How Insurance Brokerages Are Applying AI: BrokerTech Connect 2026 Takeaways

BrokerTech Connect was filled with a dynamic, thought-provoking group of speakers, brokers, solution providers, and investors. The conversations were practical and forward-looking, with less emphasis on AI as an abstract concept and more emphasis on the questions brokerages are now facing as they determine how, where, and why to put it to use.

Across those conversations, a few questions came up again and again: Where is AI today? Where does it make sense to apply it? What are other brokerages actually doing with it?

Here are key takeaways from BrokerTech Connect: Chicago 2026.

Where is AI?

AI has moved from experimentation to implementation.

A clear theme was the shift from “should we use AI?” to “where can we put AI into production?” Many discussions were tied to specific brokerage workflows such as submission intake, policy-checking, quoting, renewals, COIs, document processing, commissions, and back-office work.

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Agentic AI is becoming practical, but workflow design matters.

The conversation is moving beyond copilots that simply answer questions toward AI agents that can execute multi-step processes. But technology alone is not enough. Brokerages need to redesign workflows, define where humans remain in the loop, and where AI belongs in the loop.

What should I do with AI?

Start with the low-value work around the broker.

Rekeying information, chasing documents, comparing quotes, preparing submissions, and handling routine service work are all opportunities to reduce repetitive activity around experienced insurance professionals, giving producers and account teams more time for judgment, advice, and client interaction.

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Use-case selection needs to be intentional.

Brokerages should be disciplined about where they apply AI; the most exciting idea is not always the best place to start. In many cases, a narrower use case with measurable value and manageable risk will produce better results and build confidence for broader adoption. The order in which use cases are implemented matters as well.

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ROI is becoming the standard, not the quality of the demo.

Brokers increasingly want tangible outcomes: reduced processing time, fewer manual touches, greater capacity, lower servicing costs, additional revenue, and the ability to grow without proportional increases in headcount. The practical question is becoming: what economic or client value does this create, and how quickly can we prove it?

How can AI make my brokers more effective?

AI should be viewed as a relationship-enhancing technology, not just an efficiency tool.

One of the biggest opportunities is to use AI to make brokers better at the parts of the job clients value most. If AI can reduce administrative burden, surface relevant information before client conversations, anticipate needs, identify changes in risk, and help a producer respond faster; it can strengthen the human relationship. The goal should be to give the broker more time and better information to be a trusted advisor. Misuse of AI can significantly weaken client relationships.

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Better service is a competitive advantage.

Information already generated through emails, calls, renewals, claims activity, and service interactions can reveal when a client may need attention before they have to ask for it. Used effectively, that information can shift service from simply responding to client needs to recognizing them earlier.

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The opportunity is expanding from efficiency to growth.

AI has shifted from a focus on cost reduction and simple automation to how it can help brokerages serve more clients with greater relevance and personalization. A client interaction may signal an unmet need, a coverage gap, a life or business event, a retention risk, or an opportunity to cross-sell or upsell. AI can help turn those breadcrumbs into proactive recommendations for the producer.

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Human judgment and relationships remain the broker’s differentiator.

The more routine work technology can handle, the more valuable human judgment, empathy, negotiation, placement strategy, and relationship-building become. The best use of AI is to make the broker more informed, more responsive, and more available to the client.

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Plan for adoption from the beginning.

Even a well-chosen AI use case will not create value if people do not adopt it or if it does not fit the way work actually gets done.  Future-state planning needs to account for change management, training, human oversight, and exception handling from the beginning, not after the technology has been selected. Users need to see and feel the value it creates quickly.

What are other brokers doing?

Large brokers are increasingly thinking at the operating-model level.

Technology decisions are becoming less about isolated applications and more about enterprise architecture, data strategy, integration, governance, and operating models. That is particularly important for acquisitive brokerages, where M&A creates additional complexity across books of business, billing, carrier relationships, data, and multiple AMS environments.

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AI can make fragmented brokerage data more usable.

The opportunity is no longer simply to store client information in the AMS. Valuable knowledge is spread across AMSs, CRMs, PDFs, email, spreadsheets, carrier portals, and other systems. AI creates the possibility of making that information usable across the enterprise, without necessarily replacing every underlying platform first.

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Brokerages are looking beyond efficiency as the measure of AI’s value.

Efficiency and cost reduction remain important, but brokerages are increasingly looking at a broader set of outcomes. Improving service, strengthening relationships, increasing retention, and helping producers recognize and act on revenue opportunities can all factor into the value that AI creates.

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Future state

The future brokerage will not be one in which AI replaces the broker. The most successful organizations will embed AI throughout the operating model to quietly support the broker at every stage of the client relationship. Routine work is increasingly automated. Relevant information is surfaced automatically. Changes in client circumstances, risk, service activity, and signals are identified when they occur. Producers are prompted to engage at the right time with the right issue, and clients receive faster, more proactive service.

In that future state, the competitive advantage will not come simply from having AI. Most firms will have access to similar technology. The advantage will come from how intelligently a brokerage applies it: selecting achievable use cases with measurable ROI, putting appropriate governance around them, integrating them into real workflows, and using them to make relationships stronger.

Bottom line

The first waves of AI within brokerages were about getting information into systems and automating tasks. The next opportunity is broader: use AI to create capacity, improve service, strengthen client relationships, and help producers identify and act on revenue opportunities.

It should also be: How can we use AI and the information we already have to make our people more effective, give clients a better experience, deepen relationships, identify needs earlier, and grow more intelligently, while choosing use cases that are economically sound, achievable, and responsible?

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If your brokerage is working through these same questions, FormativGroup can help you identify where to start and what makes sense for your organization. Start a conversation with our team.