Vincent Chen is founder and CEO of Panta, where he builds AI-native systems that run a commercial insurance brokerage.
Everyone says they use AI now. Far fewer are actually AI-native, and the line between the two is sharper than the marketing suggests. It comes down to a single question: Does your company close the loop?
Let me explain what I mean with the business I know best. I run an insurance brokerage, and a single hard placement in our operation can mean roughly 240 emails, 16 people, 340 pages of documents, five carriers and four rejections—for maybe $8,000 in commission on $60,000 in premium. One client once took around 500 back-and-forth emails with underwriters before the coverage was bound. When I started, I assumed the pain lived in the steps: Fill this form, chase that document, compare those quotes. I was wrong. What makes it brutal isn’t any single step. It’s that one human has to hold the entire case in their head and personally carry every handoff. The job was never the steps. It’s the movement between them.
That distinction is why most software aimed at work like this quietly fails. The software built for it is a system of record—a CRM, a place a human goes to look things up and then does the work themselves. That is the wrong shape. What you actually want is a system of action: The judgment lives in the system, and the system decides and executes and only pulls a human in when it’s genuinely unsure.
The reason this matters is that ours is roughly a 100-step process. If you automate one step out of 100, nothing changes. Automate five, still nothing. The cost was never in any one step; it was in holding scattered context together. You wake up to 100 emails from 70 different clients, and a single quote needs the first, the 10th and the 90th. Even if the quote generation itself is automated, a human still spends a day or two reassembling the picture—and things slip through. The limit on how many accounts one person can carry is really a limit on how much state one brain can hold. So, selling more software to that human doesn’t fix it. You have to change who holds the state.
A System Of Action In 5 Moves
Capture Everything
From day one, capture all the trajectories: every call, email and document. This sounds trivial but isn’t. If a decision or a counterparty’s answer never gets recorded, it didn’t happen as far as your systems are concerned. You cannot act on or learn from the context you threw away.
Route It Into A Company Brain, Not A Filing Cabinet
Skip the CRM as your center of gravity. Instead, every decision a senior person makes becomes a rule the system keeps. A company brain that holds the live state of every account and the reasoning behind every past call. A CRM asks a human to remember and look things up; a company brain maintains the state continuously so nobody has to rediscover it with every email.
Let The System Drive The Process
In our setup, the system opens the tickets, drafts the follow-ups, chases the missing document, decides the next action and puts it on the right person’s desk at the right moment. The mental model I use: People become stations on the line, and the system is the line. That is the actual inversion: humans move from running the workflow to sitting on top of it.
Keep Humans On The Judgment
This is where honesty matters more than hype. A meaningful share of the work can already run without a person in the loop—but the genuinely consequential decisions, like binding someone’s coverage, still go through a human on purpose. The bottleneck there is not model capability; the technology is ready. What gates you is trust and evaluation data: Do you have enough evidence to let the system decide a given case? Where you don’t, you deliberately keep a person on it.
Feed Corrections Back So The Loop Closes
This is the move almost everyone skips, and it’s the whole point. When a person flags something the system got wrong, capture the correction as a permanent, labeled rule—not a chat message that evaporates—and recheck it against past cases. Do that consistently and the same class of mistake stops recurring; reliability compounds instead of resetting with every new hire. The next, less-experienced person never has to re-solve a problem a senior person already solved.
None of this is really about insurance. It’s about whether your organization’s expertise accumulates or evaporates.
So, here’s the one-question diagnostic for any leader claiming to be AI-native. The next time someone on your team fixes a mistake the AI made, ask where that fix goes. If it lives only in their head, you have a system of record with AI bolted on the side. If the system inherits it forever, you’re closing the loop. That, not the model you licensed, is what actually makes a company AI-native.
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