Key Challenges in Moving from Legacy to AI-Native Insurance Systems

What challenges do insurers typically face when transitioning from legacy systems to AI-native software?

When transitioning from legacy systems to AI-native software, some challenges insurers typically face include integration complexity, data silos, and outdated infrastructure.

The first obstacle is integration complexity. Many insurers operate a mixed technology stack containing legacy core systems, modern legacy applications, point solutions, third-party services, and custom interfaces. Replacing or connecting these systems without disrupting policy servicing, billing, claims, or distribution takes careful planning.

Data silos make the job harder. Customer, policy, billing, and claims information may use different structures, definitions, or identifiers, and AI can’t produce trustworthy decisions from fragmented or contradictory inputs. Before insurers automate anything important, they need consistent data and clear ownership. 

Outdated infrastructure also limits scalability. Systems built around batch processing and tightly coupled components weren’t designed for real-time decisions, agentic workflows, or continuous model improvement. They can support isolated AI tools, but each new connection adds cost and technical debt.

These insurance core transformation challenges are why a phased approach often works better than a single, high-risk replacement. Modular, cloud-native platforms let insurers modernize priority capabilities while maintaining essential operations. EIS OneSuite™ provides open APIs, event-driven architecture, and configurable workflows that support transformation at a carrier’s chosen pace.

The deciding factor in any legacy to AI-native migration is alignment. Business leaders and IT teams need to agree on the outcomes, operating model, governance, and measures of success. Otherwise, transformation becomes an expensive software project rather than a business change.

What best practices can insurers follow to ensure data integrity and security during the migration from legacy systems to AI-native platforms?

Best practices insurers can follow to ensure data integrity and security during the migration from legacy systems to AI-native platforms include treating data migration as a governed business program, taking an inventory of data sources, establishing quality thresholds, encrypting sensitive information, governing role-based access, and continuous monitoring, among other strategies. 

Start by treating data migration as a governed business program, not a copying exercise. Insurers should inventory data sources, define authoritative records, establish quality thresholds, and document every transformation. Reconciliation should happen repeatedly, with audit trails showing what changed, why it changed, and who approved it.

Sensitive information should be encrypted in transit and at rest, with role-based access, segregation of duties, retention controls, and continuous monitoring. Parallel testing can compare outputs from legacy and new systems before workloads move into production.

An AI management system adds another layer of discipline. ISO/IEC 42001 specifies requirements for establishing, maintaining, and continually improving governance around organizational AI use, including accountability, transparency, risk, and oversight. (ISO)

Regulatory expectations must also shape the migration plan. As AI-native platforms begin operating in various jurisdictions, it reinforces the need for documented governance, testing, consumer protection, and oversight of third-party AI systems.

Platform stability matters here. An open architecture with business activity monitoring, access controls, encryption, anomaly detection, and auditable workflows helps insurers preserve data integrity without turning compliance into a scavenger hunt.

How can insurance companies effectively manage employee training and change management to maximize adoption of new AI-native software?

Insurance companies can effectively manage employee training and change management to maximize adoption of new AI-native software through tailored training, leadership alignment, and empowerment through natural language control, with guardrails built in. 

Successful AI adoption in insurance begins with explaining what will change for each role. Claims handlers need different training from product managers, underwriters, compliance teams, and developers, hence the need for tailored, role-based training. 

Training should use real workflows, real decisions, and realistic exceptions. Employees need to understand when AI can act, when human approval is required, and how to question or override an output. That builds confidence without encouraging blind trust.

Leadership alignment is equally important. Executives must reinforce that AI-native operations aren’t simply a cost-reduction exercise. The goal is to remove repetitive work, accelerate decisions, and give employees more time for judgment, problem-solving, and customer support.

Natural language control and agentic orchestration can reduce the technical barrier to change, allowing authorized business users to configure products and workflows more directly. EIS OneSuite™ powered by CoreGentic™ embeds these capabilities alongside governance and human oversight. 

What is the significant challenge in implementing AI systems in the insurance industry?

The biggest challenge in implementing AI systems in the insurance industry isn’t the AI model, but the environment surrounding it.

Entrenched processes, unreliable data, disconnected systems, unclear accountability, and regulatory uncertainty can undermine even an impressive model. AI layered onto a rigid legacy core may automate one task while leaving the wider insurance lifecycle just as fragmented.

Core-embedded AI changes the equation. EIS OneSuite powered by CoreGentic combines AI, agentic orchestration, natural language capabilities, open integration, and insurance-specific context within the operational core. 

This approach addresses persistent insurance core transformation challenges by connecting intelligence to the workflows, controls, and data that run the business. The result is stronger compliance, faster adaptation, and better customer experiences.

Get Started with AI-Native Insurance

AI-native software is redefining what’s possible in insurance, driving smarter workflows, faster decisions, and greater agility across the value chain. 

If you’re ready to see how native AI can transform your core operations, connect with our team today to see how it can accelerate your business goals in all areas of insurance.