6 Best Practices for Implementing AI in Insurance

From underwriting and claims processing to customer service and compliance, AI can help insurance companies reduce costs, improve customer satisfaction, and increase operational efficiency.

In fact, according to a recent survey from Glide of over 1,000 operations leaders, 28% of businesses are already using AI, and another 45% have active implementation plans underway. That means nearly three-quarters of today’s organizations are already optimizing with AI.

And for insurers, the pressure to modernize is mounting. 

As with any transformative technology, success and meaningful ROI results hinge on clear strategy and thoughtful execution. So let’s break down:

• What AI in insurance can do
• How it’s changing the industry
• Best practices for implementing AI in your insurance processes.

How is AI used in the insurance industry?

AI in insurance refers to the application of machine learning, natural language processing (NLP), and automation to optimize processes like claims handling, customer support, underwriting, and more.

Some of the most impactful use cases include:

• Enhanced decision making: AI for insurance tools can assist human agents and underwriters with AI-powered insights to speed up complex decision-making like claims.
• Elevating customer experiences: AI virtual agents can offer 24/7 self-service for FAQs and routine requests, so policyholders get the answers they need, when they need them.
• Process automation: AI can automate repetitive and manual tasks like data entry, claims processing, and document classification, so processes move faster and teams work more efficiently.
• Advanced data analysis: AI tools can quickly identify risk patterns and detect fraud, reducing the risk of error and delays.
• Policy and knowledge management: AI can help surface policy details, organize company documents, and keep information up-to-date so teams can make faster decisions.

The current state of AI in the insurance industry

By now, most insurers know they need AI. What they might be wondering instead is, “How do I use AI in insurance safely and effectively while getting the most ROI?”

A 2023 KPMG study found that 58% of insurance CEOs are confident their organizations will see a return on AI investments within the next 3-5 years. There’s lots of opportunity for AI use cases in the industry, and insurance companies know that the tool offers a great way to innovate.

However, the same report highlights that regulatory complexity and ethical concerns remain top barriers to widespread AI adoption for insurance leaders. 

To stay compliant with evolving data privacy laws, such as GDPR and HIPAA, as well as reduce bias mitigation in decision-making processes, insurance teams must ensure they choose the right tool from the get-go. 

How AI is changing core capabilities within insurance

Underwriting

Underwriting is a complex, time-consuming process, but AI can expedite it. By analyzing historical claims, common risk factors, and third-party data like credit scores or satellite imagery, AI can help insurance companies make faster, more accurate underwriting decisions.

Claims processing

Another time-consuming part of the insurance lifecycle, claims processing can benefit in multiple ways from AI. Here’s how one company is doing it: Lemonade uses AI to triage claims and significantly speed up the process for both policyholders and their team, while reducing fraud risk.

Their AI analyzes each claim for fraud patterns and risk factors. If the AI doesn’t note any red flags, it may instantly approve the claim, handling about 40% of claims instantly. If not, Lemonade’s AI escalates the claim to the team to handle. 

Lemonade’s application of AI empowers them to offer more prompt service while deflecting routine or lower-level claims from their team.

Risk assessment

Risk is an essential part of insurance. Machine learning models can make it easier and faster to evaluate risk by analyzing patterns in vast, unstructured datasets. They’re also more accurate, able to spot fraud and risk in huge amounts of data that human teams might miss.

Customer experience

Insurance companies have vast numbers of policyholders to support. Often, those policyholders ask the same questions over and over or need quick assistance with submitting claims. And much of the time, when people need to contact their insurance company, it’s outside of business hours.

AI agents can help by offering instantly accessible, 24/7 support for everything from answering FAQs, resetting account passwords, or even submitting claims. Over chat, SMS, voice, email, and more, AI agents like those from Capacity make it easier to support customers at scale, even deliver personalized recommendations in the process.

Internal operations

Insurance teams deal with a lot of documentation and information, from various regulations to complex policies, company information and training materials, and more. 

Companies can make it easier for their teams to access correct and up-to-date information with AI tools like internal chatbots and knowledge bases. An internal chatbot like Capacity can become the go-to for teams looking for quick information, while tools like the Answer Engine® can surface relevant documents (complete with page numbers) from a simple prompt.

Outbound campaigns

Even insurance companies need to market themselves. Insurance giant Progressive wanted to find a better way to boost campaign performance and bring in new customers—so they turned to generative AI.

Using AI to create distinct audio voices, Progressive tested campaign effectiveness across 6 million impressions. This allowed them to determine which would be most effective, even with different audiences.

As a result, Progressive boosted conversions from their ad campaigns by a whopping 52%.

In this case, AI was able to raise brand awareness while maximizing the impact of their team’s effort.

Best practices for implementing AI in your insurance company

To succeed with AI in insurance tools, it’s necessary to have realistic and achievable goals as well as a defined implementation and adoption strategy. Here’s how to get started:

1. Align AI initiatives with strategic business goals

Why it matters: Too many AI projects fail due to poor alignment with broader company objectives.

Best practice: Set clear, measurable goals that can easily scale into larger initiatives, such as lowering the claims cycle time or achieving higher customer satisfaction. Ensure that every team involved, like compliance, operations, and customer support, is aligned on these goals and how the tool will be used.

Example: Tie your AI initiative to specific support KPIs, like handle time or deflection rate, so you can prove ROI. AI voice agents that handle tier-one inquiries are a great way to get started with this.

2. Prioritize use cases with immediate ROI

Why it matters: Insurers are risk-averse, and every big investment should come with short-term and long-term results. 

Best practice: Start with high-volume, low-risk implementations, like automating FNOL intake or claim status updates. These use cases have tangible benefits that are easy to see, and they can win stakeholder buy-in as you work to scale your AI strategy.

Example: SMS agents can reach customers instantly, where they are, and alert them on the status of their claim automatically. This improves customer experiences while reducing work for human teams.

3. Build a unified knowledge layer

Why it matters: Scattered data, outdated knowledge, and inaccurate information slow productivity and raise the risk of error. 

Best practice: Centralize organizational knowledge in a single, AI-accessible layer that connects to the systems your team uses. That way, both AI agents and human teams can pull from the same source of truth, creating more consistent experiences while reducing delays.

Example: Capacity’s knowledge base powers every other part of its support platform, from outward-facing AI agents to internal team support, connecting every support activity to the same information—and learning and updating knowledge the more it’s used.

4. Design for human & AI collaboration, not replacement

Why it matters: AI isn’t here to replace jobs. Rather, AI in insurance can enhance and streamline work, making it easier for human teams to focus on projects that feel impactful rather than spend time on repetitive tasks.

Best practice: Develop training and adoption incentives so that teams can see the benefits of using new AI tools, especially on their own productivity and workload. Keep humans in the loop so that teams can participate in AI processes while working more efficiently.

Example: One major insurer automated a single call flow with AI and recouped their entire tech investment for the year—while reallocating agents to more strategic, revenue-driving roles.

5. Ensure compliance from day one

Why it matters: Insurance companies deal with a lot of red tape and guidelines. The AI tools they use must meet strict regulatory and ethical standards to avoid any legal issues.

Best practice: Choose platforms with credentials like HIPAA, SOC 2 Type II, and GDPR compliance. Engage legal and compliance teams throughout the implementation process to ensure that all AI-driven decisions are transparent, auditable, and bias-aware.

6. Start small, scale fast

Why it matters: It’s best to prove ROI and value (and iron out any kinks) before expanding AI use cases across the insurance process. Laying a strong foundation with tangible results makes it easier to scale initiatives. 

Best practice: Pilot an AI tool within one single department at first, like customer support. Document wins, challenges, and how to overcome them. Then expand use cases within the same department (depending on your goals), or to other departments such as claims, underwriting, or risk management.

Want to learn more about AI in insurance?

The insurance industry stands at a crossroads. AI is already changing how insurers work, but implementing it correctly can be a challenge. 

The most successful adopters of AI in insurance will be the ones who:

• Start with clear, ROI-driven goals
• Prioritize low-risk, high-reward use cases
• Design for collaboration, not disruption
• Include compliance considerations from day one

With insurance automation solutions like Capacity, insurers can future-proof their business while lowering costs and risks, building brand awareness and improving customer satisfaction, and empowering their teams to make faster, more strategic decisions.

Book a demo today to learn how Capacity can help!

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