Walk onto most operations floors today and you'll find AI already embedded in the daily work: demand forecasting tools, automated exception flagging, procurement systems that surface anomalies before a human would spot them. Walk into the classroom training the next generation of supply chain professionals, and the gap is often stark: the tools being taught lag years behind the tools already in use.
This isn't a criticism of educators; curricula move slowly by design, and that's often appropriate. But it does mean graduates increasingly arrive at the operations floor needing to learn, on the job, the very skills that should have been foundational: reading an AI-generated forecast critically rather than trusting it blindly, knowing when an automated flag deserves escalation and when it's noise, and understanding enough of how these systems work to spot when they're wrong.
The more useful shift isn't "teach students to use AI tools" as a bolt-on module. It's teaching the judgment underneath: what a forecast actually assumes, where automation quietly narrows a decision that used to require human context, and how to stay accountable for a decision an algorithm helped make. That's a curriculum question as much as a technology question, and it sits squarely at the intersection of practice and education.
Operations teams and training institutions have a shared interest here, even if they rarely talk to each other directly. The businesses adopting these tools fastest are also the ones best placed to say what the next cohort actually needs to know.