AI in insurance isn’t stuck because of use cases, it’s stuck because of infrastructure. Production systems require three things: a unified data foundation, architecture built for scale and failure, and workflows people will actually use. Most effort should go into data and engineering early, not models. The teams that get this right aren’t piloting AI. They’re operationalizing it.

Many insurers are stuck in AI pilot purgatory. The models work. The business cases make sense. Yet AI rarely scales into daily operations. The real blocker isn’t algorithms or ambition. It’s data architecture built for reporting, not production AI. Modern insurance AI requires real-time and historical data, structured and unstructured content, and evolving context across workflows. Without the right data foundation, AI stays stuck in pilot mode.