Kickstart Your Agentic AI Journey with a Single Use Case

Agentic AI deployed at scale is proven to deliver a wide range of benefits, including increased capacity, time savings, better underwriting outcomes, reduced claims leakage, and improved worker/customer satisfaction. Delegating non-core and administrative tasks to AI allows human experts to spend more time doing what they do best: evaluating and managing risk.

There’s no shortage of ways businesses can use AI to improve performance, but if you’re starting to test the AI waters, it’s best to pick a single AI use case. Testing multiple use cases simultaneously can be a drain on resources and produce uneven results. Focusing on a single use case applicable to a specific task helps teams to focus these resources where they’ll have the most value.  

The potential of AI in insurance is enormous, even when you start small. Taking this approach creates a less disruptive adoption experience for underwriters and claims managers. 

How to Decide on the Agentic AI Use Case 

Deciding on a use case for agentic AI in an insurance company requires a strategic, risk-aware, and ROI-focused approach. Here’s how you can go about it: 

1. Define Business Goals:

• Improving customer experience (faster claims processing, personalized policies).
• Increasing operational productivity and efficiency – focus on tasks that eliminate non-core/lower value/administrative work (automating underwriting, fraud detection).
• Enhancing risk management (predictive analytics, dynamic pricing).
• Ensuring regulatory compliance (paying claims on a timely basis, reporting information to regulators).
  

2. Identify High-Impact, Attainable Use Cases:

• Submission Intake and Data Extraction, Endorsement Processing, and Renewals – AI can accurately extract, analyze, and validate key data from insurance applications, loss runs, statements of value [SOV] and other schedules, endorsements, and other documents and automatically pass the data to the underwriting platform.
• Submissions Triage – AI can automatically assign submissions based on predefined carrier rules, ensuring efficient workflow and optimal resource utilization.
• Request Documents from Brokers – AI can identify missing items based on custom configured rules and request the missing application documents from brokers and agents, to ensure all necessary information is collected and reduce back-and-forth communication.
• Analyze Risk Thresholds – AI can classify submission risks and match them to predefined thresholds, flag for further review, or even decline uninsurable risks based on predefined business rules.
• Premium Audits – AI can request, collect, and extract data automatically from employer payroll records, job classifications, 1099 reports, tax forms, and other relevant premium data to increase accuracy and accelerate auditor workflows.

• Claims Indexing – AI can automatically read, understand, and classify documents like demand letters, medical records, police reports and ACORD forms to reduce manual effort, speed processing, and improve data consistency and accessibility.
• First Notice of Loss (FNOL/FNOI) Setup – AI can streamline FNOL/FNOI processing through automated data capture, coverage verification, duplicate claim lookup, and immediate claim creation in systems of record.
• Identify Legal Demands – AI can find and track time-sensitive requests buried within multiple-page demand packages to ensure prompt action from adjusters to help prevent missed deadlines and improve overall compliance.
• Send Out Explanation of Benefits (EOBs) – AI can distribute EOBs and policy ID information to claimants, streamlining communication and accurate delivery of important information.
• Calculate Reserves – AI can analyze claim data to calculate and set initial reserve estimates, minimizing subjectivity and improving consistency across the process. It can also flag and assign potential high-value claims to more seasoned adjusters.
• Identify Subrogation Opportunities – AI can analyze claims data to improve recovery rates by identifying accounts for recovery or subrogation.

• Mailroom Classification – AI can automatically monitor multiple digital channels for new mail, classify the content automatically, and route based on business rules to claims, underwriting, or servicing departments, enabling faster processing with reduced manual effort for faster response times.
• COI Creation – AI can generate Certificates of Insurance and instantly distribute them to policyholders, boosting customer satisfaction while significantly reducing manual servicing effort.
• Compliance Reporting – AI can automatically create and distribute Medicare and state reports as needed, improving efficiency and staying compliant.
• Financial Processing – AI can help with collections/recoveries by identifying accounts and tracking the recovery status.
• Premium Audits – AI can request, collect, and extract data automatically from employer payroll records, job classifications, 1099 reports, tax forms, and other relevant premium data to increase accuracy and accelerate auditor workflows.

3. Assess Technical & Regulatory Constraints:

Define policies and protocols on AI usage, ethics, and accountability (define human oversight roles, establish escalation procedures for AI decision disputes, and implement AI risk assessments and model validation procedures). Ensure alignment with corporate goals and regulatory requirements. Many insurance companies have created an AI governance committee to oversee deployments as part of this process. Insurance is a highly regulated industry, so ensure the AI system complies with:

• Data privacy laws (GDPR, HIPAA, CCPA).
• Fairness & bias regulations (AI ethics & explainability).
• Auditable decision-making (AI transparency in underwriting & claims).

4. Test with Prototypes and Pilots:

Start with a low-risk pilot project in a well-defined area (e.g., AI-powered claim triaging). Gather feedback, measure performance, and refine the model before full-scale deployment. Continuously improve with human-in-the-loop (HITL) feedback and update AI models based on real-world data.

5. Measure Success with KPIs:

Select use cases with broad applicability across the business to simplify AI ROI measurement. Define clear metrics such as:

• Reduction in claims processing time
• Decrease in fraud-related losses
• Increase in customer satisfaction scores
• Cost savings from automation
  
  

6. Scale & Expand:

Once a successful pilot is validated, gradually expand AI capabilities to additional functions (e.g., risk modeling, underwriting automation). Ensure AI models integrate with legacy insurance systems and build scalable architecture to handle growing AI workloads.

While the potential for AI is substantial, not all organizations are ready to deploy it on a large scale. Set a balanced pace that allows for quick deployment without moving so fast that it leaves team members behind, and or makes agents and brokers uncomfortable.

A well-governed, explainable, and continuously improving AI system is proven to create efficiency, cost savings, and better risk management while maintaining trust and compliance. Look at AI as a tool to augment human expertise for optimal results. Selecting the right use case and proof-of-concept can set your agentic AI journey on the path to success. 

 
Learn more about how other insurers are successfully managing AI adoption by downloading our 2025 State of AI Adoption in Insurance Report.