Insurers Weigh AI Workflow Builds Against Buying Existing Tools

AI has made it easier for insurers to prototype custom workflows, shifting the buy-versus-build calculation. But a convincing demo says little about the work required to connect a new tool to core systems and make it part of daily operations. For carriers considering AI, the practical choice is usually made workflow by workflow.

The author, whose experience includes building Clearcover and Dearborn Labs, says his organizations use a mix of purchased and custom technology. They build more than they buy, and that balance has been moving toward building as development costs fall. Still, no carrier needs to treat the choice as an all-or-nothing decision.

Choose by workflow

Buying makes sense when a need is broadly similar across carriers and an established product already handles it well. In those cases, building a duplicate can consume time and money that might be better spent on work where the insurer’s own approach matters.

Custom development is more compelling when a workflow is a source of meaningful difference. The article points to claim triage and decisions about which risks to write as examples. A purchased tool may impose a standard process shared with other customers; that can be a poor fit when a carrier wants to handle the work in a distinctive way.

There is no universal boundary between the two approaches. A carrier’s lines of business and operating needs affect where it falls, and the right answer may differ across workflows within the same company. A single organization-wide rule can therefore miss important differences between use cases.

The integration work behind the demo

AI can change how a claim is handled or which decisions happen without a person opening a file. Yet the systems underneath those processes often remain in place. A claims intake tool still needs to send information to the core system and set reserves in a way that fits actuarial expectations. An underwriting workbench needs to draw from policy administration and return its decisions there.

Those connections are rarely visible in a demonstration. The interface may be relatively straightforward to produce; fitting the application or AI agent into the insurer’s existing software is often the harder engineering task. A redesigned workflow has to operate alongside the systems that support the rest of the business, and those systems may not change quickly.

That distinction matters when comparing a custom build with an integrated product. For a well-solved, common problem, a purchased workflow may arrive with useful connections already in place. But a vendor product is designed to serve a range of carriers. If the goal is to work differently from the market, that standardization may limit the value of buying.

Go-live is the start of adoption

Integration is only part of the implementation. Teams also need to use the new workflow in production until it becomes a regular part of how work gets done. Projects often define completion around go-live, even though the adoption effort continues afterward.

The author cites Clearcover’s claims team, where adoption of AI tools is above 92%. That figure, he says, reflects use in real claims after launch and the time it takes for staff to settle into a new way of working. It illustrates why a successful demonstration or deployment alone cannot establish whether a workflow has delivered operational change.

For insurers assessing a project, the work plan should account for both the technical connections and the period after launch. The central question is not just whether a tool can perform a task, but whether it can do so within current systems and whether employees will use it consistently in live work.

Three checks before building

The author suggests asking three questions before developing a new workflow:

1. Foundation: Was the current process designed around limits that no longer apply?
2. Fit: Can the new workflow work with existing systems without replacing them?
3. Engineering: Does the insurer’s engineering team own the integration layer and have the ability to test, govern and audit what runs there?

The first check is about avoiding automation that preserves an outdated process. Some insurance procedures were shaped by systems that could not communicate or by practical limits on reviewing files. If those constraints have changed, automating the old steps may simply make an inefficient process run faster. The insurer could spend real resources keeping a limitation that no longer needs to define the work.

The second check recognizes that workflow changes do not always require replacing core platforms. Existing systems can remain in place if engineers can connect the new process to them reliably. That makes integration capability a key part of deciding whether a build is feasible.

The third check deserves attention before a project reaches a polished demonstration or a leadership review. If engineers do not own the integration layer, or cannot test and oversee it, the demo may conceal a gap that appears only during implementation.

AI’s lower development barriers give carriers more reason to consider building, especially where their workflows need to differ. The decision still depends on the task, the systems it must connect to and the people who will use it. The demo is the visible start; integration and sustained adoption determine whether the workflow becomes part of the business.