Scaling Agentic AI in Insurance: Overcoming Barriers and Building Momentum

In part 3 of this series, we demonstrated how Agentic AI can reshape underwriting, claims, and operations. However, transformation at scale doesn’t happen automatically. Most insurers encounter friction as they try to move from localized pilots to enterprise-wide adoption. These challenges aren’t just technical; they’re also organizational, cultural, and regulatory. In this final installment, we explore how to overcome these barriers and create a long-term strategy for scaling Agentic AI across the insurance value chain. 

Common Barriers to Scaling AI in Insurance 

Despite growing interest in AI, many insurers remain stuck in early adoption. According to a Roots survey, key inhibitors include: 

• Skills gaps (52%): Many insurers lack in-house AI talent or the frameworks needed to integrate AI into workflows. 
• Data quality issues (40%): Incomplete, siloed, or unstructured data limits the effectiveness of AI models. 
• ROI uncertainty (38%): Leaders struggle to quantify the impact of AI initiatives, especially in early stages. 
• Regulatory scrutiny (36%): Concerns around explainability, bias, and auditability make leaders wary. 
• Change resistance (35%): Cultural inertia, fear of automation, and lack of buy-in stall transformation. 

These hurdles are real, but they are also addressable with the right roadmap and strategic approach. 

Framework for Enterprise-Scale Adoption 

Scaling AI across the insurance enterprise demands more than deploying innovative technologies; it requires a deliberate strategy that integrates business priorities, human oversight, and robust governance. By aligning AI initiatives with enterprise objectives and fostering a culture of collaboration, insurers can overcome barriers to adoption and unlock the full potential of intelligent systems in transforming workflows and achieving measurable outcomes. 

1. Anchor AI to Business Outcomes: Start by identifying high-friction, high-volume workflows that align with your strategic goals. Avoid “cool tech for tech’s sake.” Instead, aim for use cases where automation leads to measurable results (e.g., cycle time reduction, margin improvement, or customer satisfaction). 
2. Invest in Human-in-the-Loop (HITL) Systems: Early-stage AI should not operate unchecked. HITL design ensures agents support, not replace, human experts. This builds trust, captures feedback, and improves model accuracy over time. 
3. Establish AI Governance from the Start: Governance is not optional. It must address: 
   • Model explainability 
   • Role-based access and decision logging 
   • Regulatory compliance (e.g., NAIC guidelines) 
   • Escalation procedures 
   • Transparency when decisions or communications are handled with AI 
   • Equity and accountability in reasoning 

Governance is what transforms AI from a pilot to a sustainable capability. 

4. Create Scalable, Reusable AI Agents: Don’t build from scratch for every use case. Instead, develop modular agents that can be repurposed.  For example: a document ingestion agent that supports underwriting, claims, and customer service. 
5. Treat AI as a Change Program, Not a Tech Project: Technology alone doesn’t transform workflows, people do. Prioritize change management, training, and executive sponsorship. Build internal champions who can own and evangelize AI success stories. 

Measuring and Communicating Impact 

Scaling AI requires ongoing proof of value. Develop KPIs tailored to each domain: 

• Underwriting: Quote-to-bind time, triage accuracy, underwriter capacity lift 
• Claims: Cycle time, customer satisfaction scores, fraud detection rates 
• Operations: Manual task reduction, compliance accuracy, straight-through processing rates 

Use dashboards and internal communications to share wins and build momentum. Make success visible, not theoretical. 

Why Strategic Partners Matter 

Insurers don’t need to build this alone. In fact, they shouldn’t. Scaling AI across the enterprise is as much about orchestration as it is about innovation. That’s where Sikich plays a critical role. 

How Sikich Supports Scalable AI Adoption 

Sikich works side-by-side with insurers to: 

• Define strategic AI priorities based on your business goals 
• Assess workflow readiness and data health 
• Design flexible, compliant AI architectures 
• Implement and iterate real-world Agentic AI pilots 
• Build internal capabilities for long-term ownership 

We help you scale with confidence, ensuring every AI deployment is governed, measurable, and aligned to enterprise value. 

The Path Forward: Beyond Transformation to AI Enablement 

AI is no longer a novelty, but rather a foundation of operational excellence, risk resilience, and customer-centricity in modern insurance. What was once an experimental edge is now a strategic imperative. Yet, only those who approach AI with discipline, clarity, and vision will unlock its full potential. Done right, Agentic AI doesn’t just accelerate tasks; it redefines what’s possible across the insurance value chain. 

Our experienced and dedicated team doesn’t just deploy AI; we embed it into the DNA of your organization. Our mission is to help P&C insurers shift from tactical experimentation to enterprise-wide enablement, intelligently, responsibly, and at scale. 

Now is the time to move from ambition to execution. Whether you’re just beginning to explore AI use cases or looking to scale proven pilots, we’re here to guide your journey. 

If you missed the first three parts of the series, you can revisit them here: 

• From Generative to Agentic AI – Why P&C Insurance Must Evolve Its AI Strategy
• Adopting Agentic AI in Insurance – What Real Modernization Looks Like
• Agentic AI in Insurance: Underwriting, Claims, and Enterprise Operations

Let’s build the future of intelligent insurance together. Connect with Sikich to map your AI strategy, unlock tangible results, and lead your organization into its next era of performance and innovation.