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Most conversations about legal tech ROI start with time saved or work done faster. But for plaintiffs' firms operating on contingency, there’s even more to the story: stronger case strategies, higher settlement values, and better client outcomes.
Here’s the whole story. By boosting case values and settlement amounts, AI directly contributes to a firm’s profitability, as fees are typically a percentage of the recovery. Even if outcomes are not always easily quantifiable, qualitative improvements—such as more consistent demand packages or better-informed case strategies—help maximize each case's value. These improved settlement results are a core component of AI’s return on investment (ROI) in plaintiff practices and often go hand-in-hand with time savings, as faster information processing can lead to both earlier and higher settlements.
Here’s how to calculate ROI from qualitative improvements.
Track Increases in Average Settlement Amount
One of the clearest signals of a positive return on investment (ROI) is an increase in the average settlement value per case. Tracking should begin before your firm adopts AI tools to establish a baseline comparison.
Tracking Timeline:
How Legal AI Contributes to Increased Average Settlement
AI tools can help increase settlements by enhancing key aspects of case preparation and communication. For example, when drafting demand letters or case intake and evaluation, these tools help legal professionals strategically frame the strongest facts up front for maximum impact.
They can also tailor the tone of communications to be most persuasive with a specific opposing counsel or insurer. Furthermore, AI can efficiently cite the most relevant precedents or statutes to substantiate demands.
In addition to increasing efficiency, AI fundamentally improves your case outcomes. By conducting more thorough analyses, identifying overlooked damages, and ensuring no critical evidence slips through the cracks, AI-powered firms can handle larger caseloads without sacrificing quality. The result? A measurable increase in return on investment that goes straight to your bottom line.
When measuring increased ROI, the formula is straightforward:
Compare the average pre-AI settlement value to the post-AI settlement value. After establishing a baseline timeline, compare.
Example:
Control for case type (e.g., compare PI-to-PI or employment-to-employment). In the interest of calculating meaningful averages, you can break this comparison down by practice area, case type, or other criteria.
Example calculation by case type:
Case Type: Medical Malpractice
Pre-AI avg. settlement: $425k Yearly
Post-AI avg. settlement: $495k Yearly
Increase per case: $70k = 16.5% Improvement
Multiply the increase ($70k) by the number of cases closed per year (10) as seen below:
Medical Malpractice:
Monitor Early Settlements at Higher Values
AI-generated demand letters that are well-organized, persuasive, and fully supported by evidence often lead to earlier settlements—and sometimes at better terms.
This is especially valuable in contingency-fee practices, where each month a case remains open means sunk cost. A better-structured demand letter can prompt defendants to settle before discovery or trial prep, reducing overhead while locking in value.
How to Measure Earlier Settlements with Better Terms:
Capture Damages You Previously Missed
Traditional case review often operates under time pressure and human limitations. Even experienced attorneys can overlook valuable damage elements buried in extensive documentation, miss subtle patterns across multiple incidents, or fail to fully quantify complex future damages. AI tools systematically analyze every piece of evidence, ensuring no compensable element goes unclaimed. AI tools can surface elements of a case that might otherwise go unclaimed—especially in personal injury or labor and employment matters.
An example of a pattern AI-powered analysis can identify, includes:
Hidden Wage Loss Patterns:
When AI helps uncover and properly frame these damages, the total claim value rises.
How to measure the increased case value from previously-missed data:
Example:
If 1 in 3 cases now includes an extra $5,000 in documented wage loss or medical damages, and you settle 100 cases/year, that’s $165,000 in additional revenue at a 33% fee rate.
Reduce Write-Offs and Negotiation Concessions
When you can't immediately respond with solid proof, you end up making concessions. AI changes this dynamic completely by making sure you have every piece of evidence organized, analyzed, and ready to defend. Stronger, clearer demand letters reduce the likelihood of back-and-forth negotiation that chips away at settlement value. When your letter is:
…defendants have less room to devalue the claim or drag out talks. That translates to fewer settlement concessions and lower “leakage” between initial demand and final payout.
How to measure reduced concessions:
In Conclusion: Enhanced Legal Work and Higher Profits
For plaintiffs’ firms, ROI isn’t just about doing more work—it’s about doing better work that translates into higher-value cases and stronger financial outcomes. Legal AI is most potent when it sharpens your claims, tightens your argument, and makes your client’s needs harder to ignore. If you’re seeing even small bumps in settlement value across many cases, the return adds up quickly.
