Why review boundaries matter more than model capability in legal AI

The useful distinction in legal AI is not simply model capability. It is the review boundary: the defined line between work a system may handle, work a human must review and work that stops for senior escalation.

That boundary is a design decision. A workflow can permit a first pass on a known document type while requiring review when the document contains an unfamiliar clause, a changed commercial structure or an issue outside the approved playbook. The system’s role is to identify the boundary crossing. It is not to turn an unfamiliar exception into a confident answer.

The trade-off is unavoidable. A boundary set too widely sends more work through automation but gives monitoring more responsibility. A boundary set too narrowly sends more matters to people and reduces the amount of work the system can handle. Neither setting is permanent: the boundary changes as the playbook, risk tolerance and evidence of failure change.

This is where accountability sits in an AI-native operation. The important questions are not only which model is used, but who writes the escalation rules, who can change them and what happens when no rule matches. A sound workflow makes those decisions visible. It treats a refusal, a stop and a human handoff as designed outcomes rather than defects to be optimised away.