Top 7 Agentic AI Use Cases for Insurance

TL;DR

Agentic AI stands to be one of the most transformtive technologies to impact the insurance industry. But knowing where to apply the technology is step one. Process intelligence helps identify high impact use cases and this article will discuss ideas that are suitable for AI agents.

Agentic AI for Insurance is Here. Are you Targeting the Best Use Cases?

The insurance industry faces mounting pressure from rising claim costs, regulatory complexity, and customer expectations for faster service delivery. Traditional manual processes that once defined insurance operations are becoming unsustainable bottlenecks that threaten competitive positioning and profitability.

Agentic AI automation—powered by autonomous AI agents that learn, adapt, and make independent decisions—is emerging as the transformative solution insurers need. Unlike static rule-based systems, agentic AI delivers dynamic, context-aware solutions that can dramatically improve operational efficiency while reducing costs.

According to Deloitte's 2024 Insurance Outlook, insurers who implement intelligent automation see operational cost reductions of 25–35% within the first year. The National Association of Insurance Commissioners (NAIC) reports that administrative expenses account for approximately 27% of total insurance industry costs, representing a $150 billion annual opportunity for efficiency gains through automation.

It can be challenging to identify agentic AI use cases. Below, we explore seven critical use cases that demonstrate how agentic AI is driving unprecedented transformation across property & casualty insurance.

The Foundation for Successful Agentic AI: Process Intelligence

Before deploying agentic AI, insurers must first map their actual human workflows—not documented procedures, but how work really happens. Process intelligence reveals the hidden inefficiencies that agentic AI can address. Most insurance organizations operate with a dangerous assumption: their documented processes reflect operational reality.

Claims adjusters develop workarounds for system limitations. Underwriters create shadow processes to meet tight deadlines. Customer service representatives build informal knowledge bases to handle edge cases.

These undocumented workflows represent the greatest opportunities for agentic AI transformation. Process intelligence platforms like Skan AI capture this reality by observing actual human-system interactions across all applications. For insurance CIOs, this granular visibility prevents automating dysfunction at scale.

When a Fortune 500 customer of ours discovered their underwriters spent 40% of time switching between 12 applications, they understood why automation attempts had failed. Process intelligence identified the specific data re-entry tasks that agentic AI could eliminate.

1. Transform Insurance Underwriting and Risk Assessment with Agentic AI

Insurance underwriting remains one of the most complex and critical processes in the industry, requiring detailed analysis of risk factors, policy terms, and regulatory requirements. Traditional underwriting workflows often involve multiple systems, manual data entry, and subjective risk assessments that can lead to inconsistent pricing and coverage decisions.

Challenge: Underwriters spend a significant amount of their time manually gathering data from multiple disparate systems. This creates multi-day quote delays that result in lost prospective customers.

Agentic AI Advantage:

• AI agents handle data aggregation, system navigation, and routine validation tasks. They instantly pull information from credit reports, property records, and risk databases. They automatically flag applications outside normal parameters and detect data inconsistencies.
• Human underwriters focus on strategic risk assessment, complex coverage decisions, and building client relationships. The high-judgment work that drives competitive advantage.

2. Agentic AI Revolutionizes Claims Processing and Adjudication

Claims processing represents the largest operational expense for most insurers, with manual review, investigation, and settlement decisions creating significant cost centers. The complexity increases with regulatory requirements, fraud concerns, and varying policy terms across different product lines.

Challenge: Claims adjusters spend significant portions of their time on administrative tasks rather than investigating claims. This creates multi-day settlement delays and drives higher customer churn rates.

Agentic AI Advantage:

• AI agents handle administrative workflows, document organization, and routine processing tasks. They automatically route claims, extract policy information, and process straightforward claims that match established patterns. They prepare investigation files with relevant documentation.
• Human adjusters focus on complex investigations, customer negotiations, and disputed claims resolution. The expertise-driven work that requires human judgment and empathy.

3. Optimize First Notice of Loss (FNOL) Workflows using Agentic AI

The First Notice of Loss process sets the tone for the entire claims experience, yet many insurers still rely on manual call center operations and paper-based documentation that creates delays and customer frustration. Poor FNOL processes can significantly impact customer retention and regulatory compliance.

Challenge: Call center agents spend significant time per FNOL call manually navigating between systems. During high-volume periods, this creates extended hold times and rushed conversations.

Agentic AI Advantage:

• AI agents handle system navigation, data entry, and coordination tasks. They instantly verify coverage, create claim files, and schedule inspections automatically. They populate forms and notify internal teams with complete details.
• Human agents focus on customer support, emotional guidance, and complex claim situations. This is the relationship-building work that drives customer retention during stressful events.

4. Leverage Agentic AI for Policy Administration and Customer Service

Policy administration involves numerous touch points throughout the customer lifecycle, from initial applications and endorsements to renewals and cancellations. Manual processes create opportunities for errors, delays, and poor customer experiences that can drive churn in competitive markets.

Challenge: Customer service representatives spend significant portions of their time manually processing routine policy changes. Simple changes require multiple days while agents handle fewer customer interactions per day.

Agentic AI Advantage:

• AI agents handle routine policy changes, data validation, and system updates automatically. They process address changes, billing adjustments, and standard endorsements instantly. They resolve billing discrepancies and prepare customer profiles.
• Human representatives focus on consultative selling, complex problem-solving, and retention conversations. The high value work, relationship-driven work that increases customer lifetime value.

5. How Agentic AI Streamlines Fraud Detection and Investigation

Insurance fraud costs the industry approximately $308 billion annually according to the Coalition Against Insurance Fraud. Traditional rule-based fraud detection systems struggle to identify sophisticated fraud schemes and often generate excessive false positives that burden investigation teams.

Challenge: Fraud investigators spend the majority of their time researching false positives from rigid systems. They analyze fewer cases per day while sophisticated fraud rings operate undetected.

Agentic AI Advantage:

• AI agents handle data analysis, pattern detection, and routine verification tasks. They cross-reference claims across databases, compile evidence files, and eliminate false positives through continuous learning. They organize investigation materials and track case timelines.
• Human investigators focus on complex fraud scheme analysis, witness interviews, and strategic case building. This is the analytical and investigative work that requires human intuition and experience.

6. Agentic AI Transforms Regulatory Compliance and Reporting

Insurance regulatory compliance requires meticulous tracking of transactions, regular reporting to state and federal agencies, and adherence to constantly evolving regulations across multiple jurisdictions. Manual compliance processes are error-prone and resource-intensive.

Challenge: Compliance analysts spend significant portions of their time manually compiling data from disparate systems. They struggle to interpret conflicting regulatory guidance across multiple state jurisdictions.

Agentic AI Advantage:

• AI agents handle data compilation, report formatting, and deadline tracking automatically. They monitor regulatory changes, prepare documentation, and validate data accuracy before submission. They organize historical compliance data for easy access.
• Analysts focus on regulatory interpretation, strategic compliance planning, and stakeholder communication. Humans can focus on the advisory work that prevents violations and guides business strategy.

7. Agentic AI Impacts Actuarial Analysis and Pricing Models

Actuarial analysis requires complex mathematical modeling, extensive data analysis, and continuous model refinement to maintain competitive pricing while ensuring profitability. Traditional approaches often rely on historical data and static models that cannot quickly adapt to changing market conditions.

Challenge: Actuaries spend significant portions of their time on data preparation and model maintenance rather than strategic analysis. They manually update pricing models on quarterly schedules instead of responding to real-time market changes.

Agentic AI Advantage:

• AI agents handle data preparation, model updates, and computational analysis automatically. They process claims data continuously, run scenario analyses, and track model performance with real-time reporting. They collect and analyze market pricing data.
• Actuaries focus on strategic risk assessment, product development, and competitive positioning. The analytical and strategic work that drives profitability and market differentiation will still need a human touch.

Next Steps: Reshaping Insurance Operations

Agentic AI is not merely an operational improvement-it represents a fundamental transformation in how insurance companies create value, manage risk, and serve customers. The insurance industry's traditionally conservative approach to technology adoption is giving way to recognition that agentic AI is essential for competitive survival.

The insurance organizations leading this transformation are those systematically mapping their core processes and strategically deploying agentic AI to eliminate inefficiencies, reduce errors, and create superior customer experiences.

Want to identify your highest-impact agentic AI opportunities in insurance? Let's connect to develop a customized roadmap that addresses your specific operational challenges and competitive positioning needs.