Why Your AI Supply Chain Strategy Should Start With Security, Not Software

Ask most manufacturers where their AI supply chain strategy begins and you’ll hear about platforms: which tool to buy, which chatbot to try, which software promises better forecasts. That’s the part that gets the attention. It’s also the part that should come last.

During a recent Moore on Manufacturing episode, Mike Sibley shared valuable insights on how manufacturers should bring AI into their supply chains. The discussion highlighted the importance of building a foundation of security, governance and training before any company data goes into an AI platform.

The Pressure to Move Fast

Supply chains don’t give anyone much time to think these days. Geopolitical events and cost swings can change the picture quickly, and when conditions change that fast, customer data changes too, and forecasting has to keep up.

That’s exactly why AI is so tempting. Manufacturers and distributors already sit on huge amounts of data, and the hard part is turning it into something they can act on. Too much information often leads to analysis paralysis, where teams end up doing nothing with it at all. AI looks like a way out.

The catch is that rushing into it can create a bigger problem than the one it solves.

Security Comes First

The most common misstep isn’t picking the wrong tool. It’s employees taking company data and dropping it into personal ChatGPT accounts. It happens at manufacturers and other businesses, usually with good intentions, and the result is private information sitting in an open LLM.

“Don’t just go sign up for an AI account on any of the platforms and start throwing all your data into it,” Mike cautioned. “You can really open yourself up to releasing information that you don’t want released.”

So before anything else, ask a simple question: is your data secure? If the answer isn’t a confident yes, that’s the first thing to fix.

Governance Sets the Rules

Once data is protected, the next layer is governance: a clear policy on where AI can and can’t be used. Without one, employees make those calls on their own. Some will use unsecured versions. Others may connect AI straight to data sources the company never meant to open up.

This is the mistake underneath all the others. “An even bigger mistake is to not look at the overall infrastructure,” Mike said. “Look at your AI framework first and then go from there down in.”

Training Closes the Gap

Most hesitation around AI isn’t stubbornness. It’s a lack of understanding, which is a big reason adoption has been slow. Training fixes that, and it belongs inside the framework rather than tacked on after it.

Good training covers more than which buttons to press. It teaches people how to put the right information in, how to prompt well and how to read the output with a critical eye. Anyone who has pasted a question into ChatGPT and gotten back an answer that has nothing to do with their business knows why that last part matters.

Workflows Are Where AI Pays Off

With the foundation in place, AI can finally do the work people wanted from it in the first place. The biggest impact doesn’t come from writing emails or building spreadsheets. It comes from building AI into workflows.

That requires knowing what those workflows are. Manufacturers usually have production mapped in detail, but the administrative side (supply chain, finance and HR) rarely gets the same attention. Mapping those processes is what makes it possible to put AI where it helps.

AI Informs the Decision. People Make It.

Even with good data and good workflows, AI shouldn’t be making calls on its own. Think of it like an Excel model. A wrong formula can lead to a wrong decision, so you check the result before acting on it. AI works the same way, and experience still has to fill in what the model can’t.

As Mike put it, “Use it as a tool. Don’t use it as your sole advisor on how you’re going to manage and run your business.”

Build the Foundation, Then Build Up

The order matters more than the tools. Security, then governance, then training, then workflows. Manufacturers who follow that sequence can put AI to work in their supply chain without putting their data at risk along the way.

Watch the full Moore on Manufacturing episode with Mike Sibley to hear more about where AI fits in the supply chain, and subscribe to the Moore on Manufacturing YouTube channel for future episodes.

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