Artificial intelligence is rapidly reshaping how insurance companies process claims, detect fraud, and manage risk. But to be effective and fair, AI must be developed and deployed with careful attention to data quality, model transparency, and ethical use. AI systems are only as good as the data they are trained on, and if that data is biased or incomplete, the outcomes will reflect and even amplify those problems.

In a conversation filled with lived experience, John Standish, Co-Founder and Chief Innovation and Compliance Officer at Charlee AI, laid out a powerful and pragmatic vision for how artificial intelligence must be built for the insurance industry. Having transitioned from a long and substantial career in law enforcement and insurance fraud investigations to the world of InsurTech, John offers rare dual expertise: a regulator’s scrutiny and a technologist’s curiosity. His perspectives cut through hype and buzzwords and land squarely in the domain of real-world consequences, compliance, and human-centered innovation.

John underscored the importance of domain-specific AI models that are trained with relevant, clean, and unbiased data. He cautioned against using generic models and stressed the need for explainability, transparency, and regulatory compliance in all AI-driven decisions. The conversation illuminated a crucial point: AI isn’t a magic fix for outdated processes—it’s a force multiplier for organizations willing to rethink their foundational data strategies and workflows. For the insurance industry, embracing this challenge is not just a matter of innovation, but of survival in a rapidly changing digital landscape.