AI Native Software for Insurance:

AI-native software for insurance puts intelligence inside the operating foundation of the business. AI connects directly with policy administration, billing, claims, customer management, workflows, data, security, and compliance. It can reason from the insurer’s knowledge, orchestrate work across systems, and execute governed actions, not merely generate suggestions.

EIS OneSuite™ powered by CoreGentic™ takes this approach, using core-embedded AI, agentic orchestration, natural language control, and governed execution. Underneath, a MACH-based platform—built around microservices, API-first connectivity, and a cloud-native infrastructure—gives insurers an open and flexible platform for high-velocity insurance.

Can you share examples of insurance companies that have successfully adopted AI-native coretech solutions?

Public examples of insurers operating fully AI-native cores are still emerging, which is understandable: the industry has spent decades running core platforms designed long before today’s agentic AI capabilities existed.

One notable milestone came in January 2026, when iAAH announced they were live on EIS OneSuite™ powered by CoreGentic™. The deployment combines an open, API-rich and event-driven core with embedded AI, agentic automation, and natural-language capabilities.

Earlier EIS customer transformations also show the measurable outcomes made possible by the architectural foundation beneath an AI-native coretech platform.

esure replaced its modern legacy core with EIS OneSuite. They were able to decommission all of their legacy systems within three months of completing the EIS OneSuite rollout, were able to launch new propositions within weeks, and had 92% of their customers making digital mid-term adjustments via self-service as of December 2024.

Tokio Marine & Nichido Fire used EIS claims technology to replace expensive, heavily manual claims processes. The insurer reduced call-center volume by 20%, cut fraud-related costs by 40%, captured five times more fraud, and generated millions of dollars in annual savings.
Atsushi Wada, Claim Department Team Manager, described the impact:

“It’s helping to revolutionize our claims management process for both customers and employees.”

That result came from applying automation and machine learning throughout the claims lifecycle—not placing a disconnected AI tool beside the claims system. Fraud models score claims continuously, digital first notice of loss captures better information, and workflows route work to the right people.

Tower Insurance used EIS to simplify its product portfolio, establish digital distribution, automate policy processes, and extend its insurance platform across New Zealand and Pacific markets. Tower reported a 59% increase in digital sales, growth in digitally logged claims, and an NPS increase from 27 to 40 between fiscal years 2020 and 2021. In Fiji, the share of new business purchased digitally rose from 23% to 88%.

Tower Chief Digital and Data Officer Greg Moore summarized the change neatly:

“We now have digitally enabled products on sale resulting in improved outcomes for customers, and increased efficiency allowing us to take advantage of market opportunities.”

These examples aren’t identical, but the pattern is. Insurers gain speed when core data, workflows, digital experiences, automation, and intelligence operate as one system rather than a collection of loosely connected projects.

For a closer look at the supporting architecture, see EIS OneSuite powered by CoreGentic.

What specific benefits can insurers expect by switching to an AI-native coretech platform?

An AI-native core insurance system changes more than technology costs. It changes how quickly the business can act.

Operationally, insurers can automate routine work across underwriting, policy administration, enrollment, billing, claims, customer service, and compliance. Agentic orchestration coordinates activities across those functions, while human specialists remain responsible for exceptions, judgment calls, and sensitive customer moments.

Financial benefits can include:

• Lower development and maintenance costs
• Fewer manual handoffs and reconciliation tasks
• Faster product and market launches
• Reduced claims leakage and fraud losses
• Less dependency on scarce technical resources
• Lower integration and upgrade costs
• Better use of existing employees’ expertise

The customer benefits are just as practical. Faster quotes, cleaner enrollment, accurate bills, real-time claim updates, personalized service, and consistent experiences reduce the friction customers usually associate with insurance.

Core-embedded AI also changes the economics of compliance. When AI execution happens within governed core processes, the insurer can apply permissions, approved rules, audit histories, and human review requirements consistently. That’s safer than allowing independent AI tools to act across fragmented systems with different controls.

EIS describes this as turning AI intent into governed, auditable execution inside core operations. The result is less rework, faster change, and lower compliance risk.

What is AI-native software for insurance?

AI-native insurance software is designed with AI as part of its operating architecture, rather than adding AI after the platform has already been built.

Traditional insurance software stores records and executes predetermined transactions. AI-enabled software adds models, assistants, or analytics to selected processes. AI-native software goes further: it connects knowledge, reasoning, orchestration, and execution across the insurance lifecycle.

Three characteristics make the difference.

Core-embedded AI gives intelligence access to the business context required to do useful work: products, policies, customers, billing arrangements, claims, workflows, permissions, regulations, and historical decisions.

Agentic orchestration coordinates a sequence of actions toward a defined outcome. An agent might collect required information, validate eligibility, apply business rules, create tasks, request approval, update records, and trigger customer communications.

Natural language control lets authorized users express business intent in plain language. Instead of turning every change into a development project, a product manager might describe a new coverage rule, workflow, or product variation conversationally. The platform can help translate that intent into governed configuration.

This doesn’t mean allowing an AI model to improvise with customer policies. A credible platform applies security, explainability, permissions, testing, approvals, and complete auditability. Intelligence needs guardrails—especially when money, coverage, and people’s livelihoods are involved.

Agentic AI in Insurance explains how these capabilities apply to specific insurance processes.

How is AI-native insurance software different from AI-enabled systems?

The difference between AI-native vs AI-enabled insurance begins with architecture.

An AI-enabled system usually attaches an AI capability to an existing process. Examples include a chatbot connected to a knowledge base, a prediction model feeding scores into a claims application, or a copilot summarizing documents. These tools do improve individual tasks, but their effectiveness depends on the quality of their integrations and the limitations of the underlying core system.

If the core is monolithic, batch-based, policy-centric, or difficult to integrate, the AI inherits those constraints, and may know what should happen without being able to make it happen.

An AI-native platform treats intelligence as a shared operating layer. AI can work with real-time data and governed processes across policy, billing, claims, and customer operations. It can initiate actions through APIs, respond to events, coordinate workflows, and maintain an auditable record.

This turns the core into an AI insurance operating system.

Consider the process of launching a new insurance product. With an AI-enabled legacy system, a generative AI tool might draft requirements or produce sample code. Teams still have to translate that output into the core’s product model, rating logic, workflows, documents, integrations, and user interfaces.

With an AI-native core, natural language control can help turn business intent into configured product components within the system’s governed framework. Agentic orchestration can coordinate supporting activities. The insurer spends less time carrying an idea between disconnected teams and tools. 

EIS GuideMe^TM is a perfect example of helping ambitious insurers turn natural language into agentic orchestration that gets actual work done in auditable, governed workflows.

Why are insurers moving to AI-native core systems?

Insurers aren’t adopting an AI core system for insurers because AI suddenly became fashionable. They’re doing it because the industry’s operating demands no longer match the capabilities of older cores.

For example, these are some challenges that insurers face with modern legacy insurance systems that aren’t AI-native:

• Product launches require long development cycles. 
• Integrations depend on custom code. 
• Customer information lives in separate applications. 
• Upgrades become projects. 
• Adding another AI tool can increase complexity instead of reducing it.

Meanwhile, insurers are expected to support:

• Rapid product experimentation
• Embedded and partner distribution
• Real-time customer interactions
• Personalized pricing and service
• Digital self-service
• Automated claims
• Continuous regulatory change
• More data from more sources
• New AI models and specialist agents

A modern insurance core system needs to accommodate that change without requiring another architectural rescue mission every few years.

This is what enables insurers to move at the pace the market requires: the ability to change products, processes, experiences, and partnerships—without losing governance or operational control.

What should insurers look for in AI-native insurance software?

The phrase “AI-native” will appear on plenty of sales slides, but insurers need to inspect what’s behind it.

Starting with architecture, a credible insurance AI platform should be MACH-based:

Microservices: Capabilities are modular, independently deployable, and easier to evolve. Insurers can modernize incrementally instead of treating every improvement as a full-core replacement.

API-first: Functions and data are available through documented interfaces, making it easier to connect distributors, data providers, payment systems, AI services, portals, and internal applications.

Cloud-native: The platform is engineered for elasticity, resilience, automated deployment, and frequent improvement. Hosting an old application in the cloud doesn’t make it cloud-native.

Headless: Core capabilities are separated from the user interface. Insurers can deliver experiences through customer portals, broker workspaces, mobile applications, embedded journeys, or new channels without rebuilding core logic.

Beyond MACH, look for:

• AI embedded across the core rather than limited to one application
• A domain-specific insurance knowledge base
• Governed agentic orchestration
• Natural language control for business and technical users
• Explainability, audit trails, approvals, and role-based security
• Real-time, event-driven data processing
• Customer-centric records spanning multiple policies and products
• Configurable products, rules, workflows, and experiences
• Support for multiple lines of business
• Non-disruptive upgrades and continuous delivery
• Freedom to use external models, data, and ecosystem partners

Be wary of platforms that call themselves open but restrict insurers to a vendor-controlled marketplace. An open and flexible platform should give the insurer options, not another locked door with a newer handle.

How do insurers build a business case for AI-native software?

A business case for AI-native software for insurance shouldn’t begin with, “We need agentic AI.” It should begin with a business problem that needs solving.

To make a solid business case for AI-native software, choose several high-value journeys: product launch, quote-to-bind, group enrollment, billing reconciliation, first notice of loss, fraud investigation, claim settlement, or policy servicing, and establish the current baseline for each.

Measure:

• Cycle time
• Manual touches
• Error and rework rates
• Cost per transaction
• Call volumes
• Leakage or fraud losses
• Abandonment and conversion
• Customer satisfaction
• Development effort
• Compliance exceptions
• Time spent maintaining integrations

Next, model the value of improving those measures. For example: 

• A 20% reduction in manual work may release capacity. 
• A faster product launch may produce additional premium. 
• Better fraud detection may improve the loss ratio. 
• Straight-through processing may reduce both handling cost and customer waiting time.

The business case should also quantify avoided costs. Maintaining an old AI insurance technology stack involves more than license fees. Include custom integrations, upgrade projects, duplicated data, vendor dependency, specialist skills, delayed releases, and the opportunity cost of products that never reach market.

Finally, adopt a phased value approach across the new AI-native software, not as isolated AI features layered onto existing systems. Start with one or two end-to-end insurance journeys with measurable business outcomes, validate the platform architecture, integration, and governance model in production, then expand across additional capabilities. 

When AI is embedded within a full insurance platform like EIS OneSuite™, value should compound across policy, claims, billing, and customer operations.

What is insurance coretech?

Insurance coretech is the technology foundation used to create and administer products, issue and service policies, manage billing, process claims, maintain customer records, and coordinate business operations.

Its evolution broadly follows three stages.

1: Legacy systems digitized records and transactions but depended heavily on batch processing and manual work. 

2: Modern legacy platforms introduced graphical interfaces and multi-tier architectures, but many remained monolithic, policy-centric, and difficult to integrate.

3: Today’s coretech is modular, customer-centric, API-first, event-driven, and cloud-native. An AI-native coretech platform adds an intelligent operating layer that can interpret business intent, reason using domain knowledge, orchestrate work, and execute governed actions.

This means coretech is more than a system of record; it can become an insurance AI platform and system of action.

EIS Platform™ already provides an open, event-driven, real-time-responsive foundation for customer, policy, billing, and claims operations. EIS OneSuite™ powered by CoreGentic™ extends that foundation with embedded intelligence and agentic execution.

How does AI-native software fit into an insurer’s digital transformation?

A digital transformation can’t stop at a nice-looking customer portal, because customers eventually feel what’s underneath it.

When a simple request triggers manual rekeying, overnight batches, disconnected approvals, or a call to another department, the digital experience is mostly decoration, and the core system still remains the main constraint to true digital transformation.

AI-native software gives transformation an operational backbone. In an AI insurance operating system, cloud-native infrastructure provides scale, microservices support modular change, APIs connect the ecosystem, and event-driven processing keeps data and workflows moving in real time.

Agentic orchestration then coordinates work across that architecture.

For example, when a policyholder reports an auto loss, the system could:

1. Retrieve the customer’s policy and coverage.
2. Guide the customer through relevant questions.
3. Analyze submitted images and information.
4. Calculate and update a fraud-risk score.
5. Assign the claim based on complexity and adjuster expertise.
6. Contact approved repair or rental partners.
7. Request human approval where required.
8. Update the customer through their preferred channel.
9. Record every action for audit and compliance.

EIS claims solutions already demonstrate parts of this model through digital FNOL, continuous fraud assessment, event-driven workflows, API-based partner connections, and real-time customer updates.

The broader AI insurance technology stack should apply the same approach across product, policy, enrollment, billing, claims, and service. That’s when transformation stops being a collection of digital projects and becomes a faster way to run the insurer.

What capabilities define a truly AI-native insurance platform?

A truly AI-native insurance software platform should combine six capabilities.

Core-embedded intelligence

AI needs direct, governed access to the insurance core’s products, customer relationships, rules, workflows, policies, claims, billing, and events. Otherwise, it’s forced to operate through brittle integrations or incomplete data.

Insurance-specific knowledge

General models understand language, but they don’t automatically understand an insurer’s products, operating procedures, underwriting appetite, regulatory obligations, distribution arrangements, and approval authorities.

A domain-specific knowledge base grounds the platform in the insurer’s context, which makes AI responses and actions more relevant, accurate, and controllable.

Natural language control

Business users should be able to describe what they need without translating every request into a technical specification.

Natural language control can help users configure products, processes, rules, and journeys in plain language. The platform must still validate the request, identify conflicts, apply permissions, test the configuration, and route it for approval.

Agentic orchestration

An AI assistant responds and gives you answers or ideas, but an AI agent acts toward a goal.

Agentic orchestration can sequence tasks across people, systems, and other specialist AI agents. It can recognize that information is missing, retrieve it from an approved source, perform a calculation, apply a rule, update the core, and trigger the next action.

The system should also know when to stop. High-impact underwriting, coverage, payment, and compliance decisions may require human authorization. Good orchestration includes escalation, not just automation.

Governed execution

Insurance AI needs role-based permissions, approval controls, traceable decisions, testing, monitoring, data protection, and audit histories. An insurer should be able to determine what the AI did, which information it used, why an action was permitted, and who approved it.

Governance can’t be a document written after implementation. It must be part of the execution layer.

Open MACH architecture

AI will keep changing, so your core system needs enough architectural freedom to adopt new models, agents, data services, channels, and partners without another core transformation.

This means microservices, API-first integration, cloud-native operations, and headless delivery. It also means avoiding hard dependencies on a single model or closed ecosystem.

Together, these capabilities create an AI core system for insurers that supports high-velocity insurance. Teams can launch, learn, and adjust more quickly. Employees spend less time shepherding work through fragmented systems. Customers get faster and more consistent service. The insurer can innovate without turning every good idea into a two-year technology program.

See More About EIS AI Capabilities

AI-native software is redefining what insurers can expect from their core: greater agility, smarter automation, governed intelligence, and faster execution across the insurance lifecycle.

EIS OneSuite™ powered by CoreGentic™ brings those capabilities together in an open, customer-centric, MACH-based platform built for ambitious insurers.

Ready to see what an AI-native platform like EIS could do for your business? 

Book a call with us to explore how EIS can help you modernize operations, accelerate innovation, and build an insurer that’s ready for whatever comes next.