Logistics news

Intelligence Is Becoming Part of the Logistics Control Loop

17-09-2026 14:57 via logisticsviewpoints.com

The New Logistics Advantage — Part 2 of 9
The first wave of enterprise AI was largely additive. Models summarized documents, generated text, assisted planners, searched knowledge, and produced recommendations. Useful capability was placed beside the existing operating model.
The next wave is different. AI is beginning to enter the decision process itself. That shift is developed in the foundational AI in the Supply Chain architecture white paper and extended in AI in the Supply Chain: From

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Salesforce Dreamforce Keynote: The Deterministic Layer Behind the Agentic Supply Chain

17-09-2026 14:56 via logisticsviewpoints.com

The most important supply chain message from the Salesforce Dreamforce keynote was not about a new model, chatbot, or even a new agent. It was about architecture.Enterprise AI is moving into a phase where probabilistic systems are being asked to act inside deterministic operating environments. That creates a fundamental problem for supply chains, where decisions may involve uncertainty but execution cannot. Inventory balances, shipment transactions, supplier approvals, purchase orders, user perm

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May Mobility’s $1.4B SPAC Tests Asset-Light Autonomy

17-09-2026 14:56 via logisticsviewpoints.com

May Mobility is going public in a SPAC transaction that values the autonomous vehicle company at approximately $1.4 billion. The obvious story is the valuation. The more important logistics story is the operating model.May Mobility is attempting to separate autonomous intelligence from transportation assets. Its partners can own the vehicles, operate the depots, maintain the fleets and provide the customer interface, while May provides the autonomous driving system, software, remote assistance a

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Harness Engineering in Logistics: Why Better Models Aren’t Enough

17-09-2026 13:00 via logisticsviewpoints.com

Harness Engineering in Logistics — Part 2 of 6
When an AI system produces a poor result, the natural response is to blame the model. Change the prompt. Add more context. Move to the newest model. Increase the reasoning budget. Those interventions can improve performance, but they can also hide a more important problem: many logistics AI failures are not model failures at all.
They are system failures. The model may reason correctly while receiving the wrong data, operating with incomplete

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