Microsoft is finding an early business case for artificial intelligence agents in a decidedly unglamorous corner of the enterprise: freight routes, spare parts and demand forecasts.

The company deployed more than 25 AI agents and related applications across its supply chain, according to a March company blog post. The systems simulate demand, anticipate shortages and recommend shipping routes after weighing cost, speed and carbon impact. Microsoft’s logistics teams are saving hundreds of hours each month.

The situation reflects a broader opening for agentic AI. Large supply chains produce an endless flow of purchasing data, inventory signals, invoices and transportation decisions. Many choices are too small to command an executive’s attention but too numerous for employees to review continuously. An agent can monitor that stream, identify an exception and recommend an action before a shortage or overpayment grows.

Microsoft began building the foundation years before the agents arrived. In 2018, it consolidated more than 30 systems into an Azure supply chain data lake, the post said. It started experimenting with generative AI in 2022, then developed a platform for deploying agents at scale.

Three applications show how the model works. A demand planning agent runs simulations for data center rack components. A spare parts system combines computer vision with multiple agents to predict storage needs and flag possible stockouts. CargoPilot continuously compares transportation modes, routes, costs and delivery times before recommending how a shipment should move.

The complication is that supply chain inefficiency often hides inside fragmented records and routine transactions.

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