Where Enterprise AI
Meets Operational Reality
I build AI-powered supply chains that actually work — and research why most don't. 18 years inside Fortune 50 operations, now applying that depth to transform how enterprise supply chains are planned, executed, and governed.
Practitioner. Researcher. Translator.
I started on the production floor in Binghamton, New York. That's not a footnote — it's the foundation. It's why, 18 years later, I understand exactly where enterprise AI deployments succeed and where they quietly fail.
At PepsiCo Beverages North America, I lead the North Division Command Center — an advanced operations hub delivering real-time supply chain visibility, early disruption warning up to 14 days out, and a 60% reduction in mean-time-to-resolution. I've also led network redesign initiatives generating $52M in structural savings.
In parallel, I'm completing a Doctorate in Business Administration at Fairfield University — studying why most enterprise AI deployments in supply chains fail and what separates the rare successes. Built on 202 enterprise case studies, this research has produced a patent pending AI readiness framework that directly informs how organizations design and govern AI systems at scale.
Thoughts on AI & Supply Chain
Why 80% of Enterprise AI Supply Chain Deployments Quietly Fail
The dashboards stop getting checked. The meetings get cancelled. Six months later the org is back to spreadsheets — still paying a SaaS license for a platform nobody's using. After 202 case studies, the failure almost never comes from the technology.
The Four Anti-Patterns That Are Killing Your AI Deployment
Across 202 case studies — in supply chains, healthcare, finance, and IT — four failure patterns show up again and again. They're not industry-specific. They're structural. And if you recognize them early, you can stop them before they cost you the project.
Agentic AI in Supply Chain: Separating Signal from Hype in 2026
Gartner says 55% of supply chain leaders expect agentic AI to reshape hiring by 2030. Most are reading that number without understanding what agentic AI can and can't actually do in a live enterprise environment. Here's the practitioner's version.
On Stages That Matter
The highest performing supply chain organizations aren't using AI as a cost-reduction tool. They're using it to fundamentally redesign how work gets done — and that requires operators who understand both the technology and the operations.
Brad Rogers — On Enterprise AI TransformationOriginal Research on Agentic AI
Dolan School of Business
AI Agent Adoption and Performance in Enterprise Supply Chains: Readiness Patterns, Governance Models, and Integration Principles
This empirical research examines why enterprise AI deployments in supply chain contexts so frequently fail to deliver projected value — and what distinguishes the deployments that succeed. Drawing on 202 enterprise case studies and logistic regression analysis, the research has produced a patent pending AI readiness framework identifying the readiness patterns, governance structures, and integration architectures that predict successful agentic AI deployment at scale.
AI Readiness for Supply Chains
ChainLytix is my independent advisory practice, built on the intersection of 18 years of Fortune 50 supply chain operations, doctoral-level research on AI deployment patterns, and a patent pending AI readiness framework.
I work with organizations navigating the gap between AI ambition and AI reality — helping them assess readiness, design governance frameworks, and build operating models that allow AI to create sustained value. I also advise early-stage startups building at the intersection of AI and supply chain.
Let's Build Something That Works
Whether you're navigating an AI transformation, looking for a speaker who's actually done it, advising a startup, or seeking expert perspective on supply chain technology — I'd welcome the conversation.