In a recent feature for CIAB’s Leader’s Edge, Dan Epstein, 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.
As agencies and carriers experiment with generative AI and large 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 scalable advantage — not just a trend.
The Current State of AI in Insurance
According to ReSource Pro’s recent AI adoption survey of 150 insurance leaders conducted at its annual Summit, enthusiasm for AI is high — but maturity remains low.
- 60% of respondents said AI is still in its earliest stage of adoption.
- Only 1% believe it’s mature enough for consistent, enterprise-wide use.
“Agencies see AI as real and promising,” Epstein explains. “But it’s still early in its evolution.” The research points to a mix of optimism and caution as organizations look to move from experimentation to scalable, repeatable outcomes.
Laying the Groundwork for Successful AI Adoption
One of the biggest barriers to meaningful AI use is operational fragmentation. Many agencies process similar tasks in inconsistent ways, creating inefficiencies that make automation unreliable.
“The opportunity lies in standardization and centralization,” Epstein says. “Agencies need to identify where customization adds value — and where it creates unnecessary complexity.”
By aligning processes and improving data integrity, insurance organizations create the foundation for trustworthy AI performance and easier integration across systems.
Finding Value Beyond the Technology
Watkins emphasizes that AI’s true power lies not in the models themselves, but in how they’re applied.
“The science behind large language models is important,” he explains, “but the real differentiator is combining insurance domain expertise with engineering discipline.”
ReSource Pro’s approach unites both technical and industry specialists to develop AI use cases that deliver accurate, business-relevant outcomes. Watkins cautions that innovation must be grounded in a deep understanding of insurance workflows — ensuring that every use case is practical, explainable, and sustainable.
Balancing People and Technology
ReSource Pro’s survey found that success with AI depends as much on people as on technology:
- 92% of respondents said at least half of their AI success depends on people and processes, not tools alone.
Epstein and Watkins agree that human oversight is critical for accuracy, compliance, and accountability. By combining human expertise with engineering precision, ReSource Pro has improved data extraction accuracy from 80% to 99%, while maintaining transparency around potential errors.
Looking Ahead: AI as an Embedded Capability
Epstein predicts that AI will soon be woven into every stage of the insurance value chain — from underwriting to claims and customer experience.
“The key is to combine insurance knowledge with disciplined workflow design so that AI becomes a reliable, scalable part of daily operations,” he says.
To explore Dan Epstein and Chris Watkins’ full perspectives on the future of AI in insurance, read the full Q&A on Leader’s Edge Magazine: AI Solutions Beyond the Hype | Leader’s Edge Magazine
