I’m thrilled to sit down with Vijay Raina, a renowned expert in enterprise SaaS technology and software design. With his deep expertise in software architecture and thought leadership in the field, Vijay offers invaluable insights into the evolving landscape of insurance technology. Today, we’ll dive into the intersection of agentic AI and core systems in the insurance industry, exploring why traditional systems remain vital, the potential of hybrid architectures, and the practical integration of AI as a transformative tool.
How do you interpret the notion of ‘SaaS is dead’ when it comes to insurance core systems, and why might this idea be misleading?
The phrase ‘SaaS is dead’ has gained traction with the rise of agentic AI, suggesting that traditional software models are becoming obsolete as AI takes over business logic. It stems from the idea that AI can handle core operations across systems, making standalone applications less central. However, in insurance, this perspective doesn’t fully hold. The industry operates under strict regulatory demands and long-term commitments, requiring stability and traceability that SaaS-based core systems are uniquely equipped to provide. These systems aren’t disappearing; they’re evolving to work alongside AI, ensuring compliance and consistency over decades. Dismissing SaaS overlooks the critical foundation it offers for scalable innovation in this space.
What makes core systems so indispensable for insurance companies, even as agentic AI gains prominence?
Core systems are the backbone of insurance operations. They manage everything from contracts to premiums and claims histories, often spanning decades. Unlike AI, which excels in dynamic, data-driven tasks, core systems provide deterministic reliability—crucial in a regulated environment where every decision must be auditable and reproducible. They act as the organization’s memory, ensuring data persistence and stability. Without them, you risk losing the ability to track long-term commitments or meet regulatory standards, no matter how advanced AI becomes.
What are some of the biggest risks insurance companies might face if they tried to replace traditional policy administration systems with pure AI agent systems?
Moving entirely to AI agent systems sounds cutting-edge, but it’s fraught with challenges. Operationally, you’d face potential chaos—AI can struggle with state consistency across distributed components, leading to errors in critical processes like claims or billing. Then there’s the regulatory angle: insurance demands explainable, traceable decisions, and many AI models operate as black boxes, which regulators won’t accept. Reputationally, a misstep due to unexplainable AI outputs could erode customer trust or invite legal scrutiny. These risks make a full replacement impractical and underscore the need for a balanced approach.
You’ve highlighted hybrid architectures as a way forward. Can you explain how these setups function in the insurance context?
Hybrid architectures combine the best of both worlds: deterministic core systems for stability and agentic AI for intelligence. In practice, the core system handles standardized, rule-based tasks like billing or contract renewals with speed and auditability. Meanwhile, AI steps in for complex, unstructured challenges—think fraud detection or processing medical claims documents—where context and adaptability matter. This setup allows seamless integration through modular designs and APIs, ensuring the core remains the single source of truth while AI enhances decision-making and efficiency.
How does the concept of AI as a ‘copilot’ play out in the day-to-day workflows of insurance companies?
AI as a copilot is about augmentation, not replacement. It supports human decision-makers by generating actionable insights or flagging issues. For instance, in claims processing, AI might analyze documents, summarize key points, and suggest coverage decisions while highlighting potential fraud indicators for review. It prioritizes tasks and proposes new rules based on patterns, but the final call stays with human adjusters. This balance ensures critical decisions remain transparent and accountable, while AI streamlines the workload and boosts accuracy.
You’ve described core systems as the ‘memory’ of an insurance organization. Can you elaborate on why this metaphor resonates so strongly?
Core systems are like memory because they store and preserve the entire history of an insurer’s operations—contracts, premiums, benefits, and more—often over decades. This long-term data persistence is vital for fulfilling commitments, especially in life or health insurance, where policies can span a lifetime. They ensure there’s a reliable, auditable trail, preventing inconsistencies that could arise from distributed AI components. Without this memory, insurers can’t uphold trust or compliance, making core systems irreplaceable even in an AI-driven world.
What actionable steps should insurance companies take to effectively integrate AI with their existing core systems?
First, focus on modularity—restructure core systems to support easy integration of AI components through standardized connectors or protocols. This allows for flexibility to swap or upgrade AI tools without disrupting operations. Start with low-risk pilot projects, like AI for fraud triage or chatbots, to build experience and confidence. Also, prioritize governance—embed transparency and auditability into AI processes to meet regulatory needs. Testing these integrations in controlled environments helps identify gaps and ensures a smooth transition without compromising stability.
Looking ahead, what is your forecast for the role of agentic AI in shaping the future of insurance core systems?
I see agentic AI as a powerful accelerator, not a replacement, for core systems. Over the next decade, we’ll likely witness even tighter integration through hybrid architectures, where AI handles increasingly complex tasks like personalized underwriting or real-time customer interactions. Core systems will remain the stable foundation, evolving to be more modular and API-driven to support this innovation. The key will be balancing AI’s agility with the deterministic reliability insurers need, ensuring both efficiency and compliance. It’s an exciting time, as those who master this synergy will lead the industry in speed and resilience.
