AI eDiscovery Review Workflow for Law Firms

AI-assisted eDiscovery can organize or prioritize records for review. It does not remove the need to define the collection, preserve evidence, resolve privilege and verify what is produced. The responsible attorneys need a protocol that fits the matter and the tool.

Define the scope before choosing the model

Identify custodians, sources, date ranges, issues and preservation requirements. Record what was collected and what remains unavailable. Keep a distinction between a document, an email family, a duplicate and an extracted attachment.

For federal civil litigation, consult the current Federal Rules of Civil Procedure, applicable court orders and local requirements. State proceedings and other matters can have different requirements. A general workflow cannot settle the permitted scope for every case.

Ask counsel to resolve the production form, privilege process and any required agreements before a system applies irreversible actions.

Distinguish the different tools

Traditional technology-assisted review, search, clustering, duplicate detection and generative summaries do different jobs. Do not describe all of them as a model trained on a fixed-size seed set. A platform's documented method determines what training and validation are appropriate.

A generated summary can omit a qualification while a relevance classifier can mislabel a document. Test those failures separately. A confidence score supplied by a system is not automatically a reliable probability of correctness.

A fictional pilot design

Suppose a fictional dispute concerns whether a supplier received a revised specification. Prepare a lawyer-reviewed test set with:

• An email containing the old specification.
• A later email with the revision attached.
• A forwarded message quoting only part of the exchange.
• A document with a similar project name.
• A potentially privileged message.
• An attachment the extraction process cannot read.

Ask the tool to identify potentially responsive records and explain which source supports each classification. A summary should identify the document and passage, not substitute for it.

The expected results come from attorney review. This is a proposed test design, not a report of client results or a statistically sufficient sample for every matter.

Write the review protocol

Include a route for uncertain records. If reviewers disagree about responsiveness, resolve the coding guidance before using those labels to judge the model.

Check the records the tool excludes

Reviewing only highly ranked documents cannot establish that the low-ranked population contains no responsive material. Design validation to assess missed records as well as incorrect inclusions. The sample method and conclusions need to match the actual population and the question being tested.

There is no universal acceptable accuracy percentage or seed-set count. A large pile of correct duplicate classifications should not hide a missed critical attachment. Investigate errors by type and consequence.

Keep privilege and production checks separate

AI can flag possible privilege indicators, but a label is not a legal determination. Have counsel direct privilege review and any log requirements. Do not confuse records withheld as privileged with documents produced to the other side.

Before production, check the approved population against the export. Verify redactions in the exported files, attachment relationships, identifiers, extraction failures and the agreed format. Preserve a reconciliation record so an unexplained missing or extra record can be traced.

Evaluate costs without a volume rule

Compare processing, hosting, licensing, training, review, quality checks and export costs for the actual matter. Neither ten thousand nor one million documents creates an automatic tool requirement. Sensitivity, complexity and obligations can matter more than count.

Pilot a defined task, record observed errors and let the responsible attorneys decide whether the evidence supports expanding its use.