Altif Brown

In today’s increasingly complex global supply chain landscape, Tom Moore keeps his message refreshingly straightforward: ProvisionAI helps large companies discover hidden costs and eliminate them. Organizations such as Procter & Gamble, Nestlé, and Unilever have leveraged the company’s technology to uncover and eliminate inefficiencies—particularly in transportation and warehousing—that traditional systems fail to detect. The outcome is significant and often delivers immediate savings. But Moore believes many of these problems stem from misunderstandings about the very technologies companies rely on.   Misnamed Systems & Misaligned Expectations Before the interview officially began, Moore reflected on the surprisingly inaccurate names assigned to modern supply chain technologies. ERP systems rarely plan resources across the enterprise, despite what their name suggests. Warehouse Management Systems, while certainly used in warehouses, don’t actually “manage” much at all. People behind keyboards still make most of the critical decisions. This disconnect in terminology shapes faulty expectations. Many organizations believe their planning systems will truly plan the supply chain, yet most tools merely react to demand signals. If ABC Company orders ten cases, the system automatically replenishes—without considering warehouse capacity, transportation availability, downstream implications, or cost-to-serve. Moore characterizes this as both an old problem and a new one, and it…

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…