Blogs

AI in insurance is no longer a concept we debate at conferences; It’s here, it’s live, it’s embedded in workflows, and it’s influencing decisions every day. Yet adoption still feels cautious, and the hesitation isn’t about speed or scale - it’s about trust. If an AI solution produces the wrong outcome, who is accountable? If a regulator asks for justification, can the decision be explained? These questions echo through the boardrooms of major insurance firms and plague the co

For years, insurance operations have relied on processes that are complex and fragmented. New AI solutions promise to speed up these processes and scale efficiency, but they also introduce a new challenge: their outputs often behave like a black box. Brokers, underwriters, and claims managers receive answers and recommendations, but they cannot see how these were produced. Without clarity, trust erodes, adoption stalls, and the efficiency gains promised by automation fail to

In most industries, if your AI tool makes something up, it's annoying. In insurance, it’s dangerous. Large Language Models (LLMs) are powerful, but they have a flaw: they sometimes hallucinate, confidently providing answers that sound plausible but are factually wrong or contextually off. In workflows like Underwriting Triage or Quote Comparison, this can mean misread endorsements, missed exclusions, or mismatched limits. These aren’t just technical errors. They are the kind