Right now, a quant is sitting across from a VC: “Underwriters are slow, expensive, and inconsistent — and I have AI now.” He walks out with a term sheet. A few months later, another all-digital MGA will probably announce it was “built from the ground up on agentic AI.”
None of this is a forecast. It’s already happening. Over the past year InsurTech funding topped $1.6B, agentic-AI startup funding roughly doubled year over year to about $2.9B, and analysts expect autonomous underwriting adoption to climb from 14% to 70% by 2028. The wave is real. It’s accelerating.
The winners won’t be the teams with the cleverest model. They’ll be the teams whose model never sees a bad row of data.
Agentic means the system acts before you do
Traditional automation waits to be told what to do. Agentic underwriting acts the moment a submission arrives — it structures the incoming data, triages it against appetite, hunts down the information nobody sent, and assembles a pre-filled quote before a human ever opens the file. The promise is straight-through processing: a submission goes in, a priced risk comes out, and the underwriter reviews exceptions instead of typing spreadsheets.
It’s a better operating model. But it rests on an assumption the demos gloss over: the data going in is complete, correct, and machine-readable. In commercial property, it almost never is.
The bottleneck is the submission itself
A commercial property submission arrives as a Statement of Values (SOV) — often a broker’s Excel file with odd column names, merged cells, and a tab that says “do not use.” Each building needs a clean address, construction type, occupancy, year built, square footage, stories, roof, and protection details before any model can reason about the risk.
This is where agentic ambition meets physics. Feed an AI underwriter a mis-mapped SOV and it won’t fail loudly. It fails silently. It prices a wood-frame building as masonry, or drops a risk into the wrong wildfire zone. The agent was flawless. The input was garbage. The loss ratio pays for it eighteen months later.
Garbage in, confidently priced garbage out.
Every AI-native carrier hits the same two problems
We see the same sequence with every new digital underwriting team:
- Day one — extraction. You need structured data out of messy SOVs, ACORD forms, loss runs, and broker emails, in every format, at submission speed. Without it, your “straight-through” pipeline stalls while someone re-keys spreadsheets.
- Day two — enrichment. Extracted data is thin. To price well you append what the broker didn’t send: geocoding, CAT and flood zones, fire protection class, replacement-cost validation, building characteristics, and neighboring-risk context. That’s the 360-degree view that separates a smart quote from a guess.
Neither of these is your core IP. Your edge is your appetite, your pricing, your distribution, your orchestration. The data plumbing is table stakes — mandatory, hard to build well, and a waste of your first six engineering months.
Build the model. Rent the pipes.
The teams that reach first-policy-issued fastest treat extraction and enrichment as infrastructure to plug in, not a science project to staff. That’s the layer we built Ping to provide. We deliver clean, structured, enriched insurance data by API, so your agents reason over trustworthy inputs from the first submission.
Agentic underwriting will reshape this industry. Just remember: the agent is the easy part. The data underneath is the whole game.