Most conversations about legal AI begin with drafting. It is an understandable starting point: the output is immediate, familiar and easy to demonstrate. But drafting alone is not a legal operating model.
Look at the complete workflow
A useful legal system must understand what happens before and after a document is written. A request arrives. Facts are gathered. Sources are checked. Positions are compared. Authority is established. A qualified person approves the work. Obligations are tracked after execution.
If AI assists only with the paragraph in the middle, the organisation may produce text faster while leaving the actual bottlenecks untouched.
Start with friction, not features
Good candidates for legal AI are workflows with repeated information retrieval, standard decision paths, structured review criteria or expensive coordination. Contract intake, precedent retrieval, matter summaries, compliance mapping and obligation tracking are typical examples.
The right first question is therefore not whether a model can draft. It is: where does legal work lose time, context or accountability, and what combination of knowledge, automation and human judgment can improve that system?
Design for professional responsibility
Legal AI should make its boundaries visible. Sources should be traceable. Permissions should follow roles. High-impact actions should require approval. Outputs should be reviewable, and the system should preserve a record of what happened.
This is how AI moves from an impressive demonstration to durable legal infrastructure.