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Litigation Associate

Review documents in discovery

Enhances✓ Available Now

What You Do Today

Review thousands of documents for relevance, privilege, and responsiveness — the most time-intensive phase of litigation

AI That Applies

TAR/predictive coding classifies documents using ML, reducing the volume requiring human review by 60-80%

Technologies

RelativityEverlawReveal

How It Works

For review documents in discovery, the system review by 60-80%. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

You review AI-flagged edge cases instead of reading every document; more time for substantive analysis of key evidence

What Stays

Privilege calls, strategic assessment of damaging documents, and the pattern recognition that builds a case theory from evidence

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.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for review documents in discovery, understand your current state.

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Map your current process: Document how review documents in discovery works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
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Identify the judgment points: Privilege calls, strategic assessment of damaging documents, and the pattern recognition that builds a case theory from evidence. These are the boundaries AI won't cross.
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Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support Relativity tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long review documents in discovery takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

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 KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your general counsel or managing partner

“What data do we already have that could improve how we handle review documents in discovery?”

They set the firm's AI adoption posture

your legal technology manager

“Who on our team has the deepest experience with review documents in discovery, and what tools are they already using?”

They manage the tools and can show you capabilities you don't know exist

a client who's adopted AI in their legal department

“If we brought in AI tools for review documents in discovery, what would we measure before and after to know it actually helped?”

Their expectations for outside counsel are shifting

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.

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