AI touches virtually every facet of life today—how we learn, work, shop, socialize, travel, and more. AI is also transforming insurance by intelligently reducing friction across all operational areas to help people, businesses, and communities be more secure and better prepared against losses.
Like any new, game-changing technology, there is a lot of misunderstanding around AI. For one thing, it’s not new. Some of the underlying technology and techniques have been around for at least a half-century!
To get you up to speed, here are seven debunked insurance AI myths and misperceptions to avoid.
Using AI lets machines do the work machines are good at, so humans can handle specialized or complex tasks we previously wouldn’t have had the bandwidth to address. AI now handles routine policy endorsements, freeing underwriters to focus on complex risk assessment and relationship building with brokers.
AI also makes quick work of First Notice of Loss/First Report of Injury (FNOL/FROI) and many policy servicing tasks, such as sending Certificates of Insurance (COI), enabling faster claim processing and improved customer experiences.
In the near future, as insurance’s talent shortage is expected to become more acute, agentic AI solutions can help bridge this growing resource gap by automating entire processes.
Building and managing AI systems and infrastructure internally can be prohibitively expensive. Third-party AI solutions built with insurance expertise (also called Vertical AI) offer a pathway for significantly reducing the time and expense of bringing AI into production.
Once in place, AI is proven to drive substantial time and resource savings through increased processing speed and accuracy. Not making the leap to AI can put your company at a significant competitive disadvantage.
This starts with building a culture of transparency in your business, with clearly defined resources to inform readers about AI's role in your processes and the solution's design/data sources.
Whether building solutions or working with an outside partner, establish explainability (of AI model decisioning) and traceability (documenting data used, algorithms employed, and decisions made) as the bedrock of your AI solutions. Creating an internal AI governance committee and operating principles can provide another platform for aligning AI compliance and security practices with your customer’s best interests.
Implementing AI can reduce compliance risk by improving accuracy and consistency (over work performed manually) and automating compliance-related processes, including continuous risk monitoring.
Partnering with insurance AI vendors that share your high standards for trust and security—and clearly state their policies regarding regulatory compliance—can significantly streamline AI compliance.
Many successful AI use cases involve data extraction and analysis from applications, demand reports, and other complex insurance documents. What drives ROI in these use cases is how AI increased efficiency and capacity let experts make more accurate policy pricing and claim settlement decisions, even in use cases where straight-through document processing can reach 99%.
Human-in-the-loop (HITL) is another way AI offers a collaborative approach to creating more accurate, reliable, and adaptable AI systems. Some HITL systems allow AI to interact with human experts in real time to identify and manage exceptions caused by missing data, poor document readability, and other common problems.
Working with the right solutions provider to assist your teams (including your AI governance leaders) in charting a path to successful implementation can make the difference between projects that enter production smoothly and ones that get bogged down in proof-of-concept (POC) phases.
Setting priorities by aligning AI to financial and customer-centric objectives will improve underwriting accuracy and significantly reduce claims overpayments while increasing operational efficiency. But these gains don’t happen overnight.
Insurance customers interacting with AI are already more satisfied with their insurance experience.
AI is also helping insurance operations set new standards for service by delivering faster, more accurate quotes and settling claims more efficiently. Today, some AI-powered carriers are even settling claims within seconds of a reported loss. Other areas where AI improves insurance service and satisfaction include managing correspondence with brokers/agents and automating insurance document generation.
Leading insurance businesses using AI today are realizing substantial gains in productivity, team member retention, and customer satisfaction. These include 80% straight-through processing of underwriting submissions, reducing claims document processing time from days to minutes, faster policy document generation, and elevated customer satisfaction.
Advantages like these build over time, making recovery from every month of delay increasingly costly. Rather than waiting for perfect conditions, competitive insurers are seeing success through adopting phased implementation strategies that balance immediate market needs with practical operational realities.
The most successful implementations of insurance AI treat this technology as a powerful tool to enhance human capabilities, improve customer experiences, and drive operational efficiencies.
As you evaluate AI for your insurance operations, separate the hype from reality. The technology isn't magic—but when properly implemented, the results can look like magic compared to traditional processes.
Read our 2025 State of AI Adoption in Insurance Report for insights and perspectives on AI adoption from more than 240 insurance executives.