Skip to content

eDiscovery Specialist

Plan and execute data collections

Enhances✓ Available Now

What You Do Today

Identify custodians, map data sources, conduct forensic collections from email, cloud, mobile, and collaboration platforms

AI That Applies

AI assists with data mapping, identifies additional data sources based on communication patterns, and automates collection from cloud platforms

Technologies

How It Works

The system ingests communication patterns as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output is a recommended plan or schedule that accounts for the identified constraints and optimization criteria.

What Changes

Data mapping is more comprehensive; AI identifies data sources you might miss (personal devices, ephemeral messaging, cloud collaboration)

What Stays

Collection protocol design, custodian communication, and the defensibility judgment that protects against spoliation claims

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 plan and execute data collections, understand your current state.

Map your current process: Document how plan and execute data collections works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Collection protocol design, custodian communication, and the defensibility judgment that protects against spoliation claims. 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 Nuix 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 plan and execute data collections 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's the current accuracy of our forecasting, and how would we know if an AI model is actually better?

They set the firm's AI adoption posture

your legal technology manager

Which historical data do we have that's clean enough to train a prediction model on?

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.