Data maturity is essential for any organisation thinking about using AI. Before data can be considered AI ready, there needs to be confidence that it is well managed, thoroughly understood, and supported by the right people, processes and culture.

Government Digital Service (GDS) has partnered with The National Archives to explore whether and how their legal data can be prepared for AI use. Our partnership, which completed its discovery phase in April 2026, used data maturity as a framework for understanding The National Archives’ strengths and opportunities before committing to any technology approach. We are now moving into an Alpha phase. This will explore how data maturity mitigates the risks of exposing data to AI, and test potential approaches as the work progresses.

What is data maturity, and why does it matter?

Data maturity is about ensuring data is good enough to do what it needs to do, and that the people, processes and governance around it are capable of meeting those needs. AI depends on good data for good outputs. AI readiness requires confidence that data can be used safely, ethically and responsibly.

Data is only as effective as the people who manage it. Poor quality data produces unreliable AI outputs, but good data that is poorly understood or poorly governed cannot be considered AI ready either. Data maturity is where any AI readiness assessment should start.

What we set out to do with The National Archives

The purpose of the discovery phase was to explore whether The National Archives' legal data, which includes legislation and case law, could be optimised for AI use, and whether AI could be relied on when it matters most to citizens and businesses. This is part of GDS' mission to ensure public sector data is managed as a strategic national asset.

What we found

The discovery phase produced three main findings, the third of which represents the most significant opportunity for government. First, The National Archives' legal data is already close to being AI ready. The data quality is high, and The National Archives demonstrates strong data maturity, with the right skills, leadership and cultures in place. Good data managed poorly is not AI ready. The National Archives' strength is that both the data and the organisation surrounding it meet the bar.

Second, technology is moving very quickly. Large technology companies and other governments are working on the same problems. Public sector organisations building AI services need to account for how rapidly the landscape is changing and avoid over-investing in solutions that may be superseded.

Third, we believe the most significant opportunity for government in the next phase is exploring how AI generated outputs can be evaluated and validated. This is where government can add real value by building the tools and standards to assess whether AI outputs are trustworthy, rather than replicating what the private sector is already doing.

Technology moves faster than data projects

Had the GDS and The National Archives partnership gone straight to building an AI chatbot for legislation and case law, we could have found ourselves solving a problem the world's largest AI companies are already working on, with a fraction of the budget.

Instead, we used data maturity as a framework for understanding The National Archives' strengths and areas for development, helping us identify longer-term opportunities less vulnerable to rapid shifts in technology. This included further work on Model Context Protocol (MCP), an open source standard for connecting AI applications to external systems such as databases or other tools and documents.

Work with partners to solve shared problems

One of The National Archives' important organisational strengths, which would not appear in any analysis of its data sets alone, is its culture of responsible innovation. Staff with deep expertise and strong leadership are willing to explore new opportunities while being aware of the risks of moving too fast.

To test how legal data could be exposed via MCP, we partnered with the Department for Business and Trade and the Ministry of Justice to run a hackathon bringing together 40 lawyers, policymakers, data scientists and engineers from academia, the Civil Service and small and medium-sized enterprises (SMEs).

Early evidence indicated that MCP makes a significant positive difference to the quality of AI outputs. The next phase of the GDS and The National Archives partnership will explore this further, building on GDS guidance and best practice for making government data sets ready for AI.

A model with potential for wider application

This discovery is a pilot that can be replicated. It provides an example of how a data maturity-led approach can support decisions about AI. GDS will shortly launch a revised data maturity service, building on the framework published in 2023.