‍With new AI models and platforms emerging at a relentless pace, insurance organizations are redefining what it means to stay ahead.

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Artificial intelligence is evolving so quickly that keeping up no longer means evaluating a single platform or implementing a new tool every few years. For brokers and carriers, the challenge is navigating a growing ecosystem of AI solutions while determining which innovations will create meaningful value — and which are simply adding to the noise.

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The industry has largely moved beyond the question of whether AI matters. Today, the more pressing question is how organizations can make thoughtful decisions in an environment where new capabilities seem to emerge almost daily. For many insurance leaders, success is no longer measured by how many AI tools they have access to — it's measured by how effectively they can connect technology to business outcomes.

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"I think most brokers and carriers would say they're keeping up, and in many cases they are, at least relative to their own goals and constraints," says Chris Murphy, Vice President, Innovative Technology at Holmes Murphy. "There's a clear belief that AI will matter, and you see a lot of activity across the industry. But it's still early, and there isn't a shared definition yet of what 'leading' really looks like."

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That lack of a clear benchmark is creating an interesting moment for the industry. Organizations are investing, experimenting, and learning, but many are still determining what successful AI adoption actually looks like in practice. Meanwhile, the pace of innovation shows no signs of slowing down.

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Progress Is Real. So Is Fragmentation.

If there is one theme that consistently surfaces in conversations across the insurance industry, it's that AI is simultaneously delivering results and creating complexity.

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"It's really both," Murphy says. "You can see real progress in areas like drafting, summarization, and pulling insights out of data. Those are tangible and people are using them."

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But that progress is happening across an increasingly crowded landscape of platforms and solutions.

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"Different teams are solving similar problems in different ways, and that creates duplication and inconsistency," Murphy says. "The progress is real, but until it starts to feel more seamless, the fragmentation is what people notice most."

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Heather Wargo, Vice President and Chief Information Officer at Marshall+Sterling, sees a similar trend. "The progress is real,” she says. “We have been able to bring in tools that are already helping with efficiency, productivity, and the way our teams manage their work."

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At the same time, she notes that more options do not necessarily make decisions easier.

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"There are several solutions that seem to offer similar things, and that can slow down the decision-making process," Wargo says. "It is not always easy to know which tool is the best fit, which one will truly integrate into the business, and which one will actually be adopted by the teams using it."

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The result is a paradox many organizations are experiencing firsthand: AI has never been more accessible, yet determining where and how to invest has never felt more complicated.

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The Workflow Question

When discussing AI challenges, it's easy to focus on technology selection. Both Murphy and Wargo point to something deeper. The organizations gaining the most traction are not necessarily the ones evaluating the most tools. They're the ones taking a hard look at how work gets done.

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"The biggest challenge isn't choosing a tool," Murphy says. "It's stepping back and redesigning the work itself. A lot of organizations are applying AI to existing processes when the bigger opportunity is rethinking those processes altogether."

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That distinction is becoming increasingly important. Early AI adoption often focuses on automating individual tasks. The next phase may require organizations to rethink entire workflows. Wargo agrees that the starting point should be the business problem, not the technology.

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"I think organizations get tripped up when they start with the tool instead of the business problem," she says. "Before choosing a solution, organizations need to ask some basic questions. What are we trying to improve? Who will use this? How does it fit into the current workflow?"

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Her perspective reflects the realities of insurance operations, where complexity often lives beneath the surface.

"I know the work behind the desk is not always as simple as it may look from the outside," Wargo says. "There are details, follow-ups, coverage questions, documentation needs, timing issues, and judgment calls."

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In other words, successful AI adoption isn't about replacing insurance workflows. It's about understanding them well enough to improve them.

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Cutting Through the Noise

With so many platforms, vendors, and opinions competing for attention, insurance leaders are increasingly looking for trusted sources of insight and perspective. BrokerTech Ventures provides a platform for consensus around these complicated tools. 

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"I think BTV is already playing an important role in bringing clarity to a space that can feel pretty noisy," Murphy says. "There's a lot of innovation happening, but it's often hard to tell what actually matters versus what is just interesting."

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One of BTV's strengths is its ability to bring together brokers, carriers, and insurtech leaders who are navigating many of the same challenges from different perspectives. For Wargo, that peer-to-peer exchange has become especially valuable.

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"BTV creates a space for real conversations, and that is very important right now," she says. "What is most helpful is being able to sit with other agency leaders and talk about what is actually happening inside their organizations."

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Those conversations often reveal something that product demonstrations and headlines cannot: what is actually working in practice.

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"You can hear what is working, what is not working, what people would do differently, and where they are seeing real value," Wargo says. "That kind of insight helps cut through the noise."

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As AI continues to evolve, that collective learning may become one of the industry's greatest advantages.

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The Human Advantage

For all the discussion around platforms, models, and automation, both leaders ultimately point to the same differentiator: people. Murphy believes one of the industry's biggest blind spots is the role of critical thinking.

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"A lot of the conversation focuses on the capability of the technology, but the real constraint is often the person directing it," he says. "AI is only as effective as the questions being asked, the context being provided, and the judgment applied to the output."

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As AI becomes more embedded in daily work, he sees a broader shift taking place.

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"The organizations that get the most out of this won't be the ones with access to the best tools. They'll be the ones with people who know how to think with them, challenge them, and use them intentionally."

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Wargo arrives at a similar conclusion from a different angle.

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"We are not talking enough about the people side of AI adoption," she says. "The success of AI will depend heavily on the judgment and experience of the people using it."

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After more than two decades in the insurance industry, she believes expertise remains irreplaceable.

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"AI can absolutely help us work faster and smarter, but it should support human expertise, not replace it," Wargo says. "I feel strongly that the real differentiator will be the expertise behind the AI."

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That may be the most important takeaway for brokers, carriers, and insurtech leaders alike. As AI platforms continue to multiply and capabilities continue to evolve, the competitive advantage may not come from adopting every new tool that enters the market. It may come from something far more fundamental: understanding the problem you're trying to solve, redesigning work where it makes sense, and empowering people to use technology with purpose.

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