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A Framework for Measuring Tech ROI in Litigation Finance

November 24, 2025

A Framework for Measuring Tech ROI in Litigation Finance

This article was contributed by Ankita Mehta, Founder, Lexity.ai – a platform that helps litigation funds automate deal execution and prove ROI.

How do litigation funders truly quantify the return on investment from adopting new technologies? It’s the defining question for any CEO, CTO or internal champion. The potential is compelling: for context, according to litigation funders using Lexity’s AI-powered workflows, ROI figures of up to 285% have been reported.

The challenge is that the cost of doing nothing is invisible. Manual processes, analyst burnout, and missed deals rarely appear on a balance sheet — but they quietly erode yield every quarter.

You can’t manage what you can’t measure. This article introduces a pragmatic framework for quantifying the true value of adopting technology solutions, replacing ‘low-value’ manual tasks and processes with AI and freeing up human capital to focus on ‘high-value’ activities that drive bottom line results  .

A Pragmatic Framework for Measuring AI ROI

A proper ROI calculation goes beyond simple time savings. It captures two distinct categories:

  1. Direct Cost Savings – what you save
  2. Increased Value Generation – what you gain

The ‘Cost’ Side (What You Save)

This is the most straightforward calculation, focused on eliminating “grunt work” and mitigating errors.

Metric 1: Direct Time Savings — Eliminating Manual Bottlenecks 

Start by auditing a single, high-cost bottleneck. For many funds, this is the Preliminary Case Assessment, a process that often takes two to three days of an expert analyst’s time.

The calculation here is straightforward. By multiplying the hours saved per case by the analyst’s blended cost and the number of cases reviewed, a fund can reveal a significant hard-dollar saving each month.

Consider a fund reviewing 20 cases per month. If a 2-day manual assessment can be cut to 4 hours using an AI-powered workflow, the fund reallocates hundreds of analyst-hours every month. That time is now moved from low-value data entry to high-value judgment and risk analysis.

Metric 2: Cost of Inconsistent Risk — Reducing Subjectivity 

This metric is more complex but just as critical. How much time is spent fixing inconsistent or error-prone reviews? More importantly, what is the financial impact of a bad deal slipping through screening, or a good deal being rejected because of a rushed, subjective review?

Lexity’s workflows standardise evaluation criteria and accelerate document/data extraction, converting subjective evaluations into consistent, auditable outputs. This reduces rework costs and helps mitigate hidden costs of human error in portfolio selection.

The ‘Benefit’ Side (What You Gain)

This is where the true strategic upside lies. It’s not just about saving time—it’s about reinvesting that time into higher-value activities that grow the fund.

Metric 3: Increased Deal Capacity — Scaling Without Headcount Growth

What if your team could analyze more deals with the same staff? Time saved from automation becomes time reallocated to new higher value opportunities, dramatically increasing the value of human contributions.

One of the funds working with Lexity have reported a 2x to 3x increase in deal review capacity without a corresponding increase in overhead. 

Metric 4: Cost of Capital Drag — Reducing Duration Risk 

Every month a case extends beyond its expected closing, that capital is locked up. It is “dead” capital that could have been redeployed into new, IRR-generating opportunities.

By reducing evaluation bottlenecks and creating more accurate baseline timelines from inception, a disciplined workflow accelerates the entire pipeline. 

This figure can be quantified by considering the amount of capital locked up, the fund’s cost of capital, and the length of the delay. This conceptual model turns a vague risk (“duration risk”) into a hard number that a fund can actively manage and reduce.

An ROI Model Is Useless Without Adoption

Even the most elegant ROI model is meaningless if the team won’t use the solution. This is how expensive technology becomes “shelf-ware.”

Successful adoption is not about the technology; it’s about the process. It starts by:

  1. Establish Clear Goals and Identify Key Stakeholders: Set measurable goals and a baseline. Identify stakeholders, especially the teams performing the manual tasks- they will be the first to validate efficiency gains.
  2. Targeting “Grunt Work,” Not “Judgment”: Ask “What repetitive task steals time from real analysis?” The goal is to augment your experts, not replace them.
  3. Starting with One Problem: Don’t try to “implement AI.” Solve one high-value bottleneck, like Preliminary Case Assessment. Prove the value, then expand. 
  4. Focusing on Process Fit: The right technology enhances your workflow; it doesn’t complicate it.

Conclusion: From Calculation to Confidence

A high ROI isn’t a vague projection; it’s what happens when a disciplined process meets intelligent automation.

By starting to measure what truly matters—reallocated hours, deal capacity, and capital drag—fund managers can turn ROI from a spreadsheet abstraction into a tangible, strategic advantage.

By Ankita Mehta Founder, Lexity.ai — a platform that helps litigation funds automate deal execution and prove ROI.

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