VP of Claims
Litigation Management
What You Do Today
Oversee litigated claims — outside counsel management, settlement authority, trial strategy, and litigation spend. Litigation costs can make or break a claim outcome.
AI That Applies
AI litigation analytics that predict case outcomes based on venue, opposing counsel, claim type, and similar case history. Automated litigation hold management and spend monitoring.
Technologies
How It Works
For litigation management, the system draws on the relevant operational data and applies the appropriate analytical models. 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. The strategy.
What Changes
Case outcome predictions inform settlement decisions. The AI shows that cases with this venue, this plaintiff's attorney, and these facts settle at a specific range — grounding your authority decisions in data.
What Stays
The strategy. Deciding when to settle and when to try a case, managing outside counsel relationships, and making authority calls on seven-figure settlements requires claims leadership.
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 litigation management, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
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.
Start These Conversations
Who to talk to and what to ask
your board chair or lead independent director
“What data do we already have that could improve how we handle litigation management?”
They shape expectations for how AI appears in governance
your CTO or CIO
“Who on our team has the deepest experience with litigation management, and what tools are they already using?”
They own the technology infrastructure that enables AI adoption
a peer executive at a company further along on AI adoption
“If we brought in AI tools for litigation management, what would we measure before and after to know it actually helped?”
Their lessons learned are worth more than any consultant's framework
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.