The insurance landscape has long relied on the expertise and reach of field agents to drive customer acquisition, foster relationships, and build trust. In many regions, particularly across Asia, Africa, and Latin America, field agents have served as the lifeblood of insurance distribution for decades. However, this traditional model is undergoing a rapid, foundational transformation.
Generative AI (Gen AI) and agentic AI are actively reshaping the role of the insurance agent—not as a future possibility but as a present-day reality. These technologies have not replaced the field agent; they have redefined the agent’s role by transforming them from policy sellers to AI-augmented advisors with real-time insights, automation tools, and personalized engagement capabilities.
A Historical Perspective: The Evolving Role of Insurance Advisors
For much of the industry’s history, field agents have played a central role in the insurance value chain. From identifying prospects to collecting documents and assisting in claim settlements, agents have served as the first and often only human interface between insurers and policyholders.
The early 2000s introduced CRM platforms and email marketing, but the core responsibilities remained unchanged. A shift began in the 2010s with the rise of mobile apps and self-service platforms, which offered more convenience but did not fundamentally disrupt the agent’s centrality. The 2020s, however, have ushered in a different story. AI-powered tools have moved from the periphery to the core of distribution models. Customers today expect instant, personalized, 24/7 service—a shift that human agents alone cannot deliver at scale. This has laid the foundation for intelligent systems that support and often outperform humans in specific tasks, fundamentally altering how insurers engage with their customers.
From Generation to Action: The Emergence of Gen AI and Agentic AI
Gen AI, powered by large language models, has demonstrated remarkable capabilities in generating human-like text, summarizing complex documents, and delivering contextual responses in real time. However, the insurance sector is now evolving further with agentic AI.
Unlike Gen AI, which focuses on generating content and conversation, agentic AI systems are designed to autonomously plan and execute tasks based on defined goals. They do not just respond to inputs; they initiate actions, make decisions, and deliver outcomes without continuous human oversight. In practice, agentic AI in insurance today can manage tasks such as fetching policy quotes, booking medical appointments for underwriting, processing claim intimation, updating nominee information, or following up with customers based on their life events. And all this is done in real time and without human intervention. This ability to combine contextual understanding with autonomous execution has elevated AI from a passive tool to a proactive collaborator in the insurance distribution value chain.
According to our Applied AI Services 2024–2025 Market Insights™ report, AI implementations surged 25% in CY 2024, driven by enterprise interest in multimodal and agentic AI. However, while 68% of Gen AI projects are now in production, only 30% of agentic AI initiatives have progressed beyond the pilot or proof-of-concept stage. This reflects agentic AI’s nascent but rapidly evolving nature, with insurers cautiously exploring its full potential. Furthermore, 52% of enterprises are using Gen AI to optimize internal workflows and enhance productivity, indicating a strong shift toward AI-enabled operational efficiency, especially in high-volume functions like insurance distribution. We expect a significant increase in agentic AI adoption as businesses move from experimentation to enterprise-scale deployments, particularly in areas where autonomous decision-making can drive speed, accuracy, and cost-efficiency.
Where AI-Powered Insurance Advisors Are Already Driving Impact
In customer acquisition, AI-enabled advisors can score leads based on behavioral and demographic data, craft hyperpersonalized messages across digital channels, and pre-fill applications by integrating with document verification APIs. Even in areas such as cross-sell and upsell, AI is no longer just a suggestion engine. It actively monitors customer behavior, flags relevant life events such as marriage or childbirth, and recommends personalized coverage enhancements. These conversational nudges consistently outperform traditional email campaigns in driving conversions.
Around the world, insurers are already operating with AI-enabled advisors at the frontlines. According to our Property and Casualty Insurance Digital Services 2025 Market Insights™ report, about three in four insurers spent over 5% of their digital budget on Gen AI/agentic AI in CY 2024 to achieve efficiency and productivity gains across the value chain. We also see enterprises utilizing Gen AI and agentic AI to support insurance advisors with personalized customer insights, improved efficiency, and elevated engagement.
The examples in Figure 1 below highlight that the shift from human-led to AI-augmented distribution is not experimental; it is already shaping day-to-day insurance operations.
Figure 1: Enterprise examples using Gen AI and agentic AI for insurance distribution
The Rise of the AI-Augmented Field Advisor
The future belongs not to AI alone, but to the symbiotic relationship between AI and human agents. In this model, AI handles routine tasks—fetching data, analyzing options, and managing workflows—while advisors focus on what they do best: building relationships, navigating complex decisions, and earning customer trust. Picture a field policy advisor walking into a client meeting equipped with a tablet. As the conversation unfolds, an AI copilot suggests optimal coverage based on the customer’s current policy mix and recent life events. It highlights gaps, simulates outcomes, scripts responses, and automatically logs the conversation. By the time the meeting ends, a personalized proposal is ready and a follow-up task is already scheduled.
This isn’t a future vision—it’s already being deployed by insurers who recognize that today’s insurance advisor must be part technologist and part relationship builder.
Challenges and Ethical Considerations
Despite the momentum, integrating AI agents brings a new set of challenges. Agent displacement is a real concern, particularly among traditional agents who fear redundancy. However, the reality is more nuanced. AI has automated repetitive tasks, not the need for human connection. Particularly in rural or senior customer segments, the human touch remains irreplaceable.
Important regulatory and ethical dimensions must also be considered. Generative models, if ungoverned, can hallucinate or misinterpret data. Regulatory bodies such as the National Association of Insurance Commissioners, European Insurance and Occupational Pensions Authority, and Insurance Regulatory and Development Authority of India are beginning to issue guidance on responsible AI use in insurance. Data privacy, decision-making transparency, and clear consent mechanisms are critical to maintaining customer trust.
Additionally, a digital divide persists. Urban millennials may embrace AI agents, but large segments of the population still prefer interacting with a human agent. Forward-looking insurers are responding with hybrid models that offer customers the best of both worlds.
Strategic Imperatives for Insurers
As AI becomes a cornerstone of insurance distribution, insurers must go beyond simply adopting new technologies. They must reimagine the role of the insurance advisor. This means equipping the field force with the right tools, skills, and support systems to thrive in a hybrid, AI-augmented environment. Here is how you can do this:
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- Deploy Gen AI copilots to support agents in real time: Equip field advisors with AI copilots that assist during client interactions, automate quote comparisons, summarize policy documents, and generate personalized recommendations, all while ensuring compliance with regulatory guidelines.
- Pilot agentic AI systems in products such as travel, motor, and health: Start with high-volume, rules-based product lines to test and scale agentic AI workflows for policy issuance, claims triage, and endorsements to free up human agents to focus on complex advisory cases.
- Implement targeted training programs to upskill advisors: Design structured training modules that help advisors understand how to interpret and apply AI-generated insights during sales and servicing and navigate compliance protocols in an AI-driven workflow; for instance, AI disclosures, consent collection, and audit trail review.
- Build omnichannel journeys that combine AI speed with human empathy: Enable seamless experiences where customers can switch between digital and human touchpoints without friction. AI agents can handle initiation and triage, while human agents step in for nuanced or high-emotion conversations, especially in health, life, and commercial insurance scenarios.
Conclusion: Insurance Distribution Has Already Transformed
The transition from traditional field agents to AI-augmented advisors is not a future possibility; it is already reshaping the insurance industry. AI has not displaced the field agent; it has redefined their purpose. Today’s most successful insurance advisors embrace AI as a trusted partner for working smarter, serving faster, and advising better. Insurers who recognize this shift and act decisively will not only drive efficiency but also forge deeper relationships with their customers, combining the scale and precision of machines with the empathy and wisdom of human advisors.
The age of the AI-augmented advisor has arrived. The only question now is: who will lead this transformation and who will be left behind?
By Praveen Kona, Associate Research Director, and Vishal Garg, Principal Analyst