Assess and Prioritize Agentic AI Use Cases in P&C Insurance

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Analyst perspective

From technical possibility to governed autonomy.

Property and casualty insurers are under pressure to improve underwriting speed, claims efficiency, and service responsiveness while maintaining strong control over decisions that affect coverage, pricing, fraud handling, and settlements. AI is now moving beyond copilots and narrow task automation toward agentic systems that can coordinate and execute parts of end-to-end workflows.

In P&C insurance, that shift creates a different challenge than in less regulated industries. The question is not simply whether autonomous AI can improve productivity. The question is where autonomous behavior is appropriate inside workflows that carry financial, operational, and consumer consequences. Insurance regulators are increasingly focused on governance, documentation, oversight, and the risk of adverse consumer outcomes from AI-supported decisions.

This research helps insurers define where agentic AI belongs across underwriting and claims, where autonomy should remain constrained, and where human decision authority must stay in place. The objective is to move from experimentation to defensible prioritization grounded in business value, accountability, and insurance-specific governance expectations.

Vidhi Trivedi
Senior Research Analyst, Insurance Industry
Info-Tech Research Group

Executive summary

Info-Tech Insight
Agentic AI delivers value in P&C insurance only when insurers replace isolated pilots with cross-functional use case prioritization, turning experimentation into governed investment decisions that can scale across underwriting, claims, and other high-impact workflows.

Your challenge

P&C insurers need faster decisions without weakening accountability.

This research is designed to help organizations facing these challenges:

• Underwriting and claims depend on fast, accurate action, but not all decisions are safe to delegate to autonomous systems.
• In P&C insurance, the core challenge is not automation alone. It is deciding where AI can act, where it can recommend, and where humans must retain full authority.
• Poorly governed autonomy can create pricing, settlement, fairness, documentation, and adverse consumer outcome risk.
• Without a practical framework for autonomy boundaries, insurers struggle to move beyond pilots in a way that is scalable and defensible.

Common obstacles

P&C insurers experience fragmentation across implementing agentic AI.

These barriers make agentic AI harder to scale across underwriting and claims:

• Most insurers do not yet have a structured method for separating acceptable autonomy from nondelegable decision authority.
• Core workflow data is fragmented across policy, claims, notes, documents, vendor systems, and human handoffs, which limits reliable orchestration.
• Scaling stalls when AI ambition outpaces governance, business-line ownership, and data readiness.
• In P&C insurance, even technically effective AI can fail if it cannot be explained, monitored, documented, and governed appropriately.

Info-Tech’s approach

A structured agentic AI use case tool helps P&C insurers identify where agentic AI can deliver value, where human oversight is required, and where autonomy introduces unacceptable risk.

How Info-Tech helps insurers make progress:

• Assess where agentic AI belongs in core insurance workflows. A P&C-specific review identifies where autonomous capabilities can improve underwriting and claims processes, and where traditional automation or human-led execution remains more appropriate.
• Apply a defensible evaluation framework to each use case. Info-Tech helps insurers assess use cases against key criteria such as business value, autonomy boundaries, regulatory exposure, consumer risk, and operational feasibility.
• Prioritize high-value use cases with responsible guardrails. A practical prioritization model helps leaders focus investment on use cases that are scalable, governable, and aligned to enterprise risk appetite.
• Support adoption through accountability and oversight. Clear ownership, human-in-the-loop expectations, and governance checkpoints help insurers operationalize agentic AI without weakening control over sensitive decisions.

Turn agentic AI use cases into a prioritized P&C insurance portfolio

A structured model to identify, validate, and prioritize agentic AI use cases across underwriting, claims, policy operations, and risk.

Agentic AI Use Case Development

Challenge
P&C insurers struggle to translate AI potential into measurable business value across underwriting, claims, and servicing.
Fragmented workflows, regulatory constraints, and high-volume operations create risk in adopting autonomous decisioning without a structured evaluation model.

Insight
P&C insurers unlock the most value from agentic AI when use cases are prioritized based on business impact, governance fit, and workflow readiness, not just technical feasibility or automation potential.

Decision Lens for P&C Insurance IT Leaders

• Business value (loss ratio, cost, CX)
• Implementation feasibility (data, systems, integration)
• Governance & risk fit (regulatory, decision sensitivity)
• Operational readiness (workflow maturity)

Drive faster, smarter decisions across insurance workflows
Agentic AI enables P&C insurers to shift from manual, fragmented workflows to coordinated, intelligent decisioning, improving speed, consistency, and customer outcomes while maintaining governance and control.

Info-Tech’s Methodology for Assessing Agentic AI Use Cases in P&C Insurance

Insight summary

Agentic AI Value in P&C Depends on Governance-Led Prioritization
P&C insurers create value when they define where agentic AI can support underwriting and claims, assign clear ownership, and sequence use cases based on business value, risk, and readiness rather than chasing isolated pilots.

Match Autonomy to Decision Risk
Context should determine the right agent design. In P&C insurance, use cases should be classified by the role the agent plays, the decision sensitivity involved, and the level of human accountability required across underwriting, claims, fraud, and servicing.

Pressure-Test Use Cases Early
The best use case is one the business can operationalize. CIOs should assess each idea against business impact, regulatory sensitivity, data readiness, and workflow feasibility to separate viable P&C use cases from experimental concept.

Start Where Trust Can Be Built
Confidence grows through deliberate sequencing. Early wins should come from lower-risk, measurable workflows before expanding into more sensitive decision support. This mirrors how leading insurers are scaling AI from practical use into broader transformation.

Prioritize Fast, Governable Wins
Prioritize use cases where value can be demonstrated quickly, consumer impact is limited, and cross-functional friction is manageable, such as claims FNOL support, document summarization, workflow coordination, and adjuster assist. These use cases are easier to govern than fully autonomous pricing or settlement decisions.

Gate Use Cases by Readiness
Prevent wasted effort by validating each use case against readiness criteria before it enters the portfolio, including data quality, workflow clarity, ownership, escalation design, and governance controls for consumer-impacting outcomes.

Research deliverable

Each step of this research is supported by practical P&C insurance deliverables to help you identify, evaluate, and prioritize agentic AI use cases across underwriting and claims.

Agentic AI Use Case Tool for P&C Insurance

Provides a structured environment to identify and prioritize agentic AI use cases, capture value drivers, assess autonomy boundaries, and build an initial roadmap aligned to underwriting, claims, fraud, and servicing priorities.

Measure the value of this research

Leverage this research’s approach to ensure your AI use cases align with and support your key business drivers and speed time to value

Project benefits

Measure the value of this project

Consider tracking the following measures to demonstrate the value of a more structured approach to assessing and prioritizing agentic AI use cases in P&C insurance.

Build a governed agentic AI portfolio that helps your organization move from experimentation to responsible execution.

Phase 1

Establish Context

This phase will walk you through the following activities:

You’ll review your organization’s business goals, key initiatives, and capability maps to anchor AI planning in real supply chain priorities. You’ll explore the purpose and structure of AI use cases to build a common understanding of how they support core processes and systems. Finally, you’ll assess your supply chain value chain and previously identified value drivers to confirm which business needs should guide AI use case development.

This phase involves the following participants:

• AI initiative lead
• CIO
• Other IT leadership
• Senior business executives and managers accountable for AI initiatives

AI is an innovation in machine learning

Artificial Intelligence (AI)
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Machine Learning (ML)
Machine learning (ML) is a subset of AI algorithms that parse data, learn from data, and then decide or make predictions.

Generative AI (Gen AI)
A subset of artificial intelligence systems that generate new outputs based on the data the system has been trained on using modalities such as text, audio, visual, and code.