Step into any insurance firm boardroom today and say the words “AI” and someone will be reminded of chatbots. They were for years viewed as a low-cost manner of providing 24×7 customer service and taking the pressure off some call center. And of course, they did help. But come on: most chatbots were never more than virtual receptionists.
What we are witnessing here is change. Genuine change. AI in insurance is not merely about responding to questions anymore. It’s about knowing, advising, and acting. The new AI agents are not here to simply respond. They are here to assist customers in a way that is customized, intelligent, and even humane.
Moving Beyond Lists and Filters
Historically, when a consumer searched for an insurance product on the web, they were presented with a list. Occasionally it was sorted. Frequently it was intimidating. That is shifting.
Today’s AI agents are learning to read between a customer’s lines. Not just their age or income bracket, but how they click, where they hesitate, what they ask, and what they don’t. These agents can sense hesitation, confidence, curiosity, or even confusion.
What does it imply for insurance? It would imply that a parent who is young would be steered to a child benefits-enabled flexible health policy. A retiree will be offered a simple renewal package without add-ons. It is not selling products; it is showing the correct fit. This form of micro-personalization is a builder of trust.
Making Claims Less Painful
Complaints have always been the insurance make-or-break point. And let’s be real: customers don’t typically depart in wonder. Long phone wait times, lost papers, phone tag.
With AI agents, we’re beginning to see that happen. These systems are able to walk a customer through filing, verify documents in the moment, catch inconsistencies, and even give real-time status updates on claims. If something’s incorrect or missing, the agent can catch it before it turns into a delay.
Consider, for instance, a motor claim. Suppose an AI assistant guides the user through the steps of uploading photos of breakdowns, matching them against typical patterns of damage via image recognition, and automatically estimating the cost from historical claims data. It can even arrange pickup for a stranded vehicle and suggest network garages.
The goal is not to displace human interaction entirely. Instead, it is to deal with the repetitive and infuriating parts so that when a human does step in, they are contributing real value solving conflicts, offering compassion, or enabling escalations.
At Fluid AI, we’ve a robust portfolio of AI-powered customer service solutions designed specifically for insurers so they can engage with customers on the channels that suit them best. Whether it’s smart email support that auto-generates context-relevant answers, chatbots that engage in natural, human-like flows, or advanced voice agents that handle real-time queries with multilingual support, our platform answers every touchpoint.
Every AI agent is built to sense nuance, learn without boundaries, and respond empathetically so every customer interaction feels effortless, personalized, and productive. It’s not a matter of choosing a single channel, it’s about giving insurers the solutions to offer smarter support where their customers are.
Why It Matters Now
In India, the insurance sector is transforming. Digitalization is on the rise. Consumers are younger, mobile-first, and less patient. Concurrently, the regulation is evolving and the competition is aggressive, which is forcing the insurers to differentiate.
AI agents help to tackle both sides of the challenge. They offer scale without sacrificing experience. They help you deliver personalization without needing an army of analysts to back every customer interaction.
For brokers, AI agents can pre-qualify leads by examining behavioral cues, fill out proposals automatically, and detect possible misrepresentation all before the policy is issued.
They also minimize reliance on static complex workflows. The same agent who can assist in onboarding a customer can also guide them through policy adjustments, renewal, and claims thus offering consistent interaction across the lifecycle.
The Role of Agentic AI
While chatbots are about automation, agentic AI is about autonomy. These systems do not wait for someone to tell them what to do but can learn, develop, and take the initiative.
Let’s say an artificial intelligence agent notices a trend of recurrently late premium payments from a particular customer. Instead of sending the default reminder, it gives the customer the option of either rescheduling the payment date or paying in installments. Or let’s say an agent notices a spike in health-related claims coming from a specific region and kindly urges the policyholders to get preventive health check-ups.
Another example is below: A new policy buyer from a tier-2 city does not finish his KYC process. Instead of waiting for a customer care call, the agent triggers a guided video KYC process, nudges the customer by offering regional language assistance and then follows up with time-sensitive reminders that are dynamic based on user interaction.
These aren’t hard-coded scripts. They’re smart systems, programmed to react to patterns and context. They learn, adapt, and get better without an update cycle.
Agentic AI therefore implies coordination between agents. One person does onboarding, and another person does claims. They exchange information behind the scenes, learn from one another’s experience, and adjust their logic on the basis of results.
Use Case: Agentic AI for Policy Upgrades
Let’s say the customer has a five-year term policy. Based on their profile changes e.g., new dependents, increase in income, or relocation the AI agent can trigger a review conversation for the policy. Rather than selling a customer a higher premium policy directly, the agent can simulate various upgrade scenarios based on the customer’s expense patterns, risk appetite, and lifestyle.
It may give three tailored choices: policy term extension, accidental rider addition, or the current policy with a stepped-up sum assured in phases. And all this with context describing the thinking behind the choices, the calculation done, and the possible consequences.
The customer is seen, heard, and in charge. That’s what happens when agentic intelligence is brought into action. A good AI agent can lay the groundwork. It can gather the facts, sense the mood, and raise the right options. Then, when the human advisor gets involved, they are halfway to the solution.
What Makes AI Agents So Capable
What is happening in the background that enables these AI agents to become so intuitive?
Fundamentally, it’s actually a combination of a number of AI domains into one. You have your large language models (LLMs) that comprehend natural dialogue, not key phrases but intent, tone, and context. These enable agents to maintain a conversation over time and across channels, remembering what was said and modifying based on that.
And then there is live data integration. The agent is not reading from a script, it is seeing live policy information, CRM information, user activity, claims history, even engagement cues like response time or whether someone is stalling halfway through a form. That capacity to see context in real time is what creates the illusion of personalization.
Lastly, the actual change is realized when these systems are empowered to improve themselves. This aspect is the “agentic” component. As opposed to relying on constant developer intervention to introduce new flows or modify responses, these agents develop from past successes, failures, and customer feedback. If a given upsell prompt fails to receive any interaction repeatedly, it is removed. If, on the other hand, a subsequent email receives improved response when sent during a particular time, the system learns in response.
Consequently, insurers that successfully implement these systems discover that they not only scale more effectively but also become more intelligent with each individual interaction.
What Insurers Should Think About
As these systems become more intelligent, insurers need to think very hard about how they should be implemented and deployed.
Transparency will be key. Customers must be informed when they’re interacting with an AI agent. They must be able to query why something was suggested. And they must always have a simple route to a human if they so desire.
Regulators will also want to make sure that AI is not being utilized to aggressively upsell or discriminatorily deny claims. Which is why the top AI systems are not only intelligent. They’re auditable, explainable, and ethical by design.
How Indian InsurTech is Pushing the Frontier
India’s InsurTech landscape is now among the most dynamic globally. Most striking, however, is not so much the quantity of innovation, but its localization.
AI agents designed for India understand the nuances: native languages, rural connectivity gaps, UPI-based integrations, Aadhaar-linked authentication, and even Indian consumer-specific behavioural cues. These are not transfer technologies, these are tailored for the Indian ecosystem.
Local insurers and InsurTech companies are now leveraging multilingual AI agents to guide customers through intricate product journeys using local dialects. Some of the companies are even integrating these agents into onboarding through WhatsApp, as customers are already highly familiar with the platform.
If we look ahead
The argument in InsurTech now is not whether to utilize AI, but how to do it effectively.
Insurers who employ AI agents now are not just simplifying the process. They are setting the scene for greater trust, greater loyalty, and a customer experience that actually does feel like it’s been designed with the customer in mind.
Finally, insurance isn’t just about policies; it is actually about human beings. If artificial intelligence can help us provide our service in a more human and personalized way, then it is an investment worth making.
We think India’s InsurTech ecosystem isn’t only prepared for Agentic AI, it’s designed for it. The complexity of user behavior, multilingual subtlety, and raw digital density require systems that can think, learn, and act like human beings without the need to be reprogrammed by hand each time.
We see the world where all policy discussions, claim investigations, or upsell recommendations are handled by smart agents with the ability to understand in real time. That is not automation, but smart orchestration. In addition, we are working together with forward-thinking insurers to bring that future to the present.
Agentic AI is not a future promise. It is a functioning reality already in the testing stages across retention, servicing, claims, and underwriting.
Authored By:
Raghav Aggarwal
Managing Director & Co-Founder, Fluid AI, Mumbai
Abhinav Aggarwal
CEO & Co-Founder, Fluid AI, Mumbai
