How to Use AI to Review a Proposal Without Losing the Redline

Start with two documents, not a vague request

A proposal review often begins with an innocent-looking request: “Tell me what changed.” Before asking an AI tool, identify the exact baseline and the exact revision. Label each with its date and owner, then keep untouched copies. Otherwise, a fluent summary may compare the wrong drafts or blur a client suggestion with an internal edit. The first useful output is a list of differences, not a rewritten proposal.

Ask the tool to group proposed changes by deliverables, timing, responsibilities, assumptions, and exclusions. Require it to quote the relevant passage from each version and identify the section where it appears. Treat “not found” as a valid answer. NIST’s generative AI profile describes the risk of confidently presented false content, which is a good reason to check every consequential observation against the documents themselves.

Separate an observation from a recommendation

A change from “two review rounds” to “revisions as needed” is observable; whether to accept it is a business decision. Have AI produce two columns: what the language says and what question the team should resolve. The second column might ask who decides when revisions are complete, whether the schedule allows for them, and whether the proposed wording matches the estimate.

For each flagged passage, assign a human owner: delivery lead for feasibility, finance for commercial implications, or the relationship owner for client intent. Ask reviewers to write a short rationale when they accept, reject, or counter a change. That rationale is more valuable than an unexplained clean copy when someone later asks why a commitment appeared in the final proposal.

Preserve the visible review trail

Use your document editor’s comparison or tracked-change tools alongside the AI analysis. Microsoft documents both comparison of two versions and review of individual tracked changes. Those tools show text-level edits; AI can help organize questions around their meaning. Neither should silently replace the other. Keep the comparison output separate from the source files, and inspect changes in tables, appendices, and footnotes as well as the main narrative.

When several people return separate copies, combine their revisions deliberately rather than pasting the most recent-looking text into a new file. Mark comments that are resolved, record open issues, and create one review copy for the next round. A reviewer should be able to answer three questions without reconstructing an email thread: what changed, who approved it, and which issue remains unsettled.

Review the final copy as a fresh document

After resolving redlines, ask AI for a consistency check across the complete proposal: do deliverables match the timeline, do responsibilities match the assumptions, and do exclusions contradict any promised outcome? Request references to exact sections rather than a pass-or-fail verdict. Read every flagged passage yourself, including apparent false alarms. A useful assistant raises questions; it does not approve client commitments.

Finally, compare the proposed final copy with the last reviewed version. Confirm that no unreviewed sentence slipped in during formatting or cleanup, then record which file is ready for the next step in your document workflow. This modest checkpoint helps keep “final” from becoming a moving target. It also leaves the team with a clear account of its decisions without suggesting that AI performed a legal or commercial review.

Sources and further reading

• NIST — AI RMF Generative AI Profile
• Microsoft Support — Compare document differences
• Microsoft Support — Track changes in Word