AI document review can help sort files, compare versions, or produce a first-pass summary. It can also miss a clause, misread a scan, or make a summary sound more certain than the source supports. Introduce it as a bounded aid with a named reviewer, a clear source record, and a way to identify errors.
Choose a low-risk pilot using documents the firm is authorized to process. Define the task narrowly, such as flagging differences between two versions of a standard internal checklist. Before the pilot, record how staff perform the task now and what a useful result would look like. Have a reviewer check the AI output against the actual documents, recording missed items, false alerts, and the time spent correcting results. Include poorly formatted scans or tables in the review if those are common in the firm’s files.
For documents that affect scope, design, safety, regulatory compliance, or contractual rights, keep a qualified person responsible for interpretation and decisions. Require reviewers to open the cited source passage rather than rely on a summary. Confirm that the system handles version dates correctly, retains an audit trail, and can distinguish the current document from superseded material. Establish an escalation path for conflicting clauses or uncertain output.
An owner can ask the project manager which document types create repeated review effort, then ask the quality lead what errors would be unacceptable. Ask security staff to confirm data handling and ask counsel to check relevant client or contract restrictions. Keep the original records and the firm’s review notes in the project file according to normal procedures. Do not treat an automated summary as the project record or assume every important item was found.
Expand only after the pilot results show that the process is useful and its review burden is understood. Contracts, licensed PE judgment, and local codes govern.
This is general education, not legal or engineering advice.
