Industry Temperature Check: Barriers and Enablers to AI Assurance
This report by the UK government's Centre for Data Ethics and Innovation (CDEI) summarises findings from industry engagement activities regarding the barriers and enablers to AI assurance. It examines cross-sectoral themes as well as specific challenges in three key sectors: HR and recruitment, finance, and connected and automated vehicles (CAV). The publication identifies practical interventions to support the uptake of AI assurance mechanisms and standards in the UK ecosystem.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
This document is for policymakers, regulators, industry leaders, and AI developers seeking to understand the landscape and adoption barriers of AI assurance in the UK.
What the document contains
The document covers industry perspectives on AI assurance, identifying cross-sectoral themes, common barriers such as skills shortages and regulatory uncertainty, and proposed interventions like repositories, guidance, and regulatory clarity. It also details specific findings and barriers within human resources and recruitment, financial services, and connected and automated vehicles.
It does not provide legally binding regulations or mandatory technical compliance rules, serving instead as an exploratory temperature check and roadmap input.
- AI assurance is viewed by industry as an important element of wider organizational risk management.
- Organizations express a strong desire for third-party certification, standardized guidance, and clarity around future regulatory frameworks.
- Key workforce barriers include a general lack of knowledge, skills, and internal awareness regarding AI risks and available assurance techniques.
- Small and medium-sized enterprises face distinct resource and financial barriers when attempting to adopt labor-intensive technical standards.
- Sector-specific challenges differ significantly, requiring tailored interventions for HR, finance, and connected and automated vehicles.
· Overview · General findings · Key themes · Barriers to AI assurance · Interventions · Sector-specific findings · HR and recruitment · Finance · Connected and automated vehicles · Looking towards the future · Methodology
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
2/5Clear responsibility for AI outcomes
Define legal persons’ responsibility for AI governance
Overview
screening score · 2026-10-23
Fairness
2/5Non-discrimination and equitable treatment
Embed considerations of fairness into AI
Overview
screening score · 2026-10-23
Human Dignity
1/5Respect for the inherent worth of persons
Digital exclusion
Overview
screening score · 2026-10-23
Human Oversight
0/5Humans remain in control of consequential decisions
screening score · 2026-10-23
Privacy
1/5Protection of personal data and private life
Potential implications for data processing and data protection laws.
HR and recruitment: Overview
screening score · 2026-10-23
Transparency
2/5Openness about how AI systems work and are used
Make sure that AI is appropriately transparent and explainable
Overview
screening score · 2026-10-23
Enforcement
2/5Consequences and compliance mechanisms with real force
Regulatory compliance is a key driver of assurance
General findings: Key themes
screening score · 2026-10-23
Impact Assessment
4/5Required assessment of an AI system's risks/impacts before or during use
Providing a toolbox of assurance mechanisms for use with AI - such as technical and governance standards, impact assessments
Overview
screening score · 2026-10-23