In the race to adopt AI, many insurers might just assume that wherever the technology can be integrated, it will automatically provide the most value, but simply adding a model to their ecosystem is not the way to maximise the potential of AI.

AI models are only as good as the foundation they are built upon, and insurers need to remember that when they look to adopt the technology across their ecosystem. Put simply, Simon Fagg, SVP and Go To Market Leader at IntellectAI, noted that “Pilots fail the move to production on the record, not the model.”

This sentiment was shared by Allison Lagosh, V.P. and head of compliance at Saifr. She said, “Insurers need more than models.” To get the most out of the technology, an insurer needs data, governance frameworks, regulatory requirements, human oversight and workflow integration. This can help AI models reach their full potential, while also having guardrails in place to prevent mistakes.

When it comes to implementing the technology, Dan Simmons, managing director and founder at Quensus , sees three core aspects needed for insurers to get real commercial value from AI. The first is to consolidate IoT telemetry, claims history, and policy records from siloed departments into a unified repository. Second, they need to build a robust deployment pipeline so models can be continually monitored for drift and updated without the need to start from scratch. Finally, they should bring actuaries, underwriters and data scientists together to ensure AI targets real challenges.

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