Sean G. Eldridge is the Co-founder and CEO of Gain Life, a venture-backed insurance technology company that helps individuals and organizations return to health, work, productivity, and financial wellbeing. Prior to Gain Life, Sean led a private equity-backed roll-up in the disaster restoration space and held leadership roles at Johnson & Johnson, Procter & Gamble, and Weight Watchers, launching new products and services that leverage the power of behavioral science and technology. Sean earned his B.S. in Management Information Systems from Rochester Institute of Technology and MBA from Harvard Business School. He resides in Cambridge, MA with his wife, son, and corgi.

Imagine a 1950s insurance company executive marveling at his firm’s first mainframe computer that took up an entire room.

“We’ve reached our automation peak,” he might have proclaimed. To which a colleague would have said, “Waste of space. It’ll never save us time.”

Automation has long been a point of contention in the insurance world. In the early days of computing, driven by data-heavy operations and a need for consistent processing, the insurance industry was an early adopter of mainframe computers to handle basic policy administration and claims recording.

Over the following decades, insurance companies gradually expanded their automation capabilities. They implemented rule-based systems to manage simple claims and policy applications, developed online portals for customer self-service, and adopted robotic process automation to tackle repetitive tasks across multiple systems.

However, these automation approaches share one severe limitation: their rigid nature. These processes can only handle scenarios that are explicitly programmed and break whenever underlying systems change. Due to these shortcomings, automation often does not work as intended, creating additional complexity and resulting in substantial manual work to keep operations running smoothly.

Such limitations are always short-lived and are being addressed through breakthroughs in artificial intelligence.

Early AI systems were limited to recognizing patterns or generating content, and lacked the reliability needed for autonomous decision making. Innovation has created systems reliable enough to make complex decisions independently.

This evolution has led to the emergence of next-gen AI agents. AI agents are automated assistants that can independently perform tasks and make decisions to achieve specific goals. While previous types of rule-based automation relied on predefined steps and scenarios, AI agents leverage intelligence to understand context, learn from experience, and make nuanced decisions based on different inputs.

As a result, rather than programming every possible scenario in advance, AI agents can adapt and respond to new situations as they arise.

Consider a customer reporting water damage in their home. A rule-based system programmed to process “water damage from rain” claims might not recognize a claim reporting “leaks after storms.” The discrepancy would require manual intervention — potentially delaying the process. In contrast, an AI agent can navigate and understand the intent behind the customer’s report, regardless of the specific wording used.

Moreover, the AI agent can go beyond simply processing the claim by automatically coordinating multiple processes, such as scheduling emergency repairs, initiating loss prevention measures, and even adjusting the customer’s risk profile.

This ability to orchestrate a complex response sets AI agents apart from traditional automation, where a similar result could only be achieved by programming thousands of different scenarios.

While AI agents offer compelling advantages over traditional automation, their implementation does require careful integration into existing processes. This means establishing clear boundaries for decision making and appropriate oversight.

These boundaries can range from requiring human validation for every decision to allowing fully autonomous operation, depending on the complexity and risk of different tasks. Most practical implementations will fall somewhere between these extremes, balancing efficiency with appropriate oversight.

For instance, in the water damage example, the AI agent could be configured to automatically schedule emergency repairs and initiate loss prevention measures for damages under a certain amount. If the claim suggests potential structural issues or exceeds preset financial thresholds, the agent would then escalate the case for human review while still coordinating immediate emergency response.

Rule-based systems and predefined workflows have long been used to automate insurance processes, but with clear limitations in handling changing circumstances and complex scenarios. In contrast, automation through AI agents offers a more flexible and capable approach.

While careful implementation is essential, AI agents mark a significant evolution in how insurers can use technology to automate their operations. &