9 Use Cases for Generative AI in the Insurance Industry

Glide recently surveyed more than 1,000 operations leaders and uncovered a striking insight: 28% of businesses are already leveraging AI, and another 45% are actively implementing it. That means nearly three-quarters of organizations are actively investing in artificial intelligence—and the insurance industry is no exception.

Among the different types of AI being explored, generative AI in insurance stands out for its ability to enhance customer experiences, automate complex workflows, and unlock new ways to work efficiently. Whether it’s drafting personalized policy documents, guiding claims, or equipping agents with real-time knowledge, generative AI is rapidly reshaping how insurers operate.

But what exactly does this technology do? And what should insurers keep in mind as they adopt it? 

Let’s dive in.

What Is generative AI in insurance?

Generative AI refers to AI systems that can create new content—text, images, audio, or even structured data—based on patterns it has learned from existing data. 

Using generative AI in insurance can mean everything from generating personalized policy documents, to drafting responses to customer inquiries, or even simulating scenarios for risk assessment.

Unlike traditional automation, which typically follows set rules, generative AI uses large language models (LLMs) and other deep learning techniques to understand context, nuance, and natural language—making it an ideal tool for complex, time-consuming insurance tasks.

Benefits of generative AI in insurance

When thoughtfully implemented, generative AI has the potential to improve nearly every aspect of an insurance organization across:

• Underwriting
• Improving customer service (I.e. reducing support tickets with better call deflection) 
• Optimizing sales funnels
• Internal operations

Let’s dive into the benefits of generative AI in insurance.

Make human agents more efficient

Insurance agents must navigate an ocean of information—policy details, compliance requirements, customer histories, and more. Generative AI helps by serving as an intelligent assistant, surfacing accurate information in real time.

For instance, tools like Capacity’s Answer Engine® use generative AI to allow agents to ask natural-language questions about anything, including: 

• Policy details
• Claims
• Company holidays 

Within seconds, the AI will pull the most relevant documents, down to the exact page number that includes the answer, and even provide a summary of results. 

Tools like Answer Engine® empower agents to handle inquiries more confidently, reduce errors caused by inaccurate or outdated information, and make faster decisions.

Automate repetitive workflows

Generative AI excels at automating repetitive or predictable content creation, such as:

• Drafting initial policy quotes
• Auto-filling forms based on user input
• Writing customer communications
• Summarizing claim histories

For example, an AI-powered chatbot can gather essential claim information, generate a summary for the adjuster, and even pre-fill forms to save time and ensure consistency. 

These automations reduce the burden on staff and eliminate common errors in manual processes, streamlining the entire insurance process internally and externally.

Enhance customer service while lowering costs

Today’s insurance customers expect fast, digital-first service that’s tailored to their needs. Generative AI helps insurers deliver just that, without scaling up customer support teams.

Chatbots and virtual assistants powered by generative AI can:

• Answer complex policy questions 24/7 or kick off claims processing
• Support customers as they manage their policy and verify coverage
• Enable customers to easily set up payments 

Because these systems learn and adapt, the more interactions they handle, the better they get. And rather than replace humans, they complement staff by handling high-volume requests so human agents can focus on high-impact interactions. 

Unlock revenue through personalization

Generative AI in insurance doesn’t just streamline operations. It creates new paths for growth.

By analyzing customer behavior, chat history, and preferences, generative AI can:

• Suggest relevant policy upgrades or bundles
• Personalize marketing campaigns
• Recommend next-best actions for agents

For example, if a customer asks about renters’ insurance and mentions their new car, a chatbot could proactively suggest bundling with auto insurance. With Capacity Analytics, insurers can track these interactions and identify trends that inform upsell strategies and improve retention.

Challenges and risks of generative AI in insurance

While the benefits are compelling, deploying generative AI in insurance comes with its share of challenges—especially in an industry known for regulation, data sensitivity, and risk aversion.

You might already be aware of the risks of using new tools, but as pressure mounts to innovate, it’s important to get a quick refresher of everything you should consider. 

The black box problem

One of the most discussed risks with generative AI is lack of explainability. Often called the “black box” issue, it refers to the difficulty of determining how an AI model arrived at a particular decision or recommendation.

Understanding why a decision has been made is critical in contexts like underwriting or claims. If a generative AI model recommends denying a claim, the insurer must be able to explain that rationale clearly to both the customer and regulatory bodies.

Here are some tips that can mitigate unfair or unexplainable claim decisions:

• Use transparent models where possible
• Require human review for high-impact decisions
• Keep an audit trail of AI-generated content

Privacy and data security

According to Glide, 51% of companies planning AI adoption cite privacy and data security as their biggest roadblock.

That’s no surprise—insurance companies handle deeply personal data, including financial and health information. Feeding this data into generative AI systems raises key questions:

• Is data anonymized before training?
• Where is customer data stored?
• Who has access to the AI’s output?

To minimize risk, insurers should:

• Encrypt all data
• Vet vendors for compliance certifications (like SOC 2, GDPR, HIPAA)
• Restrict internal access to sensitive inputs and outputs

These aren’t just best practices—they’re essential guardrails in a regulated environment.

Regulatory and compliance complexity

Insurance is among the most regulated industries worldwide. As insurers start to implement more and more advanced technology into their processes, staying compliant needs to remain top priority. Otherwise, insurers face costly liabilities and risks.  

Questions insurers must answer when looking at new AI tools:

• Does this tool meet regulatory and compliance requirements?
• How can automated claims processing decisions meet our audit standards?
• Do our chatbot disclosures meet regulatory guidelines?

Ultimately, each type of insurance and legal jurisdiction may have its own expectations for decision-making, privacy, and compliance. To keep implementations smooth and achieve the best results possible, insurers should assemble cross-functional teams for any new technology implementation, spanning legal, IT, operations, and customer experience. 

👉 Need help planning an implementation plan for a new AI tool? Check out our best practices for implementing AI in insurance tools!

9 real-world use cases of generative AI in insurance

Let’s explore how generative AI in insurance is already making a measurable impact and where it’s heading in the near future.

1. Risk assessment and premium calculation

Generative AI can analyze structured and unstructured data (like driving records, medical history, or location-based weather risks) to model and simulate various risk scenarios. This enables more accurate, dynamic pricing based on real-world conditions.

Some insurers even use real-time data from smart devices or connected vehicles to continuously adjust premiums based on user behavior.

This way, insurers can make faster decisions about risks and premiums, and their customers can get more consistent and accurate pricing.

2. Fraud detection

Implementing AI into your fraud detection process can make spotting red flags much easier, faster, and more accurate. Generative AI helps detect fraudulent claims by:

• Spotting linguistic patterns that resemble known fraud cases
• Comparing documentation for inconsistencies
• Generating reports for manual investigators to act on

By automating much of the review process, insurers can expedite investigations, reduce false positives, and ultimately identify more fraud—saving millions.

3. Customer profiling and segmentation

AI models can generate nuanced customer profiles by analyzing past behaviors, sentiment in support interactions, and even tone of voice. Within a CRM, this can help guide live support, identify opportunities for upsells or recommendations, and personalize outreach.

Plus, platforms like Capacity can automate support team training and call QA, making it easier to adapt to customer needs and preferences. 

When it comes to improving the customer experience, using generative AI in insurance is a great way to create quick and long-term wins.

4. Claims processing

One of the most practical and cost-effective use cases for generative AI in insurance is claims automation. AI tools can:

• Extract claim details from text, voice, or images 
• Automatically populate claim forms and databases
• Generate follow-up messages or escalation reports

Some home and auto insurers already use AI to process basic claims without human involvement—dramatically improving speed and satisfaction. 

👉 Want to streamline claims processing? Check out our best practices for automated claims processing!

Again, though, staying compliant and data-secure is critical for insurers. To make fair decisions, protect customer privacy, and follow regulatory guidelines, insurers should keep humans in the loop for every decision. 

5. Policy generation and document automation

Creating policy documents is traditionally time-consuming and rigid. With generative AI, insurers can automatically generate customized, compliant policies based on customer inputs.

Natural language generation ensures the language is clear, legal, and tailored to the individual, while minimizing manual work for teams. This can increase transparency and customer trust, while freeing human talent to focus on higher-value tasks.

6. Chatbots and AI-driven customer support

Another extremely practical, cost-effective, and accessible use of generative AI in insurance is customer support. Modern AI-powered insurance support can go far beyond static scripts, basic FAQs, and website chatbot windows.

Rather, they can:

• Answer complex questions in multiple languages across channels, including SMS and voice
• Summarize and analyze prior customer interactions to improve over time
• Execute tasks like password resets, appointment scheduling, policy recommendations, and more

Tools like Capacity use one knowledge orchestration layer—simply put, a database of all company documentation and policy—to power all customer support activities. This means that every time a virtual agent answers a question, it’s accurate and up-to-date.

Plus, it connects across channels, keeping customer context and personalizing experiences across web, SMS, voice, and email.

7. Virtual claims assistants

Claims don’t always happen during the 9-5. For those inquiries that come in after hours (or during times of heavy volume), virtual claims assistants can help.

Virtual claims assistants, powered by generative AI, work 24/7/365, so that customers don’t have to wait for one-on-one support. These assistants can guide customers through claims submissions, request required documents, and even generate updates on claim status. 

Implementing a virtual claim assistant can not only help streamline processing and improve the customer experience, but also reduce the burden on teams to answer every repetitive question, over and over again.

8. Agent assistants and knowledge management

Customer-facing staff need fast access to accurate information, so that they can provide satisfying, prompt customer experiences—and reduce their own risk for errors. Generative AI enables “agent copilots” that:

• Automate caller authentication for more seamless calls
• Suggest responses during live chats or calls and identify areas for improvement
• Auto-complete common inquiries and tasks with supporting data
• Retrieve relevant documents instantly

With Capacity’s Answer Engine, insurance teams can search their internal knowledge base using everyday language and get real-time, relevant answers, reducing time to resolution and boosting confidence.

9. Underwriting assistance

Generative AI supports underwriting by simulating customer scenarios, analyzing large volumes of third-party data, and drafting reports or approval recommendations. This shortens review cycles and ensures consistent decision-making across the organization.

Wrapping up

The rise of generative AI in insurance represents a major shift in how insurers operate, innovate, and serve their customers. By automating document creation, enhancing agent performance, and personalizing customer interactions, generative AI offers a powerful advantage in an increasingly competitive landscape.

That said, thoughtful implementation is key. Insurers must navigate privacy concerns, regulatory requirements, and AI transparency to ensure long-term success. With the right foundation and strategic planning, generative AI can become not just a tool, but a core driver of future growth and efficiency in the insurance sector.

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