LOS ANGELES, CA (May 21, 2026) Hamid Kohan, founder and CEO of Legal Soft and Law Practice AI, appeared as a guest on The Tech-Savvy Lawyer podcast, Episode 136, where he discussed how plaintiff law firms can move beyond AI experimentation and start applying it to the workflows that matter most.
The episode, hosted by The Tech-Savvy Lawyer, covered practical applications of AI across three core areas of plaintiff practice: client intake, document collection, and demand letter drafting. Kohan addressed common adoption challenges, outlined where law firms are losing the most time, and explained what separates firms that see consistent results from those still experimenting. The full episode is available on Spotify and Youtube.
Intake, Documents, and Demand Letters: Where Kohan Says the Leverage Is
During the conversation, Kohan identified client intake as one of the highest-leverage starting points for AI adoption in a contingency fee practice. He noted that a missed lead is not just a missed call. In a contingency fee model, it is permanent, unrecoverable revenue. He described how AI-powered intake can qualify leads, collect incident details, and route cases automatically, running the same consistent process whether a call comes in during business hours or late at night.
On document collection, Kohan pointed to the imbalance between the time the workflow consumes and the legal judgment it actually requires. Record requests, follow-up calls, and file organization can absorb hours of attorney and paralegal time daily without requiring any legal expertise to execute. He described this as the most consistently automatable workflow in plaintiff practice because it is both repetitive and low-judgment.
For demand letters, Kohan drew a clear distinction between platforms that read the actual medical records in a case file and those that generate generic language from AI training data. He explained that when the language in a demand letter mirrors what the treating physician documented, it is significantly harder for an adjuster to dispute. When it paraphrases or generalizes, it creates gaps that experienced adjusters use to justify lower settlement offers.
On AI Adoption Failures and What Firms Can Do Differently
Kohan addressed why many law firms have tried AI without seeing meaningful results. His view is that most AI failures in legal operations are not technology problems. They are execution problems. Tools get deployed into workflows without clear ownership, without role-specific training, and without a defined process for reviewing output before it affects a client or a case.
He recommended that firms treat the first 30 to 60 days of any AI deployment as an active monitoring phase rather than a completed rollout. He also cautioned against signing long-term contracts before a platform has been tested on real active cases.
About Law Practice AI
Law Practice AI is an AI-powered legal workflow automation platform built for plaintiff law firms including personal injury, lemon law, and other civil plaintiff practices across the United States. The platform automates client intake, document collection, case summarization, demand letter drafting, and litigation support, integrating directly with CASEpeer, Filevine, and SmartAdvocate.
To learn more, visit lawpractice.ai.
