There is one data point from this year that should make any operations leader uncomfortable: the average company now operates twelve AI agents, and half of them work entirely on their own, disconnected from the rest. That is one of the findings from Salesforce’s 2026 Connectivity Benchmark.
Twelve intelligent tools inside the same business, and six of them have no idea the others exist.
The symptom is not new. It is the same problem you see in a warehouse where inventory runs on one system, transportation runs on another, and someone reconciles both in a spreadsheet every Friday afternoon. The technology changed. The problem did not. That is why Sage’s State of Supply Chain Report found that only 10% of 200 operators have AI truly embedded in their workflows. One in ten. There is no shortage of investment or tools. What is missing is operational integration.
Gartner ranked agentic AI among the leading supply chain technology trends for 2026 and placed it alongside an area that would not have appeared on the same list two years ago: trust and governance. That inclusion alone says a great deal. AI has moved beyond being a technological curiosity. It is now influencing decisions with real financial consequences.
And when an AI project fails in logistics, the postmortem rarely concludes that the model was not intelligent enough. The problem is usually something else: the agent lacked context. It did not know the carrier’s history. It could not see the current rate. It had no way to check what happened with that same shipment the week before. Agents do not fail because they lack intelligence. They fail because they lack shared context.
The interesting part is that the building blocks to solve this already exist. Protocols such as MCP, A2A, and ACP were designed specifically to allow agents to communicate with one another. The problem is not technological. It is architectural. No one decided how those twelve pieces were supposed to communicate before buying them. The launch of LSP44 by project44 in July points directly to this issue: carrier connectivity, shipment and carrier performance data, workflows, and agents brought together within a single layer. What matters is not the product itself, but what it acknowledges. Logistics data is no longer plumbing. It has become a strategic asset, because AI is only as good as the context behind it.
The results are already there, and they are credible precisely because they are relatively modest. Aera Technology reports logistics cost reductions of up to 15% by moving from reactive workflows to proactive orchestration. Deposco helped brands such as Psycho Bunny reduce incomplete shipments by 90%. And there is one important 2026 nuance that runs counter to the usual sales narrative: investment is increasingly going toward modernizing existing warehouses rather than building new ones.
At the Supply Chain AI Symposium in July, one idea helped put all of this into perspective. Roughly 80% of logistics work is repetitive and rules-based. The remaining 20% requires judgment, context, and decision-making. AI is excellent at that 80%. But that 80% is not where an operation is won or lost.
It is lost when a shipment gets stuck at customs on a Friday. When a customer changes the order eighteen hours before cutoff. When a carrier cancels and needs to be replaced without anyone on the other side noticing. Automating the 80% without strengthening the remaining 20% does not create an efficient operation. It creates one that moves very fast until the first exception occurs, and then comes to a complete stop.
Imagine twelve brand-new forklifts, each operated by an excellent driver. None of them has a radio. Everyone performs their job perfectly, yet by the end of the shift there are blocked aisles, duplicated routes, and incomplete orders. No one made a mistake. The forklifts were never the problem.
That is what artificial intelligence looks like today in most companies: twelve capable pieces and zero coordination. And getting independent assets to behave like a single system is exactly what the 3PL industry has spent the last forty years solving—only with trucks, warehouses, and customs instead of software.
So before asking where else to apply AI, there is a much less glamorous question worth asking: Do my systems share the same context? If the answer is no, adding another agent will not fix it.
A 3PL never really sold square footage or units moved. It sold orchestration. Artificial intelligence did not change that business. It just proved the point.
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