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Explore how supply chain leaders can use governed agentic AI to compress decision cycles, connect fragmented operational evidence, maintain human oversight, and move from disruption signals to accountable action faster.
Supply-chain disruptions do not wait for the next planning cycle. Tariff changes, supplier exposure, plant constraints, and service failures can move from signal to financial consequence while teams are still assembling evidence. The DataRobot and Supply Chain Now on-demand session centers on a concrete production scenario: a tariff signal put $2.5 billion in revenue at risk and was resolved in 72 hours instead of weeks, with human sign-off at every step. [1]
For supply-chain, operations, procurement, manufacturing, IT, and transformation leaders, the question is not whether an AI agent can generate an answer. The sharper question is whether an agent workforce can operate inside real enterprise systems, assemble the right context, preserve governance, and shorten the path from disruption to accountable action.
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