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General Counsel

Litigation Management

Enhances✓ Available Now

What You Do Today

Oversee pending and threatened litigation — coordinate with outside counsel, manage discovery, evaluate settlement vs. trial decisions, and control legal spend.

AI That Applies

Litigation analytics that predict case outcomes based on judge history, jurisdiction, claim type, and opposing counsel track record. AI-assisted document review for discovery.

Technologies

How It Works

For litigation management, the system review for discovery. Predictive models fit to historical outcome data identify which variables are the strongest leading indicators, then apply those weights to current inputs to generate forward-looking scores. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

Discovery costs drop dramatically with AI document review. Outcome prediction helps you make data-informed settle-or-fight decisions instead of relying purely on outside counsel's gut.

What Stays

Litigation strategy. When to be aggressive, when to settle, and how to protect the company's reputation while managing financial exposure.

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 litigation management, understand your current state.

Map your current process: Document how litigation management works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Litigation strategy. 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 Predictive Analytics 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 litigation management 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 litigation management?

They set the firm's AI adoption posture

your legal technology manager

Who on our team has the deepest experience with litigation management, 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 litigation management, 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.