Supply Chain AI Needs Better Sensing, Not Smarter Models

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AI models are getting smarter at an astonishing pace. But if they can't reliably sense what's happening across your supply chain, they'll still make poor decisions. The real bottleneck may not be intelligence at all, it may be the quality of the physical data feeding it.

I'm joined by Doron Hazan, who leads AI at Wiliot, to explore why the next step in AI for supply chain isn't another model or agent, but a stronger data foundation connecting products, pallets and warehouses to real-time decision-making. We discuss why incomplete physical data creates misleading dashboards, slows operational decisions and increases the risks of automating the wrong actions.

We examine why AI in supply chain is fundamentally different from AI in the digital world, why many organisations are investing in AI before fixing the data beneath it, and what changes when individual items become continuous sources of operational data. We also explore where human judgement remains essential, how continuous sensing supports faster decisions at scale, and why better models alone won't solve supply-chain execution.

Listen now to understand what is really limiting AI in supply chain, and why the organisations that invest first in their data foundation will be better placed to make faster, more reliable operational decisions. 

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