By Kurt Diederich, President & CEO

AI in insurance core systems is becoming a critical focus for P&C executives. Adoption is accelerating, but that has not made decision-making any easier. The challenge is no longer whether AI matters; it’s separating real operational value from vendor noise and unrealistic expectations.

For P&C executives, the priority is finding where AI measurably improves work, especially in speeding up tasks, reducing manual effort, and enhancing workflow efficiency.

At this point, the conversation shifts from theory to strategy. In insurance, tangible value from AI typically emerges first in daily operations when employees review documents, route work, resolve exceptions, and keep decisions moving. Once leaders pinpoint these opportunities, a critical operational question follows: Can our current core system actually support and enable these improvements?

Whether an insurer is working with an existing core system or planning a new one, the key is to think strategically about how AI will be integrated into day-to-day operations. In this blog post, you’ll find a practical look at how AI can drive real workflow improvements in insurance core systems, and what executives should focus on first.

AI value in insurance is operational. 

While there’s no shortage of broad claims about what AI can do for insurance (faster decisions, better service, smarter operations, lower costs), the reality for most carriers is more practical. Value rarely arrives all at once; it shows up in smaller, incremental ways inside daily workflows.

AI can help summarize submissions, classify documents, route work, surface missing information, recommend next steps, and support employees with repetitive operational tasks. It’s important to note that there are different types of AI. Some are better suited for tasks like language processing, others for pattern recognition or workflow automation, and the impact you see depends on matching the right type of AI to your specific goals. While these improvements may not sound dramatic, they drive the real gains: less rework, fewer handoffs, better prioritization, and more consistent execution. The true value of AI in insurance core systems lies in making existing workflows more efficient, helping teams save time, reduce errors, and work smarter.

That kind of workflow efficiency matters because it directly impacts cost, cycle time, staff capacity, customer experience, and speed to market. It also determines whether a carrier can grow without operations becoming increasingly complex.

This is why, once the first use case is identified, the insurance core system should be among the first things executives evaluate when considering AI adoption. It’s tempting to focus on the latest tools and features, but the first question should be whether the current core system can support AI in a way that improves real work. The core system is the hub for transactions and processes. Submissions, underwriting, policy changes, billing, claims, business rules, audit trails, and approvals all live here.

If AI is disconnected from this environment, it becomes just another tool to manage, rather than a way to streamline work. To truly realize AI’s value, executives should evaluate whether the core system can enable AI to make a direct impact on day-to-day operations by asking:

• Can the core system expose the right data at the right point in the workflow?
• Can it trigger actions, route tasks, and support approvals without forcing workarounds?
• Can it connect structured and unstructured information in a usable way?
• Can it support governance, auditability, and human oversight?
• Can it scale across lines of business without creating additional fragmentation?

If the answer to most of these is yes, AI can add value quickly. If not, leaders may still pursue AI, but with a clear understanding of its limits, and, in many cases, the more strategic move is to ensure the next core system lays a stronger foundation for AI-enabled workflows.

AI should be explored pragmatically through targeted use cases, but scaled only where the core system, data, and governance model can support it.

Where AI improves workflow efficiency in insurance operations

The most practical AI use cases tend to appear in the parts of the business where employees spend time reviewing information, moving work, and handling repeatable decisions.

Underwriting intake and triage

Submission intake is a good example. Underwriters and support teams often spend too much time gathering information, reviewing documents, and deciding what should happen next.

AI can help summarize submission packets, extract key fields, identify missing information, and route work based on appetite, complexity, or completeness. That does not replace underwriting judgment. It helps protect it by reducing administrative drag and making it easier for teams to focus on the business that deserves attention.

Policy servicing and endorsements

Routine servicing work can create a surprising amount of friction. Simple requests still require employees to review context, confirm status, update records, and communicate next steps.

AI can support the classification of incoming requests, surface relevant policy context, and assist with routine correspondence or workflow recommendations. The result is not just faster handling. It is also more consistent execution across the service operation.

Billing support and exception management

Billing tends to accumulate small inefficiencies that repeat at scale. Exceptions, questions, follow-ups, and account-level adjustments can absorb significant staff time.

AI can help surface recurring exception types, support routing, and accelerate common service tasks. When paired with a strong workflow model, it can shorten resolution times and reduce manual follow-up.

Claims intake and early triage

Claims is another area where workflow speed matters immediately. At first notice of loss, the business needs enough information to classify, assign, and move work quickly.

AI can help summarize intake details, flag missing information, support early categorization, and guide next-step routing. Used well, it helps teams separate routine work from more complex claims earlier in the process.

Document handling across the core operation

Insurance remains document-heavy, and that creates friction almost everywhere. Employees spend time finding, reviewing, classifying, and acting on unstructured information.

AI can support document ingestion, summarization, classification, and routing. That makes it easier to move work forward without requiring staff to spend the same amount of time on repetitive review.

Operational insight for leaders

Executives and department leaders usually know where the business feels slow. The harder question is exactly where the friction starts and how often it repeats.

AI can help surface exception patterns, recurring delays, and workflow bottlenecks. That does not replace operational discipline, but it can make it easier to see where process improvement efforts will have the greatest impact.

Across all these examples, the consistent theme is clear: AI’s value lies in cleaner workflow execution, not just the technology itself.

What AI does not fix on its own

It’s at this stage that many conversations become less honest than they should be.

AI does not repair a broken process just because it is layered on top of it. It does not solve fragmented architecture by itself. It does not create trustworthy data where none exists. And it does not remove the need for human judgment.

In fact, when the underlying environment is weak, AI can make problems move faster rather than improve operations.

If employees already rely on disconnected systems, manual workarounds, inconsistent data, and unclear ownership, adding AI may increase complexity before it reduces it.

That is why executives should be careful not to treat AI as a shortcut around foundational issues. A workflow with too many handoffs still has too many handoffs. A system landscape with poor integration is still hard to manage. A team with inconsistent data still cannot trust the output as much as it needs to.

The better view is this: AI can strengthen a sound operating model, but it is not a substitute for one.

What insurance executives should evaluate first?

As insurers assess AI in their current system or as part of a modernization strategy, a few questions matter more than others.

1. Where is the workflow friction today?

Before looking at features, leaders should identify where staff time is actually being lost. Is it in intake? Document review? Exception handling? Claims setup? Policy changes? Service requests?

The most promising AI use cases are usually tied to visible operational friction, not abstract innovation goals.

2. Is the data usable enough to support the use case?

AI depends on context. If the relevant data is hard to access, inconsistently structured, or spread across too many systems, the business case weakens fast.

3. Can the current core system support AI inside the workflow?

This is a critical question. Can the system expose data, trigger actions, support business rules, and maintain oversight within the flow of work? Or will AI sit off to the side, forcing users to swivel between tools?

4. If modernization is coming, what should the next core system enable?

For insurers evaluating a replacement, the core system decision should include more than standard functionality. Leaders should ask whether the next system will create a better environment for automation, analytics, orchestration, document handling, and future AI-enabled workflows.

5. How will governance and accountability work?

Insurance is not an environment where black-box decisions should move unchecked. Executives need clarity around approvals, auditability, escalation paths, and where human review remains essential.

6. How will success be measured?

If the business cannot define success, it will struggle to prove value. Useful measures often include cycle time, touch count, turnaround time, productivity, service levels, and speed to market.

These questions are simple, but they change the conversation. They move AI out of the hype cycle and into an operating model discussion.

The current system question and the modernization question are connected.

For some insurers, the right next step will be to identify a few targeted AI use cases in the current environment and prove value in contained workflows.

For others, the more important move will be to make sure the next core system does not lock them into the same limitations they are already trying to escape.

That is especially true for carriers dealing with legacy platforms, heavy manual work, multiple vendors, duplicated data, or limited flexibility across lines of business. In those environments, AI may still help at the edges, but the long-term opportunity depends on creating a more connected operating foundation.

That is also where the modernization conversation becomes more strategic. The next core system is not just a replacement decision. It shapes how efficiently the organization can operate, how quickly it can introduce change, and how well it can adopt future capabilities without adding another layer of complexity.

For a company like Finys, that is the practical lens that matters most. A modern core system should help insurers reduce operational costs, improve time-to-market, and create a more connected environment across policy, billing, claims, portals, and reporting. From there, AI becomes easier to evaluate based on workflow impact rather than vendor promises.

The real opportunity is not louder AI. It is a better workflow design.

Those who see the greatest returns from AI will be the carriers who use it to drive measurable value within their organizations. They will be the ones who apply AI to the right workflows, with the right data, in an operating environment that supports it.

That starts with asking better questions.

Where is the friction? Which workflows matter most? Can the current system support improvement? If not, what should the next core system make possible?

Those are the questions that move the AI conversation from speculation to strategy.

In insurance, AI becomes real when it helps work move faster, cleaner, and with less friction across the core processes the business depends on every day.

FAQs

What is the real impact of AI on insurance core systems?

The biggest impact is usually operational. AI can reduce manual work, speed up intake and triage, improve document-heavy processes, and support better workflow decisions when applied within core insurance operations.

Where does AI improve workflow efficiency for P&C insurers?

Common areas include underwriting intake, policy servicing, billing support, claims intake and triage, document handling, and operational reporting.

Should insurers evaluate the core system before investing in AI?

Executives should evaluate it early. Once the use case is clear, the next question is whether the current or future core system can support the data access, workflow execution, governance, and integration required by the AI capability.

Does AI replace core system modernization?

No. AI can strengthen workflows, but it does not replace the need for modern core systems, usable data, and well-designed processes.

What should executives look for in their next core system if AI is a priority?

They should look for workflow flexibility, accessible data, integration readiness, strong governance, and the ability to support automation and decision support across underwriting, policy, billing, and claims.