AI-Powered Personalization Revolutionizes Insurance RFP Process

Custom AI assistants deliver stronger broker relationships and continuous improvement through feedback loops, Capgemini found.
By: | June 9, 2025
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Insurance companies are leveraging custom generative AI assistants to create hyper-personalized RFP responses, replacing generic templates with tailored proposals that significantly improve win rates and broker engagement.

This shift represents a fundamental change in how insurers approach client acquisition, moving from one-size-fits-all responses to context-rich, intelligent automation powered by enterprise-grade AI platforms, according to a commentary by Pinaki Bhagat, AI & Generative AI Solution Leader, Financial Services, for Capgemini.

The insurance industry faces a stark reality: Generic proposals are no longer sufficient to win business.

“Insurance RFP responses are starting to feel like they’ve been photocopied over and over,” notes Bhagat. Brokers and clients now expect proposals that speak directly to their unique needs rather than templated responses that could apply to any organization.

This shift reflects a deeper understanding of relationship-building in insurance.

“In insurance, trust is built on understanding, and understanding is signaled through specificity,” Bhagat explains. The consequences of failing to adapt are significant – many proposals don’t survive the initial review because they lack relevance to the specific client’s situation, he says.

Generic responses signal a lack of investment in the relationship, causing insurers to lose high-value deals while wasting resources on proposals that fail to convert.

“The days when you could get away with templated, one-size-fits-all responses are behind us,” according to Bhagat.

AI-Powered Solutions at Scale

Enter agentic hyper-personalization – a sophisticated approach that combines custom generative AI with enterprise data to create truly tailored responses. Unlike off-the-shelf chatbots, these private GenAI assistants are trained on historical RFP data, client interactions, industry nuances, and internal product literature, Bhagat explains. They understand both how companies communicate and what clients prioritize.

The technology goes far beyond simple auto-fill capabilities. Through Agentic AI, a modular framework powered by specialized AI agents, these assistants can read RFPs, summarize client requests, construct winning themes, and proactively draft personalized responses and intelligent improvement suggestions. The system utilizes both structured and unstructured data to pull relevant insights and shape them into resonant messaging.

Implementation and Results

A compelling case study cited by Bhagat demonstrates the technology’s practical impact. A global insurer initially approached Capgemini with a modest request: “Can we hyper-personalize our RFP cover letters better?” They were simply looking for bullet points to make responses feel less robotic.

The results were immediate and measurable.

“It wasn’t just a productivity gain, it was a reputation builder,” Bhagat says. “Brokers began to take notice. The insurer wasn’t just responding faster; they were responding smarter.”

Within five weeks, the team deployed a custom GenAI assistant that delivered not just personalized bullet points, but complete executive summaries and tailored cover letters. These weren’t piecemeal templates but coherent, compelling documents calibrated to specific opportunities.

“As expectations around relevance, precision, and value continue to rise, the future of insurance RFPs will belong to those who invest in intelligent automation and meaningful personalization,” Bhagat concludes.

Read the full commentary here. &

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

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