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In a recent feature for CIAB\u2019s Leader\u2019s Edge, Dan Epstein Tagger, CEO of ReSource Pro, and Chris Watkins, Chief Information Officer, discuss how insurance leaders can move past the hype around artificial intelligence (AI) to achieve meaningful, measurable outcomes.<\/p>\n
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As agencies and carriers experiment with\u00a0generative AI\u00a0and\u00a0large language models (LLMs), many struggle to convert early curiosity into consistent business impact. Epstein and Watkins outline how insurers can build the operational, data, and cultural foundation needed to make AI a\u00a0scalable advantage\u00a0\u2014 not just a trend.<\/p>\n
According to\u00a0ReSource\u00a0Pro\u2019s recent\u00a0AI adoption survey\u00a0of 150 insurance leaders conducted at its annual Summit, enthusiasm for AI is high \u2014 but maturity\u00a0remains\u00a0low.<\/p>\n
\u201cAgencies see AI as real and promising,\u201d Epstein explains. \u201cBut it\u2019s still early in its evolution.\u201d The research points to a mix of optimism and caution as organizations look to move from experimentation to\u00a0scalable, repeatable outcomes.<\/p>\n
One of the biggest barriers to meaningful AI use is\u00a0operational fragmentation. Many agencies process similar tasks in inconsistent ways, creating inefficiencies that make automation unreliable.<\/p>\n
\u201cThe opportunity lies in\u00a0standardization and centralization,\u201d Epstein says. \u201cAgencies need to identify where customization adds value \u2014 and where it creates unnecessary complexity.\u201d<\/p>\n
By aligning processes and improving\u00a0data integrity, insurance organizations create the foundation for\u00a0trustworthy AI performance\u00a0and easier integration across systems.<\/p>\n
Watkins emphasizes that AI\u2019s true power lies not in the models themselves, but in how\u00a0they\u2019re\u00a0applied.<\/p>\n
\u201cThe science behind large language models is important,\u201d he explains, \u201cbut the real differentiator is combining\u00a0insurance domain expertise\u00a0with\u00a0engineering discipline.\u201d<\/p>\n
ReSource\u00a0Pro\u2019s approach unites both technical and industry specialists to develop AI use cases that deliver\u00a0accurate,\u00a0business-relevant outcomes. Watkins cautions that innovation must be grounded in a deep understanding of insurance workflows \u2014 ensuring that every use case is\u00a0practical, explainable, and sustainable.<\/p>\n
ReSource\u00a0Pro\u2019s survey found that success with AI depends as much on people as on technology:<\/p>\n
Epstein and Watkins agree that\u00a0human oversight\u00a0is critical for accuracy, compliance, and accountability. By combining human\u00a0expertise\u00a0with engineering precision,\u00a0ReSource\u00a0Pro has improved\u00a0data extraction accuracy\u00a0from 80% to\u00a099%, while\u00a0maintaining\u00a0transparency around potential errors.<\/p>\n
Epstein predicts that AI will soon be woven into every stage of the\u00a0insurance value chain\u00a0\u2014 from underwriting to claims and customer experience.<\/p>\n
\u201cThe key is to combine\u00a0insurance knowledge\u00a0with\u00a0disciplined workflow design\u00a0so that AI becomes a\u00a0reliable, scalable part of daily operations,\u201d he says.<\/p>\n
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