesses now use artificial intelligence, but only one in ten of those uses it extensively. This outlook argues that the state of organisations’ records is one reason. Unstructured, legacy and paper records limit how far AI can move from pilot to production, and how well its use can be audited. Official surveys rank unclear use cases, cost and skills as bigger barriers, and the paper says so. It sets out five predictions for the end of 2027, drawing on data from the Office for National Statistics and the Central Statistics Office in Ireland, a BMJ investigation into paper records in National Health Service trusts, a Bank of England and Financial Conduct Authority survey, audit reports, and current UK, Irish and EU regulation. Each prediction names its data source, its threshold and the result that would prove it wrong. The coding rules for one analysis of public sector tender notices are registered in advance. A scored review will follow by December 2028.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 4%
E-Government and Public Services
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