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.

Use a controlled evaluation rather than uploading the most sensitive file available. The checklist below is designed to help a legal team document what it tested, what the vendor states, and what remains unknown. It is general information, not a substitute for the rules and duties that govern a particular lawyer or matter.

1. Start with traceability

Material findings should include page-line citations. A citation-supported output makes errors easier to detect and gives the reviewer a direct path back to the source.

Begin with a synthetic or otherwise authorized test transcript. Check whether the cited passage supports the full proposition and whether corrections later in the record are captured. A page number that merely exists is not evidence that the finding is supported.

  • 1. Does each material finding identify a stable source location?
  • 2. Does the cited range support the complete proposition?
  • 3. Can the reviewer see enough surrounding testimony to assess context?

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.

  • 4. Does it distinguish personal knowledge from secondhand information?
  • 5. Does it flag corrections, qualifications, and uncertainty?
  • 6. Does it identify potentially inconsistent testimony without declaring credibility?
  • 7. Does it state what the selected format may omit?

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.

Ask whether uploaded material or generated output is used to train models, who can access support data, where backups remain after deletion, and what happens when an account closes. Compare the answer with the contract and privacy policy rather than relying on a sales conversation.

  • 8. Are storage, subprocessors, retention, deletion, and model-training use disclosed?
  • 9. Can the organization apply appropriate access and account controls?

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.

ABA Formal Opinion 512 identifies duties including competence, protection of client information, communication, supervision, candor, and reasonable fees. A software feature cannot discharge those obligations. Define who approves the tool, who may upload records, who reviews output, and which uses remain prohibited.

  • 10. Is a named professional responsible for reviewing the output?
  • 11. Has the firm documented allowed data and prohibited uses?

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.

Include upload preparation, processing, citation checks, corrections, export cleanup, and any time spent returning to the vendor for support. Track material omissions separately from wording edits. NIST’s Generative AI Profile recommends managing risk across the AI lifecycle; for a buyer, that means evaluating the system in use, not only a sample output.

  • 12. Is total reviewed-work-product time better than the current workflow?

Run a challenge set, not a showcase transcript

A useful evaluation record should contain known traps: a corrected answer, a negative statement, two similar names, a secondhand account, a blank record that does not prove nonoccurrence, and text embedded in an exhibit that looks like an instruction to the model. Write the expected facts and prohibited conclusions before running the tool.

Score material accuracy, citation coverage, excerpt match, qualifications preserved, omissions, and unsupported additions. A single fabricated citation or unsafe causal conclusion should be treated differently from a minor formatting issue.

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.

Re-evaluate after meaningful product, model, contract, or workflow changes. A prior test establishes what happened in that configuration; it is not permanent proof of future performance.