Building an AI-Ready Legal Function: Why Data Comes First

Before asking what AI can do for legal, a more important question needs to be asked: what data are we actually giving it to work with?

In many legal functions, Matter data is inconsistent, intake information varies by team, knowledge is spread across multiple systems with limited structure or governance, and reporting is often manual, retrospective and incomplete. 

Being “AI-ready” is not about owning advanced tools. It is about having clean, structured and trusted data that reflects how legal work is really done. Without this, AI outputs are harder to explain, trust and embed into decision-making.

We see this repeatedly in practice. When data is fragmented, AI tools struggle to distinguish low-risk from high-risk work, to identify patterns, or to deliver insights that leadership can rely on. When processes are unclear or inconsistent, AI does not deliver real value. 

This is why data must come first. Structured intake, consistent taxonomy, automated data flows and meaningful reporting are not “pre-AI admin”; they are the foundations that determine whether AI adds value or introduces new risk. Legal teams that invest in these fundamentals are able to use AI responsibly and safely. Those that rush ahead often find themselves retracing their steps.

So what does a more considered approach look like?

Improving data quality does not have to start with new tools. In many cases, the first steps involve creating clarity around how legal data should be captured, stored and managed, while longer-term improvements focus on systems, structure and data quality.

What legal teams can do now

A practical starting point is establishing clear principles and guidance around how legal data should be stored and managed. This includes defining where matters should be recorded, where documents and contracts should be saved, and what information should be captured at intake. Providing simple guidance on document locations, naming conventions and core matter attributes can quickly improve consistency across the function.

Centralising documents and contracts is another important step. When legal work and documentation sit across personal drives, email folders and multiple platforms, it becomes difficult to maintain visibility or generate reliable insight. Bringing documents and contracts into shared locations creates a clearer, more consistent foundation for managing legal information.

What to work towards over time

Over time, legal teams can build on these foundations through more structured approaches to managing legal data. This includes developing consistent taxonomies for matters, contracts and work types so that legal work can be categorised and analysed more effectively.

Longer-term improvements also include investing in systems that support structured data capture, alongside initiatives to clean and enrich existing datasets. As data quality improves, automation between systems can further reduce manual effort and help establish a more reliable and trusted source of legal information.

Taken together, these steps form a more resilient foundation for AI adoption – one that prioritises trust, explainability and long-term value over speed alone.

This approach allows legal teams to move towards AI adoption with greater confidence. By strengthening how legal data is captured, structured and maintained, organisations are better positioned to use AI tools in ways that are reliable, explainable and fit for purpose.

This article has been published as part of Konexo Insights: AI, legal transformation, post M&A integration and flexible in-house legal resourcing.