Operationalizing Agentic AI in Claims
Without the Audit Risk
Claims leaders are under pressure to “go agentic” to reduce cycle times, lower loss adjustment expense, and scale automation. At the same time, regulators, auditors, and internal risk teams are asking a different question:
That tension shows up quickly in real-world claims operations. Many insurers are still constrained by a foundational issue: the data and documents entering the process are not reliable enough to trust. Complex, multi-format, and often handwritten inputs introduce uncertainty at the very first step. When agentic workflows run on inputs they cannot trust, the outcome is not straight-through processing — it is straight to exceptions, rework, and audit risk.
Adjusters re-check what should have been automated. Supervisors get pulled into escalations. And the business inherits a new kind of exposure: automated decisions that are not fully explainable or defensible.
This panel brings together perspectives from across the ecosystem — a carrier, systems integrator, and insurtech — to explore what it actually takes to move from AI pilots to production-grade, audit-ready claims decisions. The conversation will focus on how leading organizations are rethinking claims workflows around trust, validation, and controlled automation, so teams can move faster where confidence is high and apply the right level of scrutiny where it is not.
What You’ll Learn
- A practical blueprint for agentic claims workflows How leading insurers are designing intake-to-decision flows that work in real conditions, not just ideal ones.
- How to operationalize trust in automation Approaches to confidence scoring, validation, and standardization that make outputs reliable for downstream systems, AI, and audit.
- How to scale straight-through processing, without losing control Routing work based on confidence and risk, so human expertise is focused on true exceptions, not routine cleanup.
- How to deliver audit-ready, inspection-ready outcomes Creating a clear, defensible trail across every step — inputs, transformations, decisions, and outputs.
Featured Speakers
Chris Huff
Chris Huff is a growth-focused tech and SaaS executive with a proven track record in scaling enterprise software companies backed by private equity and venture capital. He is known for building strong teams and driving value through operational efficiency, revenue growth, and product innovation. Prior to joining Adlib, Chris was CEO at Base64.ai and Chief Strategy and Growth Officer at Tungsten Automation (formerly Kofax), where he led strategy, product, AI, GTM, marketing, and partnerships. He also co-led Deloitte’s U.S. Public Sector Intelligent Automation practice and served as a Major in the U.S. Marine Corps. Chris brings deep expertise in AI, automation, and digital transformation — and a clear vision to expand Adlib’s impact across regulated industries.
Jun Yamada
Jun Yamada is Vice President of Business Transformation at Tokio Marine Group, one of the world’s largest insurance organizations. Jun leads cross-company initiatives that modernize claims operations through AI, data, and shared operating models — orchestrating group-wide pilots, governance frameworks, and scale-up plans with executives across the Americas and Europe. His work centers on measurable outcomes and repeatable playbooks that can travel across markets. Jun currently drives Tokio Marine’s Digital Acceleration Team claims agenda as a core part of the Group’s Global Operations 2026 initiative, focused on building shared, scalable capabilities worldwide.
Frederic Stallaert
Frederic Stallaert is the Co-Founder and CEO of Paperbox, an agentic AI platform built for insurance claims and policy management. Frederic founded Paperbox on the belief that technology should improve the way people interact with insurance — so that no policyholder is just a number. Under his leadership, Paperbox has developed what it calls the insurance mailroom of the future: an AI-native system that transforms how carriers and MGAs handle document intake, routing, and processing at scale. Before Paperbox, Frederic worked in AI consultancy at ML6, where he developed deep expertise in applied machine learning for enterprise clients. He holds a degree from Ghent University and hosts the Efficiency Engineers podcast, where he interviews insurance leaders challenging the industry’s reputation for being slow to change.
