All Dataleo news, jobs, analyses and tutorials around Transportation Management in Supply Chain and Operations.
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HighInfios has announced new AI agents embedded into supply chain execution workflows. The agents are designed to operate across orders, warehouses and transportation, supporting orchestration inside operational processes rather than sitting outside them as standalone advisory tools.
The announcement is relevant for Supply Chain Execution because it moves agentic AI closer to real operational workflows: order orchestration, warehouse issue resolution, transportation updates and exception handling. Infios positions the agents as part of its broader execution stack across OMS, WMS and TMS environments.
For logistics and operations teams, the signal is that Agentic AI is moving beyond planning and analysis into time-sensitive execution decisions. This raises practical questions around autonomy, supervision, exception thresholds and how AI-driven actions are recorded inside operational systems.
This announcement matters for Supply Chain AI because execution workflows have less tolerance for ambiguity than planning simulations. When an AI agent changes an order path, supports a warehouse supervisor or triggers a transport action, the decision has immediate operational consequences.
The key governance question is therefore not only whether the agent can act, but what it is allowed to act on. Operations leaders need clear decision boundaries, source data validation, escalation rules, audit trails and human override processes before embedding agents into Warehouse Management, Transportation Management and order execution workflows.
HighManhattan Associates has made its AI Agent Workforce commercially available across supply chain and omnichannel operations. The portfolio covers workflows in warehousing, transportation, stores and customer service, extending AI from assistance toward coordinated operational execution.
The announcement signals that agentic capabilities are moving into production environments where recommendations and actions can affect inventory, fulfillment and customer outcomes.
MediumA Manhattan Associates study found that 61% of organizations expected fully autonomous agentic AI in transportation management within five years, while only 37% had deeply integrated AI and machine learning into their TMS.
The readiness gap highlights the need for data quality, process ownership and controlled automation before autonomous transportation decisions scale.
When delayed freight returns at once, business priority must determine scarce capacity rather than queue order.
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Transportation procurement must evaluate price, capacity and identity risk together rather than as separate controls.
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A parcel-execution case shows why capacity planning should test orchestration before approving warehouse expansion.
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