On the First Day: Understanding How AI Fits Into Your Routine
On the first day at a commercial insurance retailer, an analyst’s main goal is to understand how the business runs. Insurance may look complicated, but the work is built around a clear set of activities: bringing in clients, preparing submissions, reviewing risk, and managing renewals. Day one is about watching how those activities actually happen inside the office.
The first step is to meet the people who do the work every day. Brokers are responsible for finding and serving clients. They put together submissions, negotiate with carriers, and make sure clients get the coverage they need. Underwriters review those submissions, looking at applications, financials, and claims histories to decide if the carrier should offer coverage. Account managers keep everything running smoothly once the policy is in place—handling renewals, endorsements, and client requests.
For an analyst, sitting with each of these groups on day one gives a clear picture of how the pieces fit together. Watching a broker prepare a quote shows what clients care about most. Seeing an underwriter review a loss history explains how risk is measured. Observing account managers track deadlines highlights the importance of staying organized.
Commercial insurance depends on documents and data, and much of it is messy. Applications often arrive incomplete, policies are long and complex, and claims histories can be hundreds of pages. On the first day, the analyst should pay attention to how the team deals with this reality. Do they re-type data into multiple systems? Do they compare policies manually? Do they spend hours looking for missing details? These are everyday tasks, and understanding them is the analyst’s first job.
This is also where AI quietly comes in. Insurance work involves a lot of repetition, and AI is good at handling repetitive tasks. For example, instead of reading through two long policies line by line, AI can highlight the differences automatically. When a loss history is dozens of pages long, AI can summarize it quickly and call out unusual claims. When applications arrive with missing fields, AI can flag them before they slow down the process. Even client communication—like writing renewal reminders or explaining coverage changes—can be sped up with AI tools that generate clear drafts.
The point on day one is not to change the process but to see where these tools already fit, or where they might fit in the future. By noticing where people spend the most time, the analyst starts to understand the parts of the job that could be made easier. A broker flipping through binders, an underwriter copying numbers into a spreadsheet, or an account manager scrolling through past emails are all signs of work that could be supported by technology.
Building trust with the team is just as important. Brokers, underwriters, and account managers have years of experience, and they know the clients well. They want to see that the analyst is there to learn, not to disrupt. Asking simple questions like “What usually takes the longest?” or “Where do errors tend to happen?” shows curiosity without overstepping. These conversations will later give the analyst the best insight into where AI can actually make a difference.
By the end of the first day, the analyst should have a basic understanding of how information flows through the business—how it comes in from clients, how it’s reviewed by the team, and how it goes back out to carriers and customers. They should also have seen the spots where work slows down, where details are easy to miss, and where technology already helps.
Commercial insurance is full of detail, and much of it depends on paperwork. AI is built to handle that kind of work. By paying attention to both the human side and the data side, an analyst can quickly see how the job works and how their skills will add value in the weeks ahead.
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