Legal Services · Litigation & Dispute Resolution

Review documents in discovery

Automates→Stable

Available Now

Production-ready. Commercial solutions exist and organizations are actively deploying.

Trajectories describe the observable direction of human effort — not a prediction about specific roles, headcount, or individual careers.

What You Do Today

Document review teams examine thousands to millions of documents for relevance, privilege, and responsiveness — the largest cost center in litigation.

AI Technologies

Technology-assisted review (TAR)Predictive codingContinuous active learning

Roles Involved

Who works on this

Digital Strategy LeaderDigital Transformation LeaderChief Data OfficerChange Management LeadInnovation LeadAI/ML Strategy LeadOperating Model DesignerVendor / Technology Partner ManagerLitigation AssociateParalegalEnterprise Architect

VP/SVPDirectorManager/SupervisorIndividual ContributorCross-Functional

How It Works

AI classifies documents for relevance and privilege using machine learning trained on senior attorney decisions — reducing the volume requiring human review by a substantial proportion.

What Changes

First-pass review is AI-driven; humans review the edge cases and validate AI decisions instead of reading every document.

What Stays the Same

Privilege determinations, strategic review decisions, and the judgment about what documents help or hurt your case.

Cross-Industry Concepts

document-classification predictive-analytics

Evidence & Sources

•Relativity
•Everlaw
•Reveal-Brainspace

Sources listed are directional references, not formal citations. Verify against primary sources before using in business cases or presentations.

Last reviewed: March 2026

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for review documents in discovery, document your current state in litigation & dispute resolution.

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Map your current process: Document how review documents in discovery works today — who does what, how long each step takes, and where the bottlenecks are. Use your matter management system data to establish a factual baseline.

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Identify the judgment calls: Privilege determinations, strategic review decisions, and the judgment about what documents help or hurt your case. — these are the boundaries AI won't cross. Know them before you start.

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Check your data readiness: AI tools for litigation & dispute resolution need clean, accessible data. Check whether your matter management system has the historical data, integrations, and quality to support Technology-assisted review (TAR) tools.

Without a baseline, you can't tell whether AI actually improved review documents in discovery or just changed who does it.

Define Your Measures

What to track and how to calculate it

matter cycle time

How to calculate

Measure matter cycle time for review documents in discovery before and after AI adoption. Pull from your matter management system.

Why it matters

This is the most direct indicator of whether AI is adding value to litigation & dispute resolution.

outside counsel spend

How to calculate

Track outside counsel spend using the same methodology you use today. Don't change how you measure just because you changed how you work.

Why it matters

Speed without quality is just faster mistakes. Measure both together.

When to check: Check after 30 days of consistent use, then quarterly.

The commitment: Give new tools at least 30 days before judging. The first week is always awkward.

What NOT to measure: Don't measure AI adoption rate as a goal. Measure outcomes. If the tool helps with review documents in discovery, people will use it.

Start These Conversations

Who to talk to and what to ask

General Counsel or Managing Partner

“What's our plan for AI in litigation & dispute resolution? Are we piloting, planning, or waiting?”

This tells you whether to experiment quietly or push for formal investment in review documents in discovery.

your matter management system administrator or vendor

“What AI capabilities exist in our current matter management system that we're not using? Most platforms are adding AI features faster than teams adopt them.”

The cheapest AI adoption is the features already included in your existing license.

a practitioner in litigation & dispute resolution at another organization

“Have you deployed AI for review documents in discovery? What worked, what didn't, and what would you do differently?”

Peer experience is more useful than vendor demos. Find someone who has actually done this.