Artificial Intelligence (AI) systems rely on high-quality, well-structured and accessible data— yet many government datasets were created for operational or compliance purposes rather than AI.
This legacy data landscape creates barriers to scale, slows innovation, and introduces legal and governance risks for example data may be scattered across separate systems and there may be no single responsible owner for quality or authorising access decisions.
The result? AI projects can stall or fail to scale beyond experimentation or initial pilot. These issues aren’t inevitable — they can be addressed by preparing data intentionally for AI use.
Making your dataset AI‑ready enables teams to innovate safely, efficiently and confidently – unlocking the full potential of AI to support better public services.
What does ‘AI-ready’ data mean?
AI readiness is context dependent, and not all datasets will need to meet the same standard. While existing frameworks – such as the Open Data Institute’s approach to AI-ready data – provide a useful foundation, organisations should consider the following aspects to help ensure their datasets are prepared effectively for AI:
Technical optimisation: AI systems work best with data in modern, machine-readable formats and accessible through APIs and scalable infrastructure. A well-prepared technical setup ensures your data can be processed efficiently and at scale. For example, converting a legacy spreadsheet into a structured format like JSON and providing an API endpoint for real-time access allows AI models to integrate and analyse data seamlessly.
Data and metadata quality: High-quality, complete and well-documented data is the foundation of reliable AI. Good data enables accurate learning and trustworthy outputs. For instance, a housing dataset should include consistent definitions for “affordable housing” and document regional variations, while metadata should specify update frequency and data lineage to provide clarity and context.
Organisation and infrastructure context: Clear ownership and access controls create accountability and confidence. Assigning responsibility for data quality and governing AI use ensures decisions are transparent and efficient, helping teams innovate without unnecessary delays.
Legal, security and ethical compliance: Data use must be lawful, secure and trustworthy. Embedding privacy safeguards and fairness considerations from the start builds trust and reduces risk. For example, confirming legal clarity on whether a dataset can be used for AI and applying proportionate security measures ensures compliance and avoids reputational harm while enabling responsible innovation.
What to do next
Read the Guidelines and best practices for making government datasets ready for AI to gain an understanding of what makes data AI-ready.
Identify datasets in your organisation that could support AI use cases.
Use the self-assessment checklist to evaluate whether your datasets are AI-ready.
Follow the AI-ready data action plan presented in the guidance to address technical, governance or legal barriers.
NOTE: This page will be further developed to include more practical advice from Guidelines and best practices for making government datasets ready for AI.
Contact ai-knowledge-hub@dsit.gov.uk with feedback on this page, or dataAIReadiness_incubator@dsit.gov.uk with questions about implementing the guidance.
Making data ready for AI