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What once took days of manual effort now can happen in minutes. With agentic AI, insurers are automating not just repetitive tasks, but entire workflows.
Down below, we’ll show you 9 powerful use cases where insurance workflow automation is driving real results: faster processing, better accuracy, and improved customer experiences.
Before agentic AI, insurers could only automate simple, rule-based tasks like processing, document uploads, and basic triage using RPA.
Anything that involved human judgment or cross-system coordination still relied on humans.
Agentic AI changed that.
Agentic AI systems not only follow rules and instructions, but they can also make autonomous decisions, understand goals, work with other AI agents and third-party tools in real-time to achieve the goal.
This helped insurers automate complex workflows, which included handling claims processing end-to-end, coordinating across departments, and making decisions based on context.
We saw this with a client whose claims process was stuck in silos. With agentic AI, they deployed an autonomous agent that read policies, cross-checked historical relevant data, flagged inconsistencies, and routed decisions.
This cut the time from days to hours.
Therefore, this isn’t the automation level we knew. Now it’s more dynamic, context-aware, and evolving than ever before. We see how it’s opening doors insurers didn’t even know were there.
Problem:
Traditional insurance automation struggles with complex documents, inconsistent data formats, and the nuanced judgment calls underwriters make daily. This slows down the policy approval and leaves room for human error.
Solution:
Agentic AI systems that handle the complexity of the underwriting process by interpreting documents, integrating with core systems, and applying underwriting rules dynamically and autonomously. Agentic AI can adapt to policy types, read unstructured data like medical reports, and flag and escalate cases when human judgment is truly needed.
Real-world example:
A major U.S.-based insurance group that partnered with us to deploy an automated underwriting system.
This agent extracted key fields from complex documents with over 95% accuracy, made decisions in under 15 seconds, and supported custom templates for different policy types.
This led to faster approvals, lowered the costs, and improved operational efficiency without adding any risk.
Problem:
Claims processing is fragmented and often involves multiple departments, legacy systems, and unstructured inputs like handwritten notes, emails, or even scanned documents. While traditional automation can handle form-based inputs, it struggles with making decisions in real-time, fraud detection, and the ability to adapt to specific policy rules.
Solution:
Agentic AI that automates the entire claim process, from intake to resolution by reading unstructured data, cross-referencing it with internal data, and coordinating across various systems. Such AI agents can triage claims, identify inconsistencies, and make or recommend settlement decisions based on context.
Real-world example:
Allstate Corporation is the greatest example of an insurance company that has automated claims processing with the help of AI. This company expedites the claims processing, which reduces the time it takes to settle claims and gets policyholders paid faster.
When a privacyholder submits a claim, Allstate Corporation’s automated claim processing handles data and makes an assessment based on its processing system, which is also equipped with loan process fraud detection capabilities.
As a result, Allstate Corporation enhances efficiency, improves customer satisfaction, gets through claims processing faster, and gets much more accurate results while lowering the human error rate. Overall, with claims processing automation, insurance companies can improve the efficiency, reliability, accuracy, and timeframe of the claims processes.
Problem:
Managing renewals and cancellations involves learning customer preferences, policy changes, usage data, and compliance checks. While legacy systems can trigger reminders, they can’t reason through and understand context as in when a customer might churn, when to intervene, and how to personalize retention strategies.
Solution:
Agentic AI agents monitor policyholder behavior, internal data (such as claims history), and external signals (such as regulatory changes). With this information and by understanding context, AI agents can proactively recommend renewal terms, flag risky customers, and generate tailored offers to automate delicate decisions with nuance.
Real-world example:
Liberty Mutual automates policy renewal and cancellation through a variety of channels, such as the website and the mobile app, where policyholders can review, renew, initiate changes, or cancel their existing insurance policy.
Without needing human intervention, Liberty Mutual’s automated workflow not only handles customer queries and actions but also automatically offers renewal options or alternative policy solutions based on the customers’ data and preferences.
Such policy renewal automation helps expedite the time needed to renew, change, or cancel the policy, ensuring better customer satisfaction and eliminating the intervention previously required to handle policy-related queries.
Problem:
Insurance companies have to manage a high volume of transactions every day. Legacy systems often lack real-time data handling, which leads to delays, errors, and missed payments. Such inefficiencies hurt both operational margins and customer experience and trust.
Solution:
Agentic AI changes payment processing by acting as a layer between internal systems, customer communications, and third-party platforms. Such an AI agent can handle contextual decision-making, proactively initiate or approve payments, flag discrepancies in real-time, and ensure better accuracy while adopting regulatory and company rules.
Real-world example:
We helped a global telecommunications firm that operates with a high volume of payments deploy a customized agentic AI solution to optimize internal financial workflows.
It was facing disbursement, inconsistencies in data, and manual reconciliation across departments, before an AI agent that reduced payment cycle times by 50% and enabled same-day reconciliation.
While this is not an insurance company, the same agentic AI solution can be applied in claims disbursment, premium collection, and back-office financial reporting to increase speed, compliance, and customer satisfaction.
Problem:
Customer pre-approval verification is a fragmented process that involves coordination across siloed systems and manual review, which slows down onboarding and frustrates both agents and customers.
Solution:
Agentic AI automates this process by acting as a persistent and intelligent coordinator between the systems. AI agents can pull information from CRMs, policy management systems, and internal databases to verify identity or eligibility criteria.
It can also flag inconsistent information, make approval recommendations in real time, and adapt dynamically to updated underwriting rules or regulatory requirements without needing to be manually programmed.
Real-world example:
Progressive uses agentic AI to automate pre-approval verification during online policy quotes and application flows. When a potential customer submits their information, an AI agent instantly verifies key data points—such as driving history, credit score proxies, and prior coverage—by connecting to third-party data providers and internal systems in real time.
The agent determines eligibility and generates personalized policy options within seconds, without human review. It also flags inconsistencies or missing information and prompts users to resolve them instantly via the app or website.
This automated pre-approval process has led to a faster quote-to-bind conversion rate, fewer dropped applications, and an overall increase in customer acquisition through digital channels.
Problem:
New insurance customers often face an inconsistent and even confusing onboarding experience that includes lengthy forms, scattered instructions, and long wait times for human assistance. The insurance industry has to handle ID verification, document submission, product selection, compliance disclosure, and more.
When all of these get handled manually, the process becomes slow, it’s prone to errors, and it’s frustrating for both customers and internal teams.
Solution:
Agentic AI changes onboarding into a guided, intelligent workflow. AI agents can initiate personalized conversations with new users, explain key steps in simple language, collect and validate documents, trigger backend system updates, and answer questions in real-time.
These AI agents adapt dynamically to customer responses, regulatory needs, or risk levels, all while ensuring compliance and improving the onboarding experience.
Real-world example:
AXA allows potential customers to provide necessary customer information through various channels such as the website and the mobile app. From there, AI takes over and provides a personalized experience for each potential customer.
By analyzing inputted information, AI can offer tailored solutions based on customers’ specific needs and preferences. AXA’s AI system continuously learns and improves with each customer interaction, helping the company redefine and optimize its onboarding process for the best results.
The final result is a faster and improved customer service experience with a seamless automated onboarding process that benefits both the company and its customers.
By streamlining the onboarding process, the insurers can handle more customer requests. This can drive business growth without sacrificing user experience.
Problem:
Support teams are overwhelmed by repetitive questions, inconsistent knowledge bases, and fragmented internal systems.
On the other hand, customers are frustrated by long wait times, delayed responses, and the need to repeat themselves across channels. Therefore, a real problem is that resolution time lags, satisfaction scores drop, and valuable support agents are stuck handling low-value tickets.
Solution:
Agentic AI agents can act as real-time support assistants, access internal data sources and deliver instant, context-aware answers to customer inquiries.
Such AI agents can also access and search across knowledge bases, CRM notes, order histories, and policy documents. For resolving more complex cases, they collect all necessary information before escalating to a human agent.
Real-world example:
A sales-oriented insurance company used an AI agent to unify fragmented internal data and automate customer support.
This resulted in faster responses, fewer escalations, and improved customer satisfaction. Agents could easily surface relevant information from policies, claims, and prior interactions, enabling accurate answers in seconds rather than hours.
As a result, the company saw higher client retention and more effective upselling conversations, all while reducing support team strain.
Problem:
Financial services and insurance companies face pressure to comply with evolving regulations, from KYC/AML rules to privacy laws and industry-specific compliance requirements.
Traditional compliance monitoring is time-consuming, reactive, and it relies a lot on manual checks and periodic audits. Such an approach leads to an increased risk exposure, slower reporting, and missed violations.
Solution:
Agentic AI helps in real-time by proactively monitoring compliance, scanning transactions, communications, and documents for anomalies or violations. AI agents can interpret regulatory language, track changes in policy requirements, flag suspicious activities, and generate audit-ready reports.
Unlike legacy systems, agentic AI adapts to new rules dynamically, helping organizations stay compliant and responsive without big system overhauls.
Real-world example:
AIG is one of the companies that took advantage of AI to monitor regulatory changes, analyze complex legal documents, and ensure compliance with applicable laws. By automating this task, any company can benefit from risk assessment and identification of potential compliance risks, where the only human interference needed is to take necessary actions and mitigate the risk.
Alongside compliance, AI systems are good at generating reports for stakeholders, regulators, and internal teams while requiring very minimal help from human resources. AIG is also taking advantage of this by automating AI to generate and provide financial and performance reports.
Since the AI system is designed to adapt to regulatory changes, it continuously learns to stay current with the industry’s standards.
Problem:
Marketing and sales teams often work with incomplete, outdated, and disconnected customer data. As a result, campaigns are poorly targeted and outreach lacks personalization, while teams are missing out on engagement opportunities.
Manual data entry combined with slow lead qualification and inconsistent follow-ups only leads to lost revenue and inefficiencies.
Solution:
Agentic AI improves marketing and sales operations by unifying data across CRMs, email systems, analytics tools, and customer interactions.
AI agents qualify leads in real time, personalize outreach, and suggest actions for sales reps. For marketing, AI agents can generate audience segments, monitor campaign performance, and make dynamic adjustments to optimize ROI without human micromanagement.
Real-world example:
Prudential Financial is a great example of a company that utilizes AI for its marketing efforts.
An AI system trained on company-specific quality data can identify potential customers with the help of AI-driven predictive analytics. By analyzing vast amounts of data, AI systems can target potential customers based on their demographics, online behavior, and even life events, ensuring that they find and reach out to individuals who could most benefit from the company’s products and services.
Besides analyzing a vast amount of data to find potential customers who would benefit from the company’s services and products, AI can also help personalize the outreach and offer suitable insurance solutions.
It was our pleasure speaking with Meredith Barnes-Cook, the ReSourcePro Consulting Partner, on our podcast, where she shared key insights on insurance process automation.
The key benefits she highlighted included:
Automation eliminates bottlenecks across underwriting, onboarding, policy servicing, and claims handling.
Repetitive manual tasks such as data extraction, document classification, and multi-system lookups are handled by agentic AI, freeing up human agents to focus on exception management and higher-value work.
Manual data entry and fragmented communication increase the risk of errors and regulatory breaches.
Automated workflows reduce human error while enforcing compliance standards consistently across regions, teams, and policy types.
As Meredith notes:
“Speed and accuracy are what drive satisfaction”.
Automation reduces delays, eliminates redundant information requests, and helps customers and agents get timely answers.
This leads to higher retention and stronger relationships across the board.
Workflow automation enables carriers to handle surges in demand without needing to rapidly expand the workforce. AI handles high-volume tasks so teams can scale without burnout or bloat.
By integrating and automating data flow across silos, insurers get real-time insights for better decision-making.
Whether evaluating a claim, updating underwriting models, or refining customer journeys, workflow automation ensures decision-makers are no longer flying blind.
Successul insurance workflow automation is about more than just speed and cost savings. It’s about trust, traceability, and alignment with business goals.
Therefore, here are the key practices to ensure automation delivers both efficiency and accountability:
Not all decisions should be autonomous. For processes like claims adjudication, underwriting exceptions, or fraud escalations, AI should support human decision-makers instead of replacing them.
Keeping a human in the loop ensures that complex or high-risk scenarios are handled with oversight.
Automation without transparency creates risk.
Every automated workflow should generate a detailed audit trail. This is especially critical in highly-regulated industries where insurers must demonstrate compliance.
Stakeholders and regulators need to understand how decisions are made.
Implement systems that provide plain-language rationales for actions taken in processes like claims processing. Explainability reduces friction, builds trust, and helps teams troubleshoot when something goes wrong.
Without clear governance, automation can quickly become unmanageable.
Define who owns each workflow, how updates are rolled out, and what escalation paths are. This includes setting boundaries on AI autonomy, establishing version controls, and assigning responsibility for ongoing performance monitoring.
Automation should empower, not replace human connections.
Therefore, build workflows that remove friction but allow human agents to step in when needed. For example, flagging moments of customer frustration or high emotional stakes where a personal touch will make a difference.
Automation isn’t one-and-done deployment.
Monitor key performance indicators (KPIs), gather feedback from both customers and internal users, and continuously refine workflows to adapt to changing business needs, customer behavior, and regulatory changes and requirements.
Would you like to automate your insurance workflows to benefit from faster and more accurate processing, cost savings, and improved scalability that leads to better ROI?
Please book a demo to see how AgentFlow works live and how it can automate your existing workflows without a major overhaul in 90 days or less.
Yes, insurance can be automated and artificial intelligence can help streamline operations, improve efficiency, and enhance customer service. The most popular ways to automate insurance include automated underwriting systems and claims process automation, among others.
Automation in insurance is very beneficial and it affects the industry in positive ways such as increasing efficiency, reducing cost, improving customer experience, enhancing data analysis, assessing risk better, detecting fraud, and more.
When you work with us, you can automate your workflows in as little as 8 weeks. This is approximately how much it takes us to customize and integrate AI into your existing workflow.
Explore how our agentic AI can automate your workflows and boost profitability.
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