Over the past year, artificial intelligence has quietly moved from experimentation to everyday business operations. Companies have formalized their AI use for everything from drafting client communications, automating workflows, analyzing financial data, supporting hiring decisions, and accelerating customer service. In addition, companies may not even realize how deeply AI is embedded in the software they use every day.
At the same time, insurance carriers are rethinking how those risks are covered. Policy language around AI exposure is evolving quickly. New exclusions are emerging. Specialty products are entering the market. And many businesses are operating in a gray area where AI-related risks may touch multiple lines of coverage at once.
For independent agencies, this creates both a challenge and meaningful opportunity. Clients don’t necessarily need their agent to sell them AI-related policies. What they do need is someone who can recognize where change is happening, ask the right questions, and help them navigate a fast-moving risk environment so they find the right coverage.
The AI coverage conversation is shifting—quietly, but quickly
For years, AI exposure lived in a gray area. It existed inside policies, but it wasn’t clearly defined. Coverage often depended on how a claim was framed and which policy responded first.
Now, carriers are moving to make AI treatment more explicit, particularly in general liability and professional liability lines. For example, Verisk has filed updates to standard GL forms to address generative AI exposures, with changes expected to take hold across upcoming policy cycles.
At the same time, new forms of coverage are emerging. Some carriers and specialty providers are introducing affirmative AI endorsements designed to address risks like model failure or AI-enabled fraud. Others are updating cyber coverage language to explicitly include AI-driven events such as deepfake-enabled fraud.
For business clients, this might feel familiar. It mirrors the early evolution of cyber insurance: initial ambiguity, followed by exclusions, eventually leading to dedicated products. The difference is speed. AI is moving through that cycle much faster.
AI risk doesn’t always show up where you expect it to
One of the reasons this shift matters is that AI exposure doesn’t live in a single place inside a client’s business. It’s embedded in numerous everyday tools and workflows.
A manufacturer uses AI to automate customer service responses. A contractor relies on software that uses AI to estimate project costs. A professional services firm uses AI to draft client deliverables. In each case, AI is influencing decisions, communications, or financial outcomes—but in very different ways.
In practice, a single AI-related incident could touch multiple lines of coverage—or fall between them. For example, if an AI-powered customer service tool provides inaccurate guidance to a client, the resulting fallout could raise questions around cyber coverage, E&O, and broader liability exposure all at once, depending on how the policies are written.
That’s why WTW (formerly Willis Towers Watson) has noted that no single policy is designed to cover the full spectrum of AI-related risk. And it aligns with guidance from organizations like the National Association of Insurance Commissioners (NAIC), which continue to emphasize that traditional commercial policies often exclude or limit cyber-related losses. The complexity is in understanding where coverage begins, where it ends, and how quickly those boundaries are changing.
That complexity is exactly why agencies have an opportunity to provide more value.
The gap—and the opportunity—for agencies
According to research from McKinsey, the majority of organizations now report regular use of AI in at least one business function, and adoption continues to expand. That creates a potential coverage gap.
Most commercial lines agencies aren’t structured to track something like AI usage across their book of business. It’s not a standard intake field or routinely discussed at renewal. And unless there’s a known exposure, it may not come up at all. That made sense even a year or two ago. It doesn’t anymore.
Agencies that build fluency in how AI shows up in client operations are in a position to ask better questions and help clients identify emerging risks earlier. They can recognize when something has changed—and know when to take the next step.
The goal isn’t necessarily selling AI-specific policies but rather leaning into an agent’s superpower: the role of trusted advisor.
AI risk conversations with commercial clients are evolving
AI risk fluency doesn’t mean every agency needs to become a cyber specialist. It means being able to recognize when something has changed—and knowing when to take the next step.
Agencies that invest in staying current—whether through trusted insurance knowledge resources, stronger internal processes, or more consistent renewal conversations—are better positioned to deliver that kind of guidance. That’s what clients will remember.
Agencies need an intentional approach to client conversations around AI, especially at renewal. AI is already embedded in tools and workflows that clients may not think to mention, and it often shows up in places that don’t immediately feel like “risk.”
That means agencies need to lead client conversations to uncover potential risks. Are automated tools influencing customer communications? Are software platforms making recommendations that employees act on? Are financial transactions or approvals being triggered by systems that rely on AI?
These are business questions, not technical ones. They open the door to understanding where AI is shaping business outcomes, and where there may be coverage needs.
Keeping up with changes in AI coverage
Policy language is evolving. Carrier appetites are shifting. New exclusions and endorsements are being introduced, often with subtle but important differences. And unlike more established lines, there isn’t always a clear, standardized approach yet.
That makes access to current, reliable information more important than ever. Agencies have always relied on a mix of carrier relationships, internal expertise, and external resources to stay informed. What’s changing is the speed at which that information becomes outdated.
Guidance that was accurate at the last renewal may no longer reflect how a carrier is treating AI exposure today. A form change or endorsement update can materially alter how a claim would respond, even if the overall policy structure looks familiar.
In that environment, the goal isn’t to know everything. It’s to have confidence that the information you’re using is current, credible, and relevant to the situation in front of you.
That’s why many agencies are placing more emphasis on trusted insurance knowledge resources—tools that help interpret policy language, surface changes, and provide clarity in areas where coverage is still evolving.
The advantage of leaning in early
AI is still in the early stages of how it will ultimately be reflected in insurance coverage. Definitions will continue to evolve. Products will mature. Standardization will eventually follow.
But in the near term, there’s a window where knowledge and awareness create a meaningful advantage.
Agencies that take the time now to understand how AI shows up in their clients’ businesses—and how coverage is adapting in response—are better positioned to guide those clients through uncertainty.
That doesn’t require perfection. It requires curiosity, consistency, and access to the right information. And it reinforces something that has always been true about independent agents: their foundational value is in helping clients make sense of risk as it changes.
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