Adopting an Agentic Automation & Orchestration Platform for Insurance Operations
Download the Whitepaper
Agentic AI in insurance has moved from experiment to everyday conversation. Insurance companies are running more AI pilots than ever, and AI agents can now read claim files, check coverage and recommend reserves or prices, work that only people could do before.
But running AI pilots doesn't mean AI is running the business. Most projects never make it past the pilot stage. More than 40% of agentic AI projects are expected to be cancelled by the end of 2027, mostly because of rising costs, unclear value and weak risk controls, not because the AI doesn't work. Even high-value use cases like agentic AI claims processing stall when no one can show how decisions are made.
On top of that, many insurers buy automation and governance separately: AI tools from one vendor, insurance workflow orchestration and controls from another, on different roadmaps and budgets. The result is AI that moves fast with no reliable way to control or explain its decisions. In a regulated industry, that weakens AI governance in insurance and creates serious risk.
This whitepaper explains how insurers can move from scattered AI experiments to a governed, agentic operating model. It shows how pairing an agentic automation platform for insurance (AI that does the work) with an agentic orchestration platform for insurance (the layer that controls it) helps insurers scale AI safely across claims and underwriting, with every decision traceable.
In this whitepaper on agentic AI in insurance, you'll learn how to:
- Move AI from pilot to production by understanding why most initiatives stall and what the insurers that scale do differently.
- Settle claims and issue quotes faster with multi-agent systems in insurance that power AI claims automation, handling routine work in hours or minutes and handing complex cases to people with the file already prepared.
- Plan your AI journey with confidence using a four-level maturity model, from basic automation to governed autonomy, with KPIs at each level for straight-through processing, cycle time, cost per claim and fraud leakage.
- Build trust into every decision through guardrails, human-in-the-loop review for insurance AI where it matters, and full decision audit trails that regulators and policyholders can rely on.
- Make every outcome defensible with explainable AI for insurance decisions, and see why AI explainability vs. decision governance is the distinction regulators care about most.
- Make the right leadership decisions with clear recommendations for every role, from the executive team to claims, underwriting, risk and technology leaders.
By pairing automation with orchestration, insurers can process more claims and applications, reduce costs and extend AI's role with confidence, building a foundation for governed autonomy across insurance operations.
Download the whitepaper to learn how agentic AI in insurance can scale safely and deliver measurable results.
