Core System Ownership: The Fast Track to AI in Insurance

Most carriers say they “want AI.” What they often mean is they want faster underwriting decisions, fewer manual touches, cleaner submissions, and claims that don’t stall in inbox limbo.

Here’s the uncomfortable part: AI in insurance fails more often because of core system friction than model quality. If your policy admin system can’t easily accept a third-party risk signal, can’t explain how a decision was made, or needs a vendor release cycle to change a workflow, your AI initiative turns into an expensive demo.

That’s why the business model behind your core platform matters more than most teams admit. A recent example: Combined Ratio Solutions (CRS), a Hartford-based company, is pushing an alternative approach—a policy administration foundation with no software licensing fees, monetized through implementation and managed services. Whether you choose them or not, the structure is worth studying because it aligns directly with what AI programs need: control, speed, and predictable economics.

Why AI programs get stuck at the core system layer

AI depends on operational throughput. Models don’t create value until they’re embedded into quoting, underwriting, endorsements, renewals, claims intake, and billing flows.

A typical carrier AI roadmap runs into the same blockers:

• Integration bottlenecks: You can buy hazard data, fraud scores, OCR, and agent-assist tools—then spend months wiring them into the policy system.
• Release-cycle dependency: You need a small workflow change, but it’s tied to a vendor upgrade window.
• Data fragmentation: Underwriting data lives in one place, policy data in another, documents somewhere else, and nobody trusts the “system of record.”
• Unclear accountability: A vendor sells software, a systems integrator implements it, and a hosting provider runs it. When something breaks, you get a meeting—rarely a fix.

This matters because AI initiatives are iterative by nature. You don’t “implement AI” once. You test, learn, retrain, tune thresholds, adjust decisioning rules, and change how humans override the system. If every iteration is a contract negotiation or a platform constraint, you’ll slow to a crawl.

The CRS model: policy administration without “software rent”

CRS’s differentiator isn’t a shiny AI feature list. It’s ownership and economics. Their platform, OS Policy, is positioned as an open-source policy administration system with no licensing fees, governed as a community project. In this structure, carriers own the deployed code base and control their roadmap.

What “no license fee” changes in practice

When you remove software rent, three practical things happen:

1. Budget shifts from recurring fees to capability building. Instead of paying for the right to use software, you can fund underwriting automation, data engineering, and better decision support.
2. Roadmap control becomes real. You’re not waiting for a vendor’s priorities, product sunsets, or mandated migrations.
3. Negotiating power flips. If a services provider isn’t holding licensing hostage, they have to keep earning your trust through outcomes.

That last point is the quiet one. Many carriers tolerate mediocre delivery because switching costs are brutal. Ownership changes the “hostage dynamic,” and it’s healthy.

Services-first isn’t a compromise—if accountability is designed in

Some teams hear “services-driven” and assume “consulting-heavy” or “custom spaghetti.” That can happen in the wrong setup.

CRS argues the opposite: services revenue forces discipline because the provider must remain aligned to customer outcomes year after year. The founders’ backstory reinforces the culture: after a venture-backed software plan collapsed shortly before closing, they built a services-driven business out of necessity. That survival-mode origin story shaped an operating stance: keep responsibility tight, avoid projects that are doomed, and don’t hide behind multi-party delivery.

A stance I agree with: if a core replacement doesn’t come with clear accountability, you’re basically buying a committee.

Why this resonates with specialty carriers and MGAs building AI workflows

CRS focuses on tier two and tier three insurers, MGAs, and specialty organizations. That segment has a particular set of pressures:

• Legacy cores that can’t support modern underwriting tools
• Low tolerance for multi-year, high-eight-figure transformation programs
• Growing demand for AI-driven decision support (triage, risk selection, pricing guidance)
• Pressure to launch, refine, and sometimes retire product lines quickly

Specialty underwriting is increasingly “AI-assisted” rather than “AI-replaced.” The winning pattern I’ve seen is a blend:

• AI extracts and normalizes submission data
• Rules and models triage the risk (decline, refer, fast-track)
• Underwriters make the final call with better context and speed

That workflow requires a policy administration system that can:

• Accept structured data at intake
• Store third-party risk signals alongside policy data
• Trigger workflows based on scores/thresholds
• Log decisions for audit and compliance
• Change quickly as the model evolves

Ownership and flexibility aren’t ideological here—they’re operational.

AI needs a stable core: “capability layer” thinking that actually works

CRS positions AI as a capability layer rather than a label on the core system. That’s a stance more carriers should adopt.

AI doesn’t replace the core. It feeds the core.

If the foundation is unstable, adding AI creates more failure modes:

• Automated decisions routed into the wrong workflow
• Missing data fields that force manual re-keying
• Inconsistent policy objects that break downstream reporting
• Poor audit trails that raise governance risk

A practical architecture pattern for AI in insurance

If you’re designing for AI-enabled underwriting and operations, a durable pattern looks like this:

1. Core system as system of record (policy, billing, claims objects)
2. Decisioning layer (rules + model scoring services + thresholds)
3. Data/feature layer (curated underwriting data sets, feature store, enrichment pipelines)
4. Experience layer (underwriter workbench, agent portal, API partners)

The core shouldn’t try to “be AI.” It should expose clean APIs, support configurable workflows, and store decisions and evidence.

An ownership-first model can make this easier because you’re not waiting for a vendor to bless a new integration path or data object.

“One throat to choke” and why it matters for AI-enabled operations

CRS intentionally keeps responsibility across software, implementation, and managed operations—rejecting RFP-driven, multi-vendor delivery models.

I’m opinionated on this: AI projects multiply handoffs. Data vendors, model platforms, MLOps, the core, portals, document systems, and analytics all intersect. When the core replacement itself is split across vendor/SI/host, the carrier becomes the de facto systems integrator—whether or not it has the staff for it.

When one party owns delivery end-to-end, you get clearer outcomes:

• Faster root-cause analysis
• Fewer “that’s not in our scope” stalemates
• A single plan for uptime, performance, and change management

It’s not that multi-vendor ecosystems can’t work. They can. But they require strong internal architecture leadership and ruthless governance. Many mid-market carriers don’t have spare capacity for that—especially while also trying to operationalize AI.

How to evaluate an “open core” path without betting the company

Core replacement is still a big move. The smarter path is to treat it like a staged modernization that supports your AI roadmap.

Step 1: Start with a use case that forces integration reality

Pick a use case that touches both data and workflow. Examples:

• AI-assisted submission intake for a single specialty program
• Underwriting triage (decline/fast-track/refer) with clear thresholds
• Claims FNOL automation that classifies severity and routes work

If the core platform can’t support the first use case cleanly, scaling AI later will be harder—not easier.

Step 2: Demand “change velocity” metrics, not feature demos

Ask vendors and partners to commit to measurable delivery expectations:

• Time to add a new data element end-to-end (UI → API → storage → reporting)
• Time to integrate a new third-party risk signal
• Time to adjust workflow routing logic
• Release frequency you can sustain without business disruption

AI success is often a race between learning and bureaucracy. Measure the bureaucracy.

Step 3: Treat governance as a product requirement

AI creates audit needs. Your core strategy should support:

• Versioned decision rules and model scores stored with policy/claim events
• Reason codes and human override tracking
• Data lineage (what signal was used, when, and from where)

If your core can’t store the “why” behind a decision, you’ll hit regulatory and operational limits fast.

Step 4: Build internal capability even if you outsource delivery

CRS explicitly supports clients that want internal engineering capability. That’s the right direction.

A practical target for mid-market carriers is a small, high-impact team:

• 1–2 integration engineers (APIs, eventing, interfaces)
• 1 data engineer (pipelines, quality checks)
• 1 product owner for underwriting operations
• Shared security/compliance support

You don’t need a massive org chart. You need ownership and continuity.

The real takeaway for AI in Insurance: control beats hype

The core system conversation usually gets framed as a vendor comparison. It should be framed as an operating model decision—especially if AI in insurance is on your 2026 roadmap.

A licensing-heavy platform can still be the right choice for some carriers, particularly those pursuing global consolidation. But for many specialty carriers, MGAs, and mid-sized insurers, a core system strategy built on ownership, flexibility, and accountable services is a direct enabler of AI-driven underwriting and operational efficiency.

If you’re planning AI-assisted underwriting, claims automation, or workflow intelligence, don’t start by asking which model to buy. Start by asking:

Will our core let us change how work gets done every month without begging for permission?

If the honest answer is “no,” your AI roadmap is already behind schedule.