Glen Gilmore

written by Deborah Dull, on site at GreenBiz 2026   I spent today in a room full of people who think about waste for a living. And the word that kept coming up had nothing to do with recycling. It was sovereignty. Here is the situation. The United States imports 95% of its critical mineral supply. Lithium, cobalt, rare earth elements, the stuff inside every battery, every semiconductor, every electric motor. We do not make it, we do not mine much of it, and we do not control the supply chain that delivers it. That is not an energy policy problem. That is a national security problem. Now here is the part that should make you put down your coffee. A ton of smartphones contains dramatically more gold than a ton of mined ore. We are talking about concentrations that make urban mining look like a gold rush compared to digging in the ground. And yet the recovery rate for those materials, once a phone leaves its first owner, drops to around 13%. We are losing roughly 80% of the value sitting in devices right now, in drawers, in closets, in landfills. E-waste is also the fastest growing waste stream…

At Gartner Supply Chain Symposium/Xpo 2026 in Orlando, some of the sharpest minds in supply chain gathered to tackle one central question: what does the next operating model for supply chain actually look like? In a special livestream conversation, Scott Luton sat down with Mike Griswold, VP Analyst at Gartner, alongside fellow Supply Chain Now hosts Karin Bursa and Jake Barr, to unpack the biggest themes emerging from this year’s symposium. The consensus? Supply chain leaders are moving beyond AI fascination and toward something much bigger: redesigning how decisions, workflows, and organizations operate.   AI Is No Longer the Story. Outcomes Are One of the strongest themes from the event was a more mature, pragmatic approach to AI adoption. According to Mike Griswold, many organizations are finally moving past the “shock and awe” phase that dominated conversations a year ago. “People need to figure out exactly what problem or problems AI is going to solve for them,” Griswold explains. That may sound simple, but it represents a significant shift. Instead of experimenting with AI for AI’s sake, companies are becoming more disciplined about identifying operational value and measurable business outcomes. Griswold also warns against a familiar trap: creating “highly efficient…