Artificial intelligence has spent the past few years learning how people click and buy. Now it is moving closer to the harder part of commerce, where a late shipment or missed delivery window can become a customer problem before a retailer has time to react.
Agentic AI in Supply Chains
A 2025 PwC operations survey found that 53% of respondents already use AI to anticipate and reduce supply chain disruptions, while another 31% are testing it. Agentic AI is the term now attached to systems built to pursue a business goal and decide the next step as new information comes in.
Old automation stays rigid, doing exactly what its rule says and nothing past it. For example, a system set to reorder 500 units when stock dips below a line does just that, even as the supplier runs dry and the order stalls. But the agentic version starts from the goal instead, holding a product in stock at a fair price.
If the usual supplier runs out, it finds a new source and places the order itself. And inventory balancing follows the same logic, with an agent moving stock toward a regional spike and easing off where demand has cooled.
Benefits and Risks
Sinan Aral, a professor at MIT Sloan, says the agentic AI age “is already here,” with real agents at work across the economy. Agentic systems keep working when reality breaks the script, while older tools simply quit.
The work still belongs to people, though fewer decisions have to sit inside a spreadsheet or wait for a status check. In other words, faster reads inside the building lead to fewer wrong picks and cleaner handoffs before an order leaves the dock.
Once an order clears the dock, speed depends on how quickly the wider network reads demand and disruption together. Global Trade Magazine has described AI forecasting as a way to read sales history and seasonal patterns alongside outside conditions, giving retailers a faster view of where inventory may run short or sit too long.
Agentic systems then carry that view into fulfillment by treating demand planning and order movement as one connected loop. For example, when a clothing size sells out in one location but sits idle in another, the system moves stock between locations before the missed sale spreads.
Every gain from this speed carries a matching risk, and the first one sits with the data itself. An agentic system acts on what it reads, so a sensor incorrectly reporting inventory levels may have the agentic system accepting new orders with full confidence when they can’t be fulfilled.
Clear rules hold that danger in check, letting a system reorder on its own under a set dollar line while flagging a costly emergency shipment for a human to approve.
Original reporting: KTVZ (Central Oregon) — read the source article.
