5 Areas to Evaluate in the Agency Before Assessing an AI Vendor

Conference season is only half over. The larger technology conferences offered some version of what brokers need from AI and automation going forward. Most agency leaders will come home with a list, and a misunderstanding, of which platform will deliver value. However, the trouble with these events is that the sessions are not about any particular agency, so a product that sounds genuinely useful from a conference stage may end up being a poor fit for a specific operation.

I will say something here about my own side of the industry. Technology companies serving distribution tend to be better at describing what a product does than at explaining what it changes for an agency, and those are different claims. The value a tool delivers is often different from the value it is marketed on, and the agency is usually the party that discovers it.

After years of leading technology implementations inside agencies, I would argue that the most useful diligence happens internally, before anyone signs anything. The only cost is time–roughly a week if it is done properly. It tells a leadership team more about the likely return than another round of demos, or than a bet on a solution with no real value in their ecosystem.

Here are five areas worth examining–and none of them involve the software.

1. Start with a single task performed in two locations. Pull a recent renewal handled by one office and the same type handled by another, then compare the client files side by side. Whatever gap appears is the gap that any new system will meet on day one.
2. Ask who gets called when a process breaks, meaning the person who owns the sequence end to end and can change it. If that name takes more than a moment to produce, the agency has found the condition most likely to stall an implementation.
3. Trace where client information lands between the first conversation and the bound policy, and count the stops. Most agencies are surprised by the number, because each one solved a real problem at the time. The stops outside the system of record are the ones a tool will be unable to reach.
4. Look at what a new hire receives on their first day. A written process, a checklist, and a definition of a complete client file suggest an operation that can absorb change. A week of shadowing a colleague suggests the agency runs on individual knowledge, which is harder to automate and harder to scale.
5. Review the last significant system change the agency made, looking at how long adoption took, what was quietly abandoned, and which people went back to doing things their own way. That history is the best predictor of how the next change will go, and most agencies already have it.

This work can be handled internally by a committee of stakeholders rather than a consultant, and it asks for only a few hours and a willingness to accept the answers. Leaders occasionally discover that a process they described confidently in a vendor meeting works three different ways across their offices–a truth better learned before a contract than during a rollout.

What a leadership team does with the answers matters more than the answers themselves. Ownership should be settled before a contract is signed, because a tool cannot resolve an accountability question. The rest can be addressed alongside an implementation, as long as they are named in the project plan rather than discovered halfway through.

None of this is an argument for moving slowly. The agencies furthest along are pulling ahead, and the gap is widening, which makes the instinct to buy quickly entirely rational. The ones capturing the most value are moving quickly in the right order, and they spent a week on themselves before spending a year on a platform.

Topics InsurTech Data Driven Artificial Intelligence