Leading Insurance Brokerages Embrace AI Revolution

Performance gaps widen between firms embracing artificial intelligence tools and those lagging behind, agency benchmarking study finds.
By: | August 22, 2025
AI and big data

Insurance brokerages are experiencing a dramatic divide in performance based on their artificial intelligence adoption, according to the 2025 Best Practices Study from Independent Insurance Agents & Brokers of America (Big “I”) and Reagan Consulting.

Since ChatGPT’s release in November 2022, generative AI is rapidly transforming insurance brokerage operations, according to the report.

The data reveals a clear correlation between firm size and AI investment, with 84.2% of brokerages over $100 million in revenue having invested in generative AI, compared to 60% of firms in the $25-100 million range. These larger organizations are leveraging their scale and complexity to integrate AI into core workflows, achieving measurable returns on investment, the report noted.

One Best Practices agency that implemented an AI platform for producers and account teams identified four key areas for efficiency gains: Loss Run Analysis, Coverage Analysis, Statement of Value comparison, and an Ask Anything Agent feature. The implementation resulted in staff saving an average of 7.5 hours per week and the agency achieving a 244% return on investment, the study found.

The performance advantages extend beyond time savings. Producers under 35 using tech-enabled tools not only generated significantly more new business but also maintained book sizes averaging $168,000 larger than their peers without access to AI tools, according to research by Reagan Consulting examining Broker Tech Ventures partner firms.

Implementation Challenges Require Strategic Workforce Preparation

Successful AI adoption requires more than technology implementation—it demands organizational transformation and structured rollout strategies, according to the report. According to a 2025 Gartner Survey, 91% of high-maturity firms have appointed dedicated AI leaders, though these individuals don’t necessarily need deep technical backgrounds. Many successful AI leads combine 75% industry expertise with 25% technical fluency.

“A frequent pitfall in AI programs is mistaking tactical victories for wholesale reinvention,” said Emilia Sherifova, CEO/founder of a stealth AI-native technology company and former CTO at KKR and Northwestern Mutual. “The best roadmaps run on two tracks: velocity (quick wins, fast ROI) and vision (long-cycle reinvention).”

Data quality emerges as a critical success factor. “Agencies that have invested in having a complete and accurate dataset of their customers and policies will get a much better return on their AI investment than those who have not,” states Kabir Syed, CEO of ennabl.

The report said that the AI rollout process requires six key phases: mapping employee workflows, identifying use cases and defining success metrics, establishing pilot teams, implementing production releases with comprehensive training, conducting post-release monitoring, and executing phased implementation across departments.

Organizations must also address workforce preparation, as AI readiness becomes particularly critical for attracting and retaining younger producers who expect access to modern tools and data-driven workflows, the report said.

Obtain the 2025 best practices study here. &

The R&I Editorial Team can be reached at [email protected].

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