Lawyers' Mutual Insurance Company (LMIC) has integrated ISI AI into its submission processing workflow, automating the extraction and structuring of application data for professional liability underwriting. The system now generates 67% of LMIC's new business quotes with a 98%+ extraction accuracy rate before human review, and the company reports its underwriters are saving 15 to 20 minutes per new business submission.
LMIC, which provides malpractice insurance for California attorneys, went live with ISI's AI Submission Agent to give its underwriting team more complete and organized submission information earlier in the review process. The integration is part of a broader push across the insurance industry to apply AI to document-heavy workflows where accuracy and data security are non-negotiable.
What the AI handles
The ISI AI Submission Agent extracts, structures, and reviews application data across professional liability submissions. That means underwriters see organized submission information upfront rather than digging through raw documents, which lets them spend more time on risk evaluation and broker relationships.
ISI says the system is designed to keep underwriting teams in control of risk assessment and decision-making. Policyholder information stays within LMIC's systems and is not used to train public models, and data is processed under industry-standard security and privacy practices.
What the company leaders say
"Launching AI into our day-to-day insurance workflows is an important step in continuing to meet our members' needs," said Andrew Chick, CEO at LMIC. "The accuracy and efficiency of ISI AI gives our team 15 to 20 minutes back per new business submission. This allows our underwriters to focus more time on risk evaluation, and the service experience our broker partners and members expect."
Doug Caccese, CEO at ISI, framed the go-live as a practical test for the industry. "For insurance teams evaluating AI, the real test is whether it can deliver trusted outputs in the flow of daily work. ISI AI is designed to support that standard while keeping underwriting teams in control of risk assessment and decision-making."
Why this matters for insurance professionals
The LMIC deployment offers a concrete benchmark for underwriting teams weighing AI adoption: 67% of quotes generated with 98%+ extraction accuracy before human involvement. That level of performance is what makes AI viable for day-to-day workflows rather than pilots.
For underwriters, the practical takeaway is that AI is now handling the repetitive intake work that consumes hours each week. For leaders, the LMIC example shows what a production deployment looks like - including the security and data governance decisions that have to accompany it. Insurance teams evaluating similar tools should compare extraction accuracy rates and ask whether vendor systems keep policyholder data out of public model training, as ISI does here.
Those evaluating AI for Insurance workflows will find the LMIC case useful as a reference point for production deployments, while teams focused on workflow efficiency can look at how AI Agents & Automation are being applied to submission intake and quote generation.
Your membership also unlocks:
