Agentic AI in Insurance Starts With What Legacy Systems Know
Insurance leaders are moving quickly from AI pilots to real operational use cases: claims automation, customer communications, knowledge management and workflow orchestration across the enterprise. But for many carriers, the biggest barrier to scaling agentic AI is not model quality or platform choice. It is the reality that core business logic still lives inside decades-old policy, claims and operational systems.
That logic is often critical, differentiated and deeply intertwined with how the business actually runs. It includes product rules, claims pathways, exception handling, servicing processes, data dependencies and institutional knowledge accumulated over years of change. When that knowledge remains trapped in legacy code, fragmented workflows and undocumented workarounds, autonomous or semi-autonomous agents lack the context they need to act safely, reliably and at scale.
For insurers, this is the hidden prerequisite for agentic AI: modernization that makes enterprise context visible, traceable and usable.
Why insurers cannot skip the modernization step
Insurance is a high-stakes operating environment. Business-critical decisions require control, continuity and human oversight. AI agents can accelerate work, coordinate across systems and reduce manual effort, but only when they are grounded in the rules and dependencies that govern the enterprise.
Without that foundation, organizations risk building agents that can respond fluently but cannot execute with confidence. They may struggle to understand how core systems connect, where process exceptions occur, which business rules apply or how a decision should be traced back to source logic. In practice, this leads to fragmented automation, stalled pilots and limited production value.
That challenge is not theoretical. Across industries, enterprises are already using AI regularly, yet only a small minority say AI is core to how their operations run. The gap is organizational as much as technical: many businesses have adopted AI, but have not yet transformed the systems, workflows and operating models needed to capture its full value. For insurers, where legacy environments are especially dense and tightly coupled to day-to-day execution, the need to modernize becomes even more urgent.
Enterprise context is what makes agentic systems work
Agentic AI is most effective when it can reason and act within a clear understanding of the enterprise. That requires more than access to documents or APIs. It requires context that connects business systems, rules and workflows into a usable operational picture.
Publicis Sapient’s approach is built around that principle. Its enterprise context graph creates a living map of the software estate, connecting code, architecture and data to business rules, tribal knowledge and operational dependencies. This matters because agents do not operate in abstractions. They operate inside real businesses, where systems are interconnected, actions have downstream consequences and resilience depends on understanding how the environment works.
In insurance, that kind of context helps bridge the gap between core platforms and new AI-enabled workflows. It creates a foundation for agents that can support underwriting operations, claims journeys, servicing tasks and internal knowledge flows with greater precision, traceability and control.
Making legacy logic usable with Sapient Slingshot
Sapient Slingshot is designed for exactly this challenge. It modernizes legacy systems by turning existing code into verified specifications and generating modern software with full traceability. Rather than treating legacy estates as an obstacle to work around, it treats them as a source of business truth that must be understood and preserved.
This is especially important in insurance, where core systems often contain decades of embedded logic that cannot simply be replaced by assumption. Sapient Slingshot uses AI to read, interpret and extract business rules from legacy systems, helping preserve institutional knowledge while making it accessible for modernization and future execution. The platform delivers up to 99% code-to-spec accuracy and can accelerate delivery timelines by as much as 3x.
That combination of speed and verification changes the modernization equation. Teams can create a clearer, trusted view of what legacy environments actually do before redesigning applications, workflows or agent interactions on top of them. Instead of introducing unnecessary transformation risk, modernization becomes a way to reduce it.
From modernization to governed agentic execution
Once enterprise context is surfaced and verified, insurers are in a stronger position to scale agentic AI with confidence.
Publicis Sapient’s broader platform portfolio connects these stages of transformation. Sapient Slingshot helps unlock and modernize legacy environments. Sapient Bodhi provides an enterprise-grade platform to design, build and orchestrate intelligent agents and workflows at scale. That orchestration is designed for real business conditions, with the context, governance and observability needed for enterprise deployment.
This creates an important continuity between core systems and AI-enabled operations. Rather than standing up disconnected agents around brittle legacy processes, insurers can build on a foundation where logic is understood, workflows are traceable and governance is embedded. That supports more secure movement from concept to production while maintaining flexibility, compliance and human oversight.
The result is not AI layered on top of operational complexity. It is AI grounded in the way the enterprise actually works.
The business outcomes insurers should expect
- Faster time to market: By converting legacy code into verified specifications and creating a usable map of business logic, teams can move more quickly from analysis to execution. Modernization and new AI-enabled workflows no longer happen as disconnected programs.
- Lower transformation risk: Full traceability helps insurers understand how critical logic is preserved as systems evolve. That reduces uncertainty in core-platform change and supports safer rollout of agentic capabilities.
- Better continuity across the enterprise: Enterprise context helps connect policy, claims and operational systems to new orchestration layers, improving consistency between what legacy systems know and what agents do.
- Greater production readiness: Agentic systems require governance, observability and business context to operate at scale. Building on a modernized, verified foundation makes it easier to embed these from the start.
- More durable value from AI investments: Instead of trapping gains inside isolated pilots, insurers can redesign workflows and operating models around capabilities that are grounded in enterprise reality.
The path forward for insurers
The next wave of AI in insurance will not be won by organizations that deploy the most agents. It will be won by those that give agents the right foundation to act well inside complex, regulated and legacy-rich environments.
That foundation starts with modernization. It requires making decades of policy, claims and operational logic visible. It requires creating traceability between source systems and future-state workflows. And it requires building enterprise context that agents can use to reason, decide and execute responsibly.
Publicis Sapient helps insurers take that path with a connected approach: modernize legacy systems with Sapient Slingshot, ground execution in enterprise context, and scale governed agentic workflows with platforms designed for real operational complexity.
For insurers, that is how agentic AI moves from promise to production: not by bypassing legacy, but by unlocking what legacy systems already know.