Vikas Shah

Special Guest Blog Post written by Matt McKinney, Co-Founder and CEO of Loop   I spend most of my time with supply chain and innovation leaders at major enterprises who are sitting on significant AI budgets but struggling to show measurable business impact in an increasingly complex and volatile supply chain environment.   These conversations have evolved dramatically. A year ago, executives were asking basic questions about AI feasibility. Today, the questions have shifted to strategic implementation at enterprise scale.   Based on hundreds of these discussions, three questions consistently emerge that separate companies making transformational progress from those stuck in pilot purgatory.   How do we move from AI experiments to enterprise-scale impact?   Most organizations have yet to see organization-wide, bottom-line impact from AI use. This is the strategic challenge keeping C-suite leaders awake at night.   The problem isn’t the technology. It’s the application of the technology. Too many enterprises are trying to treat AI like a magic wand they can bolt onto existing systems. But garbage in, garbage out. If your underlying data is fragmented and inconsistent, AI won’t solve your problems; in fact they’ll get worse.   At its core, anything automated is powered by…

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…