ScienceSoft to showcase production-ready AI claims workbench in Chicago

ScienceSoft brought a simple test case to Chicago: if an AI claims workbench cannot shorten the claims lifecycle, fit into existing systems, and leave a clean audit trail, “production-ready” does not mean much. The company said its demo would show connected handoffs, task-specific models, evidence-backed outputs, and adjuster approval gates designed to reduce delays in property and casualty claims operations.

Vital Soupel, ScienceSoft’s Senior Insurance IT and AI Consultant, presented the session, “How to Reduce the Claims Lifecycle From Weeks to Same-Day With AI,” on June 11 at 10:10 a.m. CT at the Hilton Suites Chicago, Magnificent Mile. The appearance was part of the two-day Insurance Tech & Innovation Conference 2026, held June 10 to 11 in Chicago and aimed at senior executives, insurers, reinsurers, brokers, and technology vendors tracking AI-driven underwriting, claims automation, cyber insurance, IoT, ESG integration, personalization, and emerging risk strategies.

What made the demo relevant was not the booth-side theater but the operational logic behind it. ScienceSoft described the workbench as production-grade and production-ready, with a design meant to work alongside existing claims systems instead of replacing legacy infrastructure outright. That matters in claims, where the most durable projects usually start by improving intake, classification, evidence assembly, and adjuster workflow, then leave final decisions with people who can check the file and sign off.

The timing also underscored the pressure carriers are under. J.D. Power’s 2025 U.S. Property Claims Satisfaction Study found that the average claimant does not receive final payment until 44 days after first notice of loss, while the average cycle time from filing to finished repairs reached 32.4 days, the longest since the study began in 2008. Satisfaction drops sharply once claims stretch beyond 31 days, which makes cycle-time reduction more than a back-office metric. It is a customer-experience problem and an expense problem at the same time.

That is where the emphasis on approval gates and evidence-backed outputs becomes practical. A workbench that automates the repetitive parts of the file, but preserves human oversight on decisions that affect consumers, is much more likely to survive real carrier scrutiny than a system promising full autonomy. The National Association of Insurance Commissioners reinforced that point when it adopted its Model Bulletin on the Use of Artificial Intelligence Systems by Insurers on December 4, 2023, and by early June 2026 multiple states had already adopted it or were acting on it. The message from Chicago was clear: claims AI is moving from pilot talk to controlled automation, and vendors now have to prove it in the workflow, the audit trail, and the clock.