Michael Caney

Special Guest Blog Post written by Deborah Dull   Imagine it’s launch season in your supply chain, and a team of AI agents is hard at work: coordinating shipments with suppliers, allocating inventory, and handling a missed delivery. It’s hard to go a day without hearing about the latest AI headlines: new LLMs, new applications, new use cases. These discussions are part of my every day. Business leaders and IT teams are eager to explore – and they have a lot of questions… and apprehension. In these discussions, we have found one framework that lands consistently: consider AI agents like new hires, not new software.   Step 1: Hire Like You Mean It Just like hiring humans, this step is about defining what you need. The first decision is the purpose of the role: what problems are you trying to solve? Where are your people overwhelmed? Where is your business growing in coming months? From here, the next decision is to build the business case just like you would for a new headcount. For example, consider the ROI of having a digital employee who can process supplier performance data 24/7. Now, decide on the type of agent that will join…

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…