AI-Native Insurance Companies

As explained in AI native software for insurance, AI’s greatest value doesn’t come from a collection of disconnected tools, but from giving AI the context, authority, governance, and real-time data needed to improve decisions and execute work throughout the insurance lifecycle.

This inherently changes the role of the core system — it’s no longer just a place to store policies and transactions, but becomes an intelligent operating foundation that helps the insurer continuously learn, act, and adapt to market evolutions and expectations.

What are some real-world examples of insurance companies successfully using AI to transform their operations?

The strongest AI-native insurer examples share a common trait: AI is connected to operational workflows rather than sitting in a separate innovation lab.

Tokio Marine & Nichido Fire used EIS claims technology to digitize first notice of loss, automate claims activity, and improve fraud detection. The machine learning models continuously assess claims and update risk scores as new information arrives. The carrier reduced call volume by 20%, cut fraud-related costs by 40%, detected five times more fraud, and reported millions in annual savings. 

AI doesn’t merely tell an investigator that a claim looks suspicious. It helps collect better information during intake, scores the claim, routes it to the right team, and continues evaluating risk throughout the lifecycle. EIS ClaimSmart™, for example, combines continuous fraud assessment with automated, personalized claims workflows.

Allianz has taken a similar operational approach with Project Nemo, an agentic AI system designed for high-volume food spoilage claims following natural catastrophes. Seven specialized agents handle steps including coverage checks and fraud detection, while a human retains authority over the final payment decision. Allianz reported an 80% reduction in processing and settlement time, cutting turnaround from several days to hours. The solution was launched in fewer than 100 days.

Among startups, Lemonade is a useful AI-native insurance startup example. Its 2025 annual report states that its AI claims bot received first notice of loss without human intervention 96% of the time. Roughly 55% of claims were automated from start to finish, while more than half of customer inquiries were handled by its customer-service AI. Complex and questionable claims were escalated to human specialists rather than being automatically rejected.

Root is another carrier using AIs, machine learning, and automated systems to support real-time pricing, underwriting, and marketing decisions. Its model illustrates how an AI-native insurance company can treat data science as part of the operating system rather than a supporting analytics function.

These examples cover different levels of maturity. Some are established carriers transforming selected domains. Others were designed around AI from the beginning. Both show what happens when intelligence moves closer to the core business process: faster decisions, less manual work, and better customer interactions.

What is an AI-native insurance company?

An AI-native insurance company is an insurer whose technology, processes, and operating model are designed to use AI throughout the business.

The word “native” does the heavy lifting. In a traditional insurer, AI is often a separate tool connected to a legacy system. It might summarize documents or answer employee questions, but it lacks the authority and context to complete work.

An AI-first insurance company goes further. Its AI can work with live policy, billing, customer, and claims data. It can interpret an event, apply business rules, recommend or perform an action, document what happened, and involve a human when judgment or approval is required.

Two capabilities are central:

Core-embedded AI places intelligence within the operational platform. AI can use current data, policy terms, product rules, permissions, and workflow context instead of relying on stale extracts or disconnected databases.

Agentic orchestration coordinates multiple tasks, systems, and AI agents to complete a broader objective. A claims agent, for example, might gather documentation, check coverage, identify fraud indicators, contact a repair provider, prepare a settlement recommendation, and route the case for approval.

The carrier still controls the rules, but AI supplies speed, pattern recognition, and execution capacity. Humans retain responsibility for sensitive, complex, or regulated decisions.

How does an insurer become AI-native?

Becoming an AI-native insurer isn’t a matter of purchasing more AI licenses or integrating more AI tools into your workflows. Instead, it requires coordinated business and technology change.

The first step is selecting valuable operational domains. Claims intake, fraud detection, underwriting submissions, enrollment, product configuration, customer service, and billing exceptions are common starting points because they contain repetitive work, fragmented data, and measurable outcomes.

Next comes the foundation. An AI-native insurance transformation needs clean, accessible data and an architecture that lets intelligence operate across systems. That’s why MACH-based design, like what’s found in EIS OneSuiteTM, matters:

• Microservices-based capabilities can be changed and scaled independently.
• API-first services can exchange data and trigger actions across the ecosystem.
• Cloud-native SaaS supports elastic capacity and continuous updates.
• Headless design separates experiences from back-end functions, making it easier to deliver new interfaces and channels.

EIS is listed by the MACH Alliance as a certified independent software vendor, and EIS OneSuite is built around open, composable, cloud-native architecture.

Governance must also be designed alongside the technology. Insurers need defined decision rights, model monitoring, bias testing, data provenance, audit trails, permissions, human-review thresholds, and procedures for correcting problematic outcomes.

Finally, teams need to redesign the work itself. Automating a bad process only produces bad results faster. Becoming an AI-native insurer means deciding which steps should disappear, which should be orchestrated by agents, and where human expertise provides the most value.

What separates AI-native insurers from merely digital insurers?

A digital insurer gives customers and employees online tools. An AI-native carrier uses intelligence to decide what should happen next, and helps make it happen.

A digital claims portal might let a customer upload photographs and check a status, but an AI-driven insurance company can analyze the submission, retrieve policy information, ask relevant follow-up questions, evaluate fraud risk, assign the right adjuster, initiate vendor communications, and personalize updates.

The difference is depth.

Digital transformation improves the interface, but an AI-native transformation changes the operating logic behind it.

A merely digital insurer may still depend on batch files, manual handoffs, static rules, and employees copying information between screens. It has put a cleaner dashboard over the same machinery, and the same bottlenecks apply, even if those bottlenecks are easier to manage with the slightly improved technology.

An AI-native carrier combines real-time events, core data, business rules, machine learning, generative AI, and agentic orchestration. These enable faster insurance workflows, including the ability to launch, learn, adjust, and scale without turning every change into a major IT project.

McKinsey argues that insurers won’t capture AI’s full value through isolated use cases alone. Instead, they need a comprehensive approach that rewires the enterprise, redesigning processes, operating models, and technology across entire business domains.

What operating model changes does becoming AI-native require?

Operating model changes required for becoming AI-native often include things like modified workflows and SOPs, workforce updates, and continuous governance.

To some, an AI-native insurance company seems to operate more like a technology company than a traditional carrier with a large IT department attached.

Cross-functional product teams replace lengthy chains of requirements and handoffs. Business, operations, data, compliance, and engineering work together around a customer journey or operational domain. Their goal isn’t to “install AI,” but to improve outcomes such as claim cycle time, quote conversion, underwriting capacity, or billing accuracy.

The workforce changes too. AI takes on repetitive investigation, data collection, summarization, routing, and configuration work. Employees spend more time on exceptions, relationships, judgment, and improvement.

That doesn’t mean removing people from insurance. Insurance is full of emotionally and financially consequential moments. The better model removes administrative clutter so people can focus on those moments.

Governance must also become continuous. An AI-driven insurance company can’t afford to wait for an annual review to discover that a model has drifted or a workflow is producing inconsistent results. Monitoring, validation, feedback, and approval controls need to operate continuously alongside the AI.

Deloitte found that 76% of surveyed U.S. insurance executives had implemented generative AI in at least one business function. But poor data foundations, legacy infrastructure, and weak collaboration between IT and business functions were  causes of unsuccessful implementations. The strongest success factor was close cooperation among business, technology, data, and talent teams.

How do incumbent carriers transition to an AI-native model?

In order to transition to an AI-native model, incumbent insurers don’t have to replace every system in one dramatic weekend. 

A practical AI-native insurance transformation starts with a focused business problem and a platform capable of expanding over time, like EIS OneSuite™ powered by CoreGentic™. An insurer might modernize claims first, introduce a new digital product on a separate book of business, or add an AI-native customer and policy layer while existing systems remain in place.

Open APIs and event-driven architecture make this modular approach possible. New capabilities can operate alongside current platforms, exchange data in real time, and gradually take responsibility for more processes.

Change management deserves equal attention. McKinsey estimates that change management makes up about half the work required to secure AI’s financial and nonfinancial impact. Employees must understand the new process, trust the controls, and know when to rely on AI, and when to challenge it.

Becoming an AI-native insurer is therefore a sequence of controlled operational improvements, not one oversized technology bet.

What returns do AI-native insurance companies see?

AI-native insurance companies see returns on their efforts, but those returns vary by use case. However, the most useful measures fall into four groups: speed, cost, risk, and growth.

Speed includes reduced claim cycle time, underwriting turnaround, product configuration, and time to market.

Cost includes reduced manual work, fewer calls, lower loss-adjustment expenses, and less rework. 

Risk includes increasing fraud detection, lowering claims leakage, improving decision consistency predictability, and compliance. 

Growth includes faster product launches, new distribution and customer access channels, better conversion on quotes, and improved retention. An AI-native system like EIS OneSuite lets insurers reuse products, rules, workflows, and agents across markets instead of rebuilding them each time.

The qualitative returns matter too: 

• Customers get fewer repetitive questions, clearer updates, and faster resolutions. 
• Employees spend less time chasing information. 
• Compliance teams gain traceable decisions and audit records.

The best AI-native insurer examples don’t measure success by how many models they deploy, but by whether the business moves faster, costs less to change, makes better decisions, and creates more value for customers.

What does an AI-native carrier’s technology stack look like?

An AI-native carrier’s technology stack must connect intelligence to the systems where insurance work actually happens. It includes core-embedded AI, agentic orchestration, a knowledge layer, governance, a MACH-based architecture, and natural language controls.

At the foundation is a cloud-native core covering customer, policy, billing, and claims operations. Above that sits an event layer that detects changes and initiates relevant actions. Open APIs connect data, distribution partners, external services, and specialized models.

An AI-native carrier tech stack includes:

Core-embedded AI: Intelligence operates with live business context rather than working from disconnected copies of data.

Agentic orchestration: Specialized agents coordinate complex, multi-step processes across policy, billing, claims, and customer operations.

A knowledge layer: Product rules, underwriting guidance, regulatory requirements, workflow instructions, and organizational knowledge ground AI activity.

Governance and observability: Permissions, human approvals, data lineage, audit histories, model monitoring, bias testing, and security controls make actions traceable.

MACH-based architecture: Microservices, APIs, cloud-native SaaS, and headless experiences allow capabilities to evolve independently.

Low-code and natural-language controls: Business teams can configure products, rules, journeys, and workflows without waiting through lengthy development cycles.

EIS OneSuite™ powered by CoreGentic™ brings these elements together. It embeds AI, natural-language control, and agentic orchestration into OneSuite’s open, modular, event-driven core. AI agents can participate in workflows triggered by a human instruction or a business event, while permissions, business rules, human oversight, and the knowledge base provide context and control.

The platform supports reusable automation across PolicyCore®, BillingCore®, ClaimCore®, and CustomerCore™, rather than creating separate AI islands for each department. Its modular design also lets insurers add capabilities as their strategy develops.

The Future of Insurance Is Intelligent

AI-native software is redefining what’s possible for insurers, driving smarter decisions and seamless operations across the core platform. 

If you’re ready to see how agile, intelligent technology can transform your insurance business, book a call today.