In the second of this three-part blog series, we explore Myth #2 – “AI is plug-and-play” from our webinar AI in Insurance Claims: Myths, Realities and the ExCo Gap.
Artificial intelligence has become one of the most widely discussed topics in insurance innovation. Insurers and MGAs everywhere are experimenting with AI, to improve claims efficiency, reduce leakage, and enhance customer experience.
But despite the enthusiasm, a stark reality has emerged.
Although more than 70% of insurers are running AI initiatives, only 7% have successfully scaled these pilots through their organizations.
Why does the gap between experimentation and impactful deployment remain so large?
One reason is the belief that AI systems are essentially plug-and-play. This is a myth.
In demonstrations or early proofs of concept, AI often appears incredibly powerful. Upload a handful of documents and the system extracts the data flawlessly. Ask a question and the answer comes back instantly.
But as Sprout.ai Head of AI Bernie Camus explained in our webinar, these early successes can create unrealistic expectations about what it takes to deploy AI in real-world insurance environments.
Living real-life on the edge
“AI can look incredible in a proof of concept,” he said. “But real claims data brings edge cases that make production much harder.”
Operationalising AI in a live claims department means facing the “messy” reality of unstructured data. Claims documentation is rarely neat or consistent. Handwritten notes, damaged receipts, missing information and unexpected formatting are all common. Each of these variations creates challenges for automated systems.
The difference between a promising prototype and a reliable operational system often comes down to how well these edge cases are handled.
Camus noted that standard AI models may initially achieve around 80% accuracy in many document-processing tasks. The remaining improvements require careful work addressing the messy, unpredictable scenarios that appear in real claims data.
“That final five to ten percent is often made up entirely of edge cases,” he explained.
Technology challenges are only part of the story, however.
Setting goals that focus on technology not outcomes
According to Roi Amir, many AI projects struggle because organizations start with the wrong objective. Instead of focusing on measurable operational outcomes, they focus on the technology itself.
“The goal needs to be business impact,” Amir said. “AI is just the tool to implement it.”
Without a clear operational objective — such as improving coverage accuracy or reducing claim cycle times — projects risk becoming experiments rather than transformation initiatives.
Integration is another critical factor. AI systems must operate within the workflows where decisions actually happen, which means integrating with claims management and policy administration platforms.
Finally, successful projects tend to focus on use cases where the potential impact is large enough to justify investment. AI applied to high-volume processes can deliver meaningful improvements in speed, accuracy and cost.
For insurers looking to move beyond pilots, the lesson is clear. AI transformation is not simply a technology deployment. It requires clear business objectives, robust architecture and a willingness to address the operational realities of claims data.
Those that succeed will find that operationalising AI is less about the ‘plug-and-play’ dream and more about building a robust, integrated framework that delivers consistent value at scale. AI has the potential to reshape claims operations in ways that improve customer outcomes, reduce leakage, and strengthen competitive advantage.