Claims leaders are under constant pressure to “do something with AI.” Boards want road maps. Competitors announce pilots. Adjusters are overloaded and looking for relief wherever they can find it.
Executive Summary
"The real problem is not choosing build or buy. The problem is treating every AI use case as if it belongs in the same category."Chad Langford, Head of Data Science for CLARA Analytics, shares the insight for insurance company leaders who feel pressured to build AI tools for claims handling. Here, he spotlights the variables that really matter as he analyzes different categories of claims problems based on their level of complexity, need for external information and the degree to which problem resolutions are specifically tied to how the carrier operates.
In that environment, the build-or-buy conversation often starts in the wrong place. It quickly becomes a debate about speed versus control, innovation versus risk, or IT versus operations. Those debates feel strategic, but they usually miss the core issue.
The real problem is not choosing build or buy. The problem is treating every AI use case as if it belongs in the same category.
Not all claims AI problems are created equal. Until organizations start sorting them correctly, they will continue to overbuild where they should buy, overpay where they should build, and stall progress where neither is the right answer. I see this play out the same way often. A new AI idea surfaces, enthusiasm spikes, and the first conversation is about platforms and vendors. By the time anyone asks what problem they’re actually solving, the budget is already spoken for.
