AI deposition-summary tools can reduce the time spent on the first pass through a transcript. Their value, however, depends less on how fluent the output sounds and more on whether the legal team can verify it, use it safely, and fit it into an accountable review process.

The right question is not whether a tool can produce a summary. It is whether the summary is traceable, appropriately scoped, and handled under terms that match the sensitivity of the material.

1. Start with traceability

Material findings should include page-line citations. A source-linked output makes errors easier to detect and reduces the cost of professional review.

Test the citations on a real but appropriate transcript before adopting any tool. Check whether the cited passage supports the full proposition and whether corrections later in the record are captured.

2. Evaluate what the tool leaves out

A short, polished summary may omit limitations, objections, unclear answers, or testimony that does not fit the dominant narrative. Ask how the tool handles uncertainty and conflicting passages.

  • Does it distinguish personal knowledge from hearsay?
  • Does it flag corrections and qualifications?
  • Does it identify potentially inconsistent testimony without declaring credibility?
  • Can the reviewer reach the source quickly?

3. Understand file handling

Deposition transcripts may contain privileged, confidential, personal, or commercially sensitive information. Review the service’s access controls, storage location, retention period, subprocessors, and deletion process.

Do not rely on a broad claim such as “secure” without understanding what the service actually does.

4. Keep a defined human-review step

Assign responsibility for checking names, dates, numbers, key quotations, page-line citations, and the surrounding context of material findings. The more important the intended use, the more rigorous the review should be.

5. Measure the whole workflow

A tool that saves drafting time but creates heavy verification work may not improve the process. Compare the total time from upload to a trusted work product, and track the kinds of corrections reviewers make.

A sensible adoption approach

Begin with lower-risk, text-searchable transcripts. Use a consistent review checklist. Record citation and context errors. Expand use only when the team understands both the benefits and the failure modes.