I've spent the last three weeks in Bentonville, Arkansas, embedded in the Fuse Ventures supply chain accelerator. Bentonville is an unusual place. It doesn't look like a technology hub. It looks like a mid-sized American city that happens to sit at the center of how most of the country's goods get made, moved, and sold. The conversations here aren't about trends. They're about what actually breaks, what costs too much, and what is still being done by hand that probably shouldn't be.
I came here with a thesis. I'm leaving with product motion.
The thesis has been the same since we started SeerAI: the biggest problem in enterprise AI is not the intelligence, it is the context underneath it. The models are remarkable. The infrastructure that feeds them is not. I believed this before I got on the plane. What I didn't expect was how clearly, and how consistently, I'd hear it from the people actually running these operations.
Here is a question that should be easy to answer: which of our locations are going to run short on a critical item in the next three days, and why? Not a strategic question. A Tuesday question. The kind of thing an operations leader should be able to answer before their morning meeting. To answer it automatically, you need information from a weather service, a carrier's platform, a supplier's system, and an inventory database. Four organizations. Four systems. Each with its own vocabulary. The carrier calls a location a terminal. The supplier calls it a site. The inventory system calls it a location. They mean the same building. No computer knows that. So a team of analysts spends two days pulling it together manually. By the time they have an answer, the situation has already changed.
This is the wall. Not an intelligence wall. A visibility wall. Every conversation I've had here eventually arrives at some version of it. Different industry, different vocabulary, same wall.
What strikes me about this is how long the problem has been recognized and how little progress has been made on it. The solution everyone reaches for is standardization, force every supplier, every carrier, every system to use the same vocabulary. That fight has been going on for decades. It never ends, because every company has a perfectly good reason to keep doing things the way they already do them.
The insight we keep coming back to at SeerAI is that you don't have to win the vocabulary argument. You don't have to force everyone to agree on the same word for the same thing. You just have to give the thing itself a permanent identity that every system can point to.
Think about what GPS did for navigation. Before it, directions were entirely local: street names, landmarks, reference points that only made sense if you already knew the area. GPS didn't make everyone agree on a shared set of directions. It gave every point on Earth a single coordinate that every navigation system could resolve. Now it doesn't matter whether you're using one app or another, speaking one language or another. The location is the location.
That is the direction we are moving in for supply chain data. Not a new system to replace the ones that exist. Not a migration or a rip-and-replace. A layer underneath the existing systems that gives every entity, every facility, every shipment, every product, every event, a permanent identity that connects across boundaries without anyone having to change the way they work.
When that layer exists, the Tuesday question becomes answerable in seconds. An AI agent can look at a weather alert, trace which routes it disrupts, follow those routes back to which suppliers feed them, and identify which locations downstream are going to fall short, all before anyone picks up a phone. Not because the AI got smarter. Because it can finally see the whole picture.
I knew the problem before I arrived. What Bentonville gave me was precision about it: the specific places where the wall appears, the specific costs when it doesn't get resolved, the specific questions that enterprises are trying to answer and can't.
It also gave me something else. Urgency. There is a window right now. AI agents have crossed a threshold where they can traverse a well-structured knowledge layer at the speed of a business decision. That was not true three years ago. What it means is that the value of solving the visibility problem is no longer theoretical. It is operational. The enterprises that close this gap in the next twelve to eighteen months are going to make decisions their competitors are still making manually.
That is not a prediction. It is what I heard in the rooms.