Why Insurers Should Augment Core Systems With AI Instead of Replacing Them

Every few years, an insurance leadership team decides the core platform is the problem. The policy admin system is too rigid, the claims platform too slow, and the billing engine is too old to touch. The proposed fix is a multi-year replacement program, and the board signs off on the promise of a clean slate.

The reality is that the core systems were rarely the bottleneck. The manual work wrapped around them was. That's where AI agents earn their keep: reading, extracting, routing and writing back into the platforms you already run, so you modernize the operation without ripping out the foundation underneath it.

What Are Insurance Core Systems

Insurance core systems are the platforms that hold an insurer's authoritative record of policies, claims and premiums, led by the policy administration system, the claims management system and the billing system found at the center of almost every carrier and MGA. The policy administration system (PAS) manages quoting, issuance, endorsements and renewals. The claims management system tracks a loss from first notice through settlement, and the billing system handles premiums, payments and commissions. Around them sit rating engines, document management, CRM, broker portals and a data warehouse that tries to reconcile all of it.

These platforms are good at what they were built for. They enforce product rules, store transactions, calculate premiums and keep an auditable history that regulators and reinsurers can rely on. Platforms like Guidewire, Duck Creek and Majesco have spent decades hardening exactly that.

What they were never built for is the unstructured work that feeds them. A submission arrives as a broker email with an ACORD form, three PDFs of loss runs and a scanned supplemental. A claim arrives as a phone call, a photo and a demand letter. Before any of it reaches the core system as clean, structured data, someone has to read it, interpret it and type it in.

That gap between how information arrives and how core systems need to receive it is where most operational cost hides. It's also why replacing the core system so rarely fixes the problem.

Why Core System Replacement Creates More Problems Than It Solves

Core system replacement creates more problems than it solves because data migration and broken integrations land at the same time the business keeps writing policies and paying claims on the old platform.

Data migration surfaces decades of inconsistent records. Product configurations that lived in someone's head have to be rebuilt from scratch. Integrations with rating, billing, reinsurance and reporting all break at once. And while the program runs, the business keeps writing policies and paying claims on the old system, so teams end up maintaining two platforms instead of one.

The budget pressure is already severe before a replacement starts. A Wipro and Coleman Parkes survey of US insurance executives found that 93% of insurers spend the majority of their IT budget on run costs, with 58% putting more than two-thirds toward maintaining existing systems. A replacement program doesn't release that money. It adds a second line item on top of it for the two to five years the program runs.

A new PAS still expects clean, structured input. Submissions that arrive as unstructured broker emails today arrive the same way on go-live day. The intake team still reads every document, keys every field and chases every missing loss run. You've spent three years and tens of millions to move the same manual work onto a newer screen.

What happens when the program finishes and your underwriters are no faster than before? That's the question replacement business cases rarely answer.

Where AI Augmentation Works Best Across Insurance Core Systems

AI augmentation works best across insurance core systems where volume is high, inputs are unstructured and the rules are known. Five areas clear that bar:

Automating FNOL and Claims Intake Without Replacing the Claims Platform

A typical FNOL call runs 15 to 20 minutes, and the adjuster often reworks the intake anyway because details were missed. One US carrier deployed Notch voice agents to take FNOL calls from first contact to structured claim handoff. The agents collect loss details, adapt questions by loss type and jurisdiction, escalate high-risk cases through a logged path and push completed records into the existing claims system. Across production deployments, Notch has delivered 70% to 73% autonomous resolution and a 6x faster median time-to-resolution, with no change to the claims platform itself.

Running Underwriting Triage on Top of Existing Submission Workflows

Underwriters lose hours preparing files before any underwriting begins. A US MGA writing 10 P&C lines across 25 states used Notch to automate submission intake and triage. ACORDs, broker emails, scans and faxes became one structured record, checked against appetite and state-specific rules, then routed to the right queue. The result was 99% accuracy across extraction and rules-based processing and more than 250% efficiency gains in intake operations.

Extracting and Writing Structured Data Back Into the PAS

You get the value of extraction once extracted fields land in the policy admin system without anyone retyping them. Agents classify documents, pull named insureds, limits, class codes and locations, then write them back through existing integrations, with every field linked to its source document. That write-back step is what separates automated data extraction in insurance from another OCR tool that leaves your team copying and pasting. 

Coordinating Workflows Across Disconnected Core Systems

Operational delays build up in the handoffs between core systems. A single endorsement request can touch the PAS, billing, the document system and a broker portal. Agents orchestrate those steps in sequence, applying business rules at each handoff, so a request that used to wait in three queues moves through in one pass.

Scaling Automation Without Scaling Infrastructure

Adding a new automation workflow takes rule and integration configuration on the platforms you already run, with no new core infrastructure to provision. Notch has gone live in VPC deployments in as little as seven weeks, and each new use case builds on the integrations already in place.

AI Augmentation vs Complete Core System Replacement

AI augmentations and complete core system replacements solve different problems. Replacement modernizes the record. Augmentation modernizes the work. For most carriers and MGAs, the work is where the cost and the delay sit.

None of this rules out replacement entirely. An end-of-life or unsupported platform needs to go. But even then, an execution layer that already handles intake and routing makes the eventual migration smaller, because the work no longer depends on the screens being replaced.

Governance, Audit and Human Oversight for Insurance Core Systems and AI

The fastest way to stall an AI program in insurance is to treat governance as something to bolt on after the pilot. Regulators have already set expectations. The NAIC Model Bulletin on the use of AI systems by insurers, adopted in December 2023 and taken up by a growing number of states, expects a written AI program, a clear accountability structure across claims, underwriting, compliance and legal, controls matched to consumer risk, and documented diligence on third-party AI vendors.

That last point matters when you're evaluating augmentation partners. Your vendor's controls become your controls in a market conduct exam. When you ask vendors how their agents behave, the answer should be specific: which actions are deterministic, which require human approval, and what gets logged.

Good augmentation architecture separates execution from judgment. Agents extract, classify, validate and route. They don't make adjudication, coverage or legal decisions. Those stay with licensed adjusters and underwriters, who now receive cleaner files with every data point linked back to the document it came from.

Three controls do most of the work. Deterministic validation layers check agent output against business rules before anything is written to a core system. Configurable guardrails block high-risk actions, enforce jurisdiction-specific handling and escalate out-of-scope cases through a logged path. Full traceability, so every extracted field, routing decision and system update can be reconstructed for an auditor or regulator

Augmentation also tends to improve auditability compared with the manual process it replaces. A human keying data from a PDF leaves no record of why a field was populated a certain way. An agent leaves a source link, a timestamp and the rule that triggered the action, which is what AI guardrails look like in production rather than on a vendor slide. Data controls carry over too. Field-level role-based access in platforms like Guidewire, data-zone selection and retention rules apply to agent activity the same way they apply to people, which is the baseline for deploying AI in regulated insurance operations without opening new compliance gaps. 

How to Modernize Insurance Core Systems With AI

Modernize insurance core systems with AI by starting with the workflows that cause measurable pain, connecting agents to the platforms you already run and expanding only after the first use case proves its numbers. The insurers getting results start small and expand from there. Four steps keep the program grounded.

Identifying Workflow and Data Bottlenecks

Start by mapping where your people spend time. Map the queues with the longest backlogs, the tasks with the most rework and the handoffs that miss SLAs. Time-demand letters sitting unread, submissions waiting days for triage and endorsements bouncing between teams are usually the first candidates.

Assessing Existing System Integration Capabilities

Inventory what your core platforms already expose: APIs, event queues, batch imports, document repositories. Modern platforms expose APIs that agents use to read and write records. Agents work through the same screens your staff use today on platforms that lack that integration surface.

Selecting Suitable AI Automation Use Cases

The best first use cases combine high volume, unstructured inputs and well-defined rules. Intake, triage and data extraction fit that profile. Before committing, weigh whether to build or buy using a framework like CLEAR, because the edge cases in insurance workflows are where internal builds tend to stall.

Measuring Operational Performance and Business Outcomes

Define success in operational terms before launch: autonomous completion rate, handle time, rework cycles, first-pass completeness, SLA compliance. Then tie those to financial outcomes. Notch deployments have delivered 200% ROI within 12 months, with payback typically between months four and eight depending on volume in scope.

Where Notch Fits In

Notch deploys AI agents that execute operational workflows on top of the core systems insurers already run. Your PAS, claims platform and billing engine stay in place, and our agents make them more productive by taking on the reading, extracting, routing and updating that currently consumes your teams.

Our insurance agents work across three layers of the operation. Customer-facing agents handle policy servicing, COI issuance, broker inquiries and structured intake for claims and submissions, with system-level execution behind each request. Internal AI coworkers give adjusters and underwriters a way to query long claim packets and policy forms in natural language and get source-cited answers. One broker used this to compare policy versions and verify coverage without leaving its existing tools. Back-office agents handle document ingestion, classification, deadline detection and severity prioritization. With a large US carrier, our agents tag time-sensitive claim packets, extract deadlines from time-demand letters and escalate high-liability cases on the spot, improving SLA compliance while keeping a full audit trail.

Every workflow runs with deterministic validation, configurable guardrails and complete traceability. Our agents execute the workflow, while adjudication and legal decisions stay with your adjusters, underwriters and counsels, maintaining compliance, too. 

The fastest route to modern insurance operations runs through the systems you already own. Book a demo to see which of your workflows could run autonomously within weeks.