From Pilots to Scale: Agentic AI and Core Modernization in Insurance – The question is when and not if!

Agentic AI—autonomous agents that act, learn, and collaborate with humans—is fast emerging as the next evolution of AI in insurance. These aren’t just smart bots; they’re coordinated agents capable of navigating multi-step workflows, making decisions, and adjusting in real time.

In insurance, the impact is broad. Functional agents can support claims intake, reserve estimation, fraud checks, and customer engagement. Helper agents work behind the scenes—extracting documents, retrieving data, or escalating exceptions. This marks a shift from one-off use cases to intelligent automation embedded across the value chain.

But most insurers aren’t scaling yet

Deloitte’s 2024 survey of 200 insurance executives in North America found that 76% have implemented generative AI in at least one business function. Yet most remain in early phases—focused on proofs of concept and exploratory initiatives—without full enterprise deployment.

The challenges are clear:

• Legacy architecture: Most core systems in insurance were designed for human-led workflows, with business logic, user interfaces, and data all tightly coupled. This makes it extremely difficult to embed autonomous AI agents or enable real-time, event-driven actions. These systems are not built for atomic functions or high-frequency API interactions, and any modification often requires expensive, manual intervention.
• Fragmented data: AI agents depend on curated, connected data across functions. In many insurers, silos persist across customer, claims, and policy data, limiting the ability to deliver real-time insights or autonomous action.
• Rigid operating models: Agile ways of working, demand intake processes, and cross-functional squads are not widely adopted. This slows down the iteration and integration of new AI use cases.
• People and change readiness: There’s limited in-house capability to design, govern, and deploy multiagent systems, and risk concerns can delay adoption.

Modernizing the core is essential—but complex

Modernizing the core is not just a tech refresh—it’s a foundational move to enable Agentic AI at scale. But core transformation is difficult due to deeply embedded legacy structures.

Key challenges include:

• Human-centric processing: Legacy systems are built around tightly coupled user interfaces and workflows that assume human-led decision making, leaving little room for AI-driven orchestration.
• Limited externalization: Business logic and data are entangled inside systems, restricting integration with AI agents and modern open architectures. Even SOA-based systems have limited flexibility.
• Costly scalability: Agentic AI demands granular (atomic) functions, fast response times, and high-volume API calls. Monolithic systems cannot meet these requirements cost-effectively. Further, modular architectures lead to a reduction in cost-to-serve

Architecting for scale: What’s the solution?

To overcome these constraints, insurers are shifting toward decomposed system architectures, such as insurance middle office platforms. These enable AI-driven transformation without replacing the full legacy core upfront.

Key features of this approach:

• Support for fragmented user experiences: As more tasks are handled by agents, user interfaces no longer need to centralize every workflow. Business processes can run across multiple touchpoints—AI agents, portals, or mobile—only involving humans when necessary.
• Atomic tooling for evolving processes: Core business functions are broken down into composable APIs (both stateless and stateful), enabling orchestration by AI agents. Regulatory rules and core calculations remain consistent and reusable.
• Data at fingertips: With data access APIs and event streaming, agents can consume real-time insights and predictive analytics without needing to replicate data systems.
• System of record stability: Existing cores—whether SaaS or on-prem—remain the system of record, reducing risky migrations while allowing new AI workflows to be layered on top.
• Scalable architecture: Built on cloud-native microservices, these platforms support limitless API calling, enabling the high-frequency interactions needed by multiagent ecosystems.

This approach not only modernizes the tech stack—it future-proofs it for AI adoption.

Adoption ≠ Scale: Use the 3R Framework

It’s a common trap to equate early adoption with readiness. Building a claims triage bot is not the same as running an intelligent ecosystem of agents across claims, underwriting, and servicing.

To assess readiness to scale, insurers can use the 3R framework:

Insurers that are strong across all three are more likely to move from pilots into scaled, value-generating AI implementations.

Closing thoughts

Agentic AI offers clear advantages across cost, speed, and customer experience—but only if supported by flexible architecture and modern delivery models. Without modernization, most insurers will continue to experiment rather than transform.

To move from AI pilots to scaled systems, insurers must rethink their technology, teams, and how work gets done.

#Insurance #AgenticAI #CoreModernisation #AIatScale #Insurtech #DigitalTransformation #Deloitte #Deloitte Core Modernizations

Sources:

https://www2.deloitte.com/us/en/pages/consulting/articles/generative-ai-agents-multiagent-systems.html

https://www2.deloitte.com/us/en/pages/consulting/articles/ai-agent-architecture-and-multiagent-systems.html

https://www.deloitte.com/au/en/Industries/insurance/perspectives/growth-in-insurance-series-2025-insurance-predictions.html

Scaling gen AI in insurance | Deloitte Insights

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