Big law firms such as Herbert Smith Freehills Kramer have quietly accumulated vast archives of contracts, depositions and legal opinions over decades. Those materials once looked like routine documents, not data.
The firm’s inaugural chief artificial intelligence officer now treats every clause, sentence and concept as a discrete data point. That mindset reframes how the firm structures information and underpins its broader AI strategy.
Legal documents historically lived in document management systems, organised by matters, clients or practice areas rather than by the information inside them. Under the new AI-led approach, content is tagged and structured so software can identify patterns across thousands of agreements.
Contract terms, negotiation positions and case outcomes become searchable inputs rather than static records. The firm can train models, automate review tasks and surface precedents faster.
Treating text as data clarifies why categorisation now matters so much. How the firm labels clauses, issues and outcomes shapes what AI tools can learn and recommend.
Better structure enables more accurate contract analysis, smarter knowledge retrieval and more tailored advice to clients. The firm’s leadership frames this as a strategic reorientation of how legal work product is captured, understood and reused.
