Agentic AI in Supply Chains: Operationalizing for Success

Agentic AI promises a revolution for supply chains, offering self-correcting systems for delays and inventory. However, there's a catch: this AI needs to understand the supply chain to perform reliably. The problem isn't the AI itself, but the fragmented systems it's expected to work within. Data is often scattered across many different platforms. When AI is applied to these disconnected data sets, the resulting "intelligence" can be artificial. The real challenge is giving AI the operational understanding it needs. To move beyond simple automation to true Agentic AI, companies must first build a foundation of process intelligence. This helps them understand how work is actually done, where deviations occur, and how improvements can be made. Without this operational backbone, AI often fails to deliver. Agentic AI is different from traditional automation; it makes decisions and takes action independently. This could mean responding to supplier delays or rerouting exceptions. But for this to be effective and safe, proper governance is crucial. This matters because it allows for a shift from just seeing what's happening to actively controlling it.