Agentic AI in Insurance: Underwriting, Claims, and Enterprise Operations

In part 2 of this series, we explored how Agentic AI moves insurers from basic automation to intelligent orchestration. Now, in part 3, we turn our attention to what Agentic AI actually looks like in specific areas of the insurance value chain—specifically in underwriting, claims, and enterprise operations. This is where strategic AI adoption becomes transformational. It’s not about incremental improvements; it’s about redefining how work is executed, decisions are made, and value is created. 

Modernizing Underwriting: From Manual Risk Assessment to Risk Intelligence 

Underwriting has always been a data-driven discipline, but the sheer volume and variability of data sources today make traditional methods inefficient. Agentic AI offers a solution by acting as an intelligent liaison between disparate data and underwriter decision-making. 

What this looks like in practice: 

• AI agents extract data from broker submissions and ACORD forms, verify information against internal and third-party datasets, and assess risk in real time. 
• The agent triages applications based on risk appetite, potential profitability, and complexity, routing straightforward submissions for auto-quoting and escalating complex ones to underwriters. 
• It proactively flags missing information and communicates with brokers, keeping the submission moving without underwriter intervention. 

With Agentic AI, underwriting moves from being reactive and manually intensive to proactive and insight-led. This isn’t about removing the underwriter, but rather about enhancing their judgment with accurate, immediate, and contextual support, allowing them to focus on specialized tasks instead of overhead.  

Transforming Claims: Speed, Empathy, and Accuracy at Scale 

Claims is where insurers either build loyalty or erode trust. It’s also where costs can spiral out of control if not managed effectively. Agentic AI changes the equation by bringing precision and scalability to the full lifecycle. 

Real-world applications of Agentic AI in insurance claims include: 

• Automated FNOL across voice, chat, and web that immediately launches the claim process. 
• Computer vision models that assess damage severity from submitted photos and cross-check against policy details. 
• Fraud detection agents that flag suspicious patterns based on historical behavior and claim context. 
• Intelligent coordination of adjusters, repair services, and policyholders, with updates and decisions logged in real time. 

By reducing cycle times and ensuring consistent handling, Agentic AI enhances both efficiency and the policyholder experience. Importantly, it empowers adjusters to focus on complex or high-sensitivity cases where human empathy and discretion are essential. 

Elevating Insurance Operations: Intelligence Across the Enterprise 

Beyond underwriting and claims, operational bottlenecks, manual compliance tasks, and disconnected data flows abound in legacy systems. Here too, Agentic AI drives transformation. 

Examples of operational improvements include: 

• Intelligent document processing: AI reads, classifies, and extracts data from high volumes of unstructured documents with accuracy and speed. 
• Regulatory compliance monitoring: Agents automatically log key activities, flag non-compliance, and generate audit-ready reports. 
• Data integration and workflow automation: Instead of building brittle one-off integrations, AI agents facilitate flexible, dynamic connections between systems. 

This transforms IT and operations from reactive support functions to strategic enablers of speed and scalability. 

Accelerating Market Development: Intelligence For Growth 

Agentic AI isn’t limited to internal operations; it also plays a powerful role in helping insurers identify, evaluate, and capture new market opportunities or improve operations in existing markets. 

Whether expanding into new geographies, launching products, or building distribution partnerships, AI agents can autonomously gather insights, surface recommendations, and initiate actions. 

Examples of market development support include: 

• Competitor and market tracking: AI scans filings, digital campaigns, and customer sentiment to track market shifts and competitor moves in real time. 
• Opportunity scouting: Agents identify underserved regions, niche customer segments, or affinity group opportunities based on demographic and behavioral data. 
• Partner research and outreach: AI qualifies potential brokers or channel partners, enriches profiles, and drafts personalized outreach, accelerating relationship development. 
• Market optimization recommendations: Agents assess underperforming regions or products by analyzing loss ratios, growth trends, and competitor positioning, informing decisions to consolidate or divest. 

This enables business development teams to move faster, focus on high-value opportunities, and stay one step ahead in a dynamic insurance landscape. 

Strategic Impact: Where Innovation Meets Execution 

According to Roots, insurers cite reducing cycle time, increasing underwriting efficiency, and improving customer experience as top AI objectives. It’s important to note, however, that these outcomes are only possible when AI is embedded, not bolted on. 

Agentic AI must be designed and implemented with a full understanding of the business context, regulatory environment, and customer expectations. That’s why partnerships matter. 

Why Sikich? Bridging Vision with Execution 

Sikich helps insurers unlock the full value of Agentic AI by: 

• Mapping current workflows to identify automation and orchestration opportunities. 
• Implementing insurance-tuned AI agents that align with your specific tech stack. 
• Ensuring governance, data integrity, and compliance are built into every step. 
• Supporting cultural adoption through training, documentation, and continuous optimization. 

We don’t just deploy technology; we co-create value with your teams, ensuring Agentic AI drives both efficiency and strategic advantage. 

Looking Ahead: Scaling What Works 

As we move into the fourth and final part of this series, we’ll focus on how to overcome the barriers to scaling AI across the enterprise. From talent gaps and data quality to change resistance and regulatory scrutiny, we’ll explore practical frameworks for long-term success. 

If you’re already imagining how these AI capabilities could transform your underwriting, claims, or operations, let’s talk. At Sikich, we bring the technical depth and industry insight to turn ideas into scalable, compliant, and value-driven reality.