Artificial intelligence is going to disrupt insurance brokerage. There is not much question about that. The question that matters is what gets disrupted, and how differently firms are preparing for it.
Most firms are approaching AI the way they approached every prior wave of technology: take the work as it exists and do it faster. But AI is dramatically changing the economics of gathering information, comparing coverage and facilitating transactions. A firm whose value lives in those activities is competing in a race to the bottom.
A firm built on specialized expertise, deep client relationships and experiential human judgment can change the paradigm, turning risk into opportunity for clients. Firms that make that leap go from being a vendor of downside protection to a business partner that helps a client achieve the fullness of their ambition. Their value becomes constrained only by what their clients can achieve.
Showing up that way requires the fortitude to completely rebuild how the work gets done. At Baldwin, alongside our partners at Dell Family Office and Sequence Holdings, we have launched into that journey. We call it Refounding.
The work has to change, not just the tools
Consider a middle-market renewal: gather exposure data, prepare the submission, market it, compare quotes, build the proposal, and present. Weeks of cycle time, most of it waiting on the next hand-off. Apply AI to each step and you compress each one a little. Redesign the work around what AI does well and you compress the entire cycle, and you hand the recovered time back to the client advisor for the only parts a client will pay a premium for: understanding the business, anticipating what could go wrong and structuring solutions that unlock potential.
That is the work underway through Catalyst, our firm-wide transformation program. Over 17 weeks, we completed more than 47,000 AI-assisted tasks at quality rates above 98 percent while standardizing 27 processes in our Client Experience teams. Those numbers matter only for what they free up: capacity redeployed into advisory work, not simply banked as cost savings.
Three things happen when you make that shift. Cycle times compress meaningfully. Throughput rises because the same team carries more complexity. And complex knowledge work gets sharper. In one early claims-review application, AI identified coverage gaps in minutes that led to $71,000 in additional claim payment for a client. A professional still had to evaluate what the technology surfaced and decide what to do. That is the model: technology ingests and processes; the human provides the judgment; and experienced professionals remain accountable for every decision that carries real financial consequences for a client.
Judgment and relationships become more valuable
AI drives the marginal cost of information toward zero, and when information is free, the premium shifts to what you do with it. Clients will have more data than ever. What they will not have is someone who has seen 200 versions of their problem, who knows which carrier will actually show up when a claim is contested, and who will tell them the uncomfortable truth about a coverage gap before the loss rather than after. That is judgment, earned through experience. The relationship that lets a client hear that perspective and act on it with conviction is earned the same way.
It is also why insurance advice has to move earlier in the conversation. Too often, insurance enters the discussion after the consequential decisions about risk have been made. With AI compressing the mechanics, the best advisors will move upstream into the room where those decisions happen, helping clients understand how risk affects their investments, their capital and their growth. That is where the advisor’s value was always supposed to live. The transactional work got in the way.
Which means we have to rebuild how we develop talent
For generations, early-career insurance professionals built judgment by doing the foundational work: reviewing policies, preparing submissions and sitting beside experienced colleagues. That apprenticeship was never efficient, but it worked. AI will eliminate the tasks it is built on. Remove those tasks and do nothing else, and we will produce a generation of advisors who can operate the tools but cannot challenge them. Judgment comes from working through hard situations, seeing the consequences of decisions and learning from people who have been there before. We have to redesign the apprenticeship for a world where the old on-ramp no longer exists.
At Baldwin, we are transforming how we work, and early-career colleagues are among the first to benefit. They now encounter real client challenges sooner than the traditional path allowed. We have begun retooling our internal university, The Azimuth Institute, using AI as a training environment where a new advisor can pressure-test a coverage program, or ask why a senior colleague structured a placement a certain way, and get a substantive answer in seconds. Because AI absorbs time-consuming tasks, these colleagues are now beginning to learn higher-level insurance concepts earlier and spend more time on client relationships and complex coverage questions. We are also committed to more structured mentorship, not less, because experienced advisors are best placed to teach how to rigorously interrogate what the technology produces. And we are rethinking performance: when a day’s work takes an hour, the question is no longer how fast a task was completed, but how well the day was spent on problem-solving and critical thinking for client solutions.
The next generation of advisors will decide this
Five years from now, the role of the insurance advisor will look substantially different, and I expect the advisors who meet that moment to be more valuable than any generation before them, because what they bring is the one thing the technology cannot: experience, judgment and a relationship the client trusts.
The firms that win will not be the ones that focus on AI purely to drive efficiencies. They will be the ones that changed the work, then built the people who could do the new work well. That is the work in front of us, and we intend to get it done.
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