Insurance AI Agents: How Automation Is Reshaping the Industry

The insurance industry is under pressure to move faster, operate leaner, and deliver more personalized service without increasing risk. At the same time, the volume of data insurers must process is growing exponentially. AI agents offer a path forward.

These intelligent systems are redefining how work gets done—automating routine processes, surfacing insights in real time, and enabling teams to make better decisions with less effort. For insurers, this means faster claims, smarter underwriting, more efficient operations, and better customer experiences.

Whether you’re a business leader exploring automation, a new analyst supporting operational decisions, or a department head looking to modernize your tech stack, understanding how AI agents work and how to implement them is a strategic imperative.

In this guide, we’ll walk through:

• What insurance AI agents are and how they operate
• The different types of agents and their use cases
• Real-world and future-facing examples across claims, underwriting, and customer service
• The benefits, challenges, and strategies for adoption
• How platforms like Domo help insurers deploy AI agents at scale
  
  

This isn’t just about emerging tech—it’s about the future of how insurance is priced, delivered, and supported. Let’s get started.

What is an insurance AI agent?

An AI agent in insurance is an intelligent system—typically powered by machine learning, natural language processing (NLP), and predictive analytics—that acts autonomously or semi-autonomously to complete specific tasks. These agents interpret data, make context-based decisions, and take action across various insurance workflows.

Unlike traditional automation tools that follow static rules, AI agents adapt to new information, learn from outcomes, and operate in dynamic environments. This makes them especially useful in areas like claims processing, underwriting, policy servicing, and fraud detection—where speed, accuracy, and adaptability are essential.

How insurance AI agents work under the hood

While AI agents are designed to be intuitive on the surface, they rely on several sophisticated components behind the scenes:

• Data ingestion and normalization: Agents collect structured and unstructured data from internal systems (policy admin, CRM, claims processing) and third-party sources (weather data, risk scoring databases).
  
  
• Machine learning models: These models learn from historical data to identify patterns and generate predictions. For example, they might flag suspicious claims based on historical fraud indicators.
  
  
• Natural language processing (NLP): Enables agents to interpret text or voice interactions—powering chatbots, email assistants, or smart intake forms.
  
  
• Decision frameworks and triggers: Once an agent evaluates input, it applies logic to determine the next step: approve, escalate, request more info, or trigger a workflow.
  
  
• Continuous feedback loops: As users interact with agents, their feedback improves future performance. This could involve model retraining or prompt refinement.

More advanced systems may also integrate retrieval-augmented generation (RAG) or large language models (LLMs) for better context and language comprehension.

Expanded types of insurance AI agents

AI agents in insurance are no longer limited to chatbots or simple workflow automations. As capabilities evolve, so does the diversity of agent types—and their value across the enterprise. Today’s AI agents are designed to specialize: some handle real-time decisioning, others focus on back-office optimization, while many serve as customer-facing assistants that improve response time and satisfaction.

Understanding the different types of AI agents and where they fit into the policy, claims, and servicing lifecycle is essential for building a scalable automation strategy. It also helps teams prioritize implementation based on business goals, whether that’s reducing operational costs, enhancing customer experience, or improving underwriting accuracy.

Below is a breakdown of the most common and emerging agent types in insurance today. Each category reflects a specific role an agent can play, and many insurers are deploying multiple types in parallel across departments.

1. Customer service agents

NLP-driven bots that manage inquiries, policy updates, document requests, or billing questions.

2. Claims processing agents

Automatically extract data, classify claim types, validate inputs, and triage based on complexity or urgency.

3. Underwriting assistants

Support or fully automate risk scoring and quote generation based on structured data, third-party records, and prior outcomes.

4. Back-office automation agents

Perform policy document updates, regulatory language checks, billing reconciliation, and reporting.

5. Risk modeling agents

Evaluate exposure using internal policies and external data (e.g., wildfire zones, flood risk).

6. Pricing and quoting agents

Leverage customer data, market trends, and behavioral signals to offer dynamic and personalized pricing.

7. Retention prediction agents

Analyze engagement, claims behavior, and market signals to identify at-risk policyholders and recommend interventions.

8. Embedded partner agents

Operate inside broker portals or white-labeled platforms to support distribution partners and embedded insurance offerings.

Each of these agents can operate independently or as part of a coordinated multi-agent system.

From concept to claims: How AI agents are transforming insurance in practice

While the potential of AI agents is widely discussed, their real impact is clearest when seen in action. Across the insurance industry, forward-thinking carriers are moving beyond experimentation and integrating AI agents into daily operations—from customer onboarding to claims resolution and compliance.

These implementations are not just proof of concept—they’re delivering measurable results. AI agents are reducing cycle times, improving accuracy, cutting costs, and unlocking new levels of customer responsiveness. And while early adopters are already seeing ROI, the use cases continue to expand with advances in data infrastructure, NLP, and machine learning.

In this section, we’ll look at real-world deployments that demonstrate how insurers are using AI agents today, along with hypothetical but plausible examples that showcase what’s possible as the technology matures. Together, they illustrate how AI agents are evolving from isolated tools into integral drivers of modern insurance operations.

Real-world deployments

• Lemonade: Uses its AI bot “Jim” to approve simple claims in seconds after fraud validation and document checks.
• GEICO and Progressive: Employ customer service agents for quotes, policy changes, and digital claims intake.
• Zurich Insurance: Applies NLP to analyze policy documents and highlight regulatory risks, saving underwriters time.

Hypothetical but plausible use cases

• Storm triage: An AI agent filters and prioritizes homeowner claims after a regional weather event based on satellite imagery and customer reports.
• Business onboarding: A commercial insurance agent helps new clients select the right coverage, verify business licenses, and submit documents.
• Policy compliance: An agent continuously scans new legislation and flags clauses in legacy policies that may need updates.
• Roadside accident intake: A voice-based agent helps a policyholder file an FNOL, verify location, and submit photos from their mobile device.

These examples show the breadth of ways agents can be embedded across departments, channels, and workflows.

Why AI agents are a strategic advantage for insurers today

AI agents are not just a technical upgrade—they represent a fundamental shift in how insurers operate, compete, and serve customers. While many organizations begin with automation to save time or reduce costs, the real value of AI agents emerges when they’re aligned with broader business goals: improving decision quality, enhancing service delivery, and enabling scalable growth.

For insurers navigating rising customer expectations, increasing regulatory complexity, and growing data volumes, AI agents offer a path to greater speed, intelligence, and agility. They work around the clock, adapt to changing inputs, and create compounding value as they learn over time.

This section explores the core advantages AI agents deliver across operations, decision-making, risk management, and customer experience—and why insurers who move early are positioned to lead in a rapidly evolving market.

1. Operational efficiency

Automate repetitive tasks like document verification, data entry, and triage, so teams can focus on exception handling and strategic decision-making.

2. Speed and responsiveness

Handle requests and decisions in real time, reducing delays in quote generation, claims processing, and customer support.

3. Greater accuracy and lower risk

AI models flag anomalies, reduce human error, and ensure compliance with regulations through consistent application of rules and logic.

4. Scalable customer experience

AI agents can deliver 24/7 service, personalized recommendations, and seamless handoffs to human support, enhancing satisfaction without increasing costs.

5. Data-driven innovation

Every interaction generates feedback. Over time, this data can reveal customer needs, pricing opportunities, and new service models.

How to automate with AI agents in insurance

Recognizing the value of AI agents is one thing—successfully implementing them is another. For many insurers, the challenge isn’t interest or intent, but knowing where to begin. Fortunately, automation doesn’t require a full-scale transformation from day one. The most effective AI initiatives start with clear objectives, targeted use cases, and an iterative approach.

This section outlines a practical path forward. Whether you're automating a single workflow or laying the foundation for an enterprise-wide strategy, these steps will help ensure that your investment in AI agents is aligned, scalable, and impactful from the start.

1. Target the right workflow: Start with a process that’s high-volume, rule-based, and measurable—like FNOL processing or onboarding.
2. Audit your data: Make sure you have clean, structured data and the ability to connect sources (internal and third-party).
3. Select the right tooling: Look for platforms that offer flexible model integration, workflow orchestration, and auditability.
4. Pilot and expand: Deploy the agent with clear KPIs. Learn, adjust, and roll it out more broadly as confidence grows.

Building blocks of a successful AI agent strategy

To implement AI agents successfully and sustainably, insurers should focus on five pillars:

1. Data accessibility and trust

Ensure clean, well-governed pipelines connect critical systems like policy admin, claims, CRM, and billing. Include external sources when needed (e.g., weather APIs, third-party scores).

2. Modular workflow design

Break large initiatives into manageable, outcome-focused workflows. Use modular building blocks that can be reused and scaled.

3. Integrated tooling and orchestration

Agents must work across systems. Choose platforms that enable real-time automation and alerting across business units, like Domo.

4. Cross-functional ownership

Assign product, business, compliance, and data leads to each agent initiative to ensure alignment across strategy, risk, and performance.

5. Feedback and iteration

Embed user feedback loops, performance monitoring, and regular retraining cycles. AI agents improve continuously if you let them.

Change management and adoption

Technology is only one part of success. Organizational alignment and adoption are equally important.

Barriers to anticipate

• Job security concerns: Employees may fear replacement or loss of control.
• Low visibility: Teams may not understand how the agent works—or why it was deployed.
• Fragmented workflows: Without integration, agents become siloed and underutilized.

Adoption strategies

• Start with champions: Pilot agents in departments open to experimentation. Involve users in design.
• Communicate clearly: Explain how agents enhance—not replace—human roles.
• Provide structured training: Teach employees how to collaborate with agents, escalate decisions, and improve performance.
• Highlight wins: Share success metrics like time saved, claim speed, or NPS improvements.

Change management should treat AI agents as part of the team—worthy of onboarding, oversight, and recognition.

What AI agents mean for the workforce

AI agents are not replacing the workforce—they’re transforming it.

A shift in human roles

• Claims adjusters become fraud specialists, reviewers, and trainers of AI systems.
• Underwriters shift from data entry to strategic analysis and exception review.
• Customer service reps spend more time solving complex issues, less time answering basic questions.

New roles are emerging

• AI operations managers monitor agent performance and escalate issues.
• Model governance leads ensure compliance with fairness and transparency standards.
• Prompt engineers fine-tune agent instructions and performance.
• Data product owners connect agents to quality data and define KPIs.

Upskilling is essential

Organizations that invest in AI literacy—from prompt design to ethical evaluation—will build more resilient, future-ready teams.

What’s next: the evolving role of AI agents in insurance

The role of AI agents will continue to evolve. What’s emerging now:

From assistance to autonomy

Agents will complete full workflows—renewing policies, updating pricing, handling communications—without manual input.

Collaborative agent networks

Multiple agents will work together across business units—risk scoring, claims, fraud detection, and customer retention—passing data and decisions between them.

Embedded intelligence

Agents will become ambient—always running, always optimizing—across the policy lifecycle, not just visible at key touchpoints.

Regulation and explainability

As agents make higher-impact decisions, they’ll have to be transparent and auditable. Insurers will need systems for override, bias detection, and disclosure.

Broader data ecosystems

Future agents will draw from climate models, smart home sensors, economic indicators, and healthcare data (with consent) to support proactive insurance.

The next generation of agents won’t just support workflows—they’ll continuously reshape them.

How Domo helps

Domo’s AI agent platform helps insurers move from data to action—faster.

With:

• Native support for LLMs and task automation
• Embedded dashboards for real-time insights
• Workflow builders and alerting across systems
• Data connectors for underwriting, claims, CRM, and finance

Insurers can prototype, test, and scale AI agents that reduce workload, improve decisions, and create better customer experiences—all in one platform.

Explore what’s possible at domo.com/ai.