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Paralegal

Process and review discovery documents

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What You Do Today

Receive document productions, load into review platforms, apply coding protocols, perform first-level review for relevance and privilege, and prepare privilege logs.

AI That Applies

eDiscovery AI performs technology-assisted review, applying relevance and privilege coding, clustering similar documents, and generating draft privilege log entries with attorney review.

Technologies

How It Works

For process and review discovery documents, the system draws on the relevant operational data and applies the appropriate analytical models. 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

Document review volume is dramatically reduced. AI performs first-pass review, and you focus on the documents AI flags as borderline or requiring human judgment.

What Stays

You still manage the review workflow, handle quality control sampling, prepare documents for attorney review, and exercise judgment on privilege calls that require context.

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 process and review discovery documents, understand your current state.

Map your current process: Document how process and review discovery documents works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: You still manage the review workflow, handle quality control sampling, prepare documents for attorney review, and exercise judgment on privilege calls that require context. These are the boundaries AI won't cross.
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 Technology-Assisted Review 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 process and review discovery documents 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

Which steps in this process are fully rule-based with no judgment required?

They set the firm's AI adoption posture

your legal technology manager

What's the error rate on the manual version, and what would "good enough" look like from an automated version?

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

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.