AI in Manufacturing: Why Leaders Need More Than Familiarity to Drive Real Impact

By Adrián González Sanchez

When people ask me where AI can have the biggest impact in manufacturing, my honest answer is “it depends”. There are plenty of transversal applications, but the real breakthroughs come when we understand the specific use cases for a given operation.

From my perspective, the most relevant opportunities come from combining predictive and optimization capabilities. If you can predict certain variables and then optimize for them in advance, you can move from reacting to problems to shaping the future. That’s powerful—not just for your operations, but for your supply chain as a whole.

I’ve also seen enormous potential in AI productivity tools that speed up access to technical information, analyze complex blueprints, or perform visual inspections of physical assets. Whether you’re working with bespoke solutions or pre-built tools, the key is knowing the exact problem you want to solve—and matching it to the right technology.

What It Means to Be “AI Literate”

There’s a big difference between being familiar with AI technologies and being truly AI literate as a leader. Familiarity might get you to understand what classification or regression means, but literacy is about connecting the dots beyond the obvious.

AI-literate leaders can scan their company’s environment, spot opportunities that are technically feasible and financially sustainable, and articulate a vision that inspires their teams. They also accept something that’s harder than it sounds: knowing what they don’t know. AI is evolving too quickly for anyone to know it all—and that’s okay. The real skill is learning in microdoses, every day, and building the confidence to lead AI initiatives despite the uncertainty.

It’s also about understanding your options for implementation. Building an AI product internally, hiring an external integrator, or licensing a pre-built tool each comes with its own trade-offs—technical, financial, and cultural. Leaders who anticipate these dynamics are far better positioned to succeed.

A Real-World Example

One of the most meaningful projects I’ve worked on was CargO2ai in Montreal during the peak of the pandemic. The goal was to accelerate the delivery of critical supplies—masks, gloves, and more—through the Port of Montreal.

What made it special wasn’t just the AI technology; it was the unprecedented collaboration between the port authority, terminal operators, truck and train companies, and government entities. No politics, no wasted time—just a shared mission. That level of coordination allowed AI to deliver immediate, tangible value when it mattered most.

The Role of Big Tech in AI Adoption

My time at Microsoft taught me that large tech platforms play a pivotal role in accelerating adoption—especially in traditional industries like manufacturing. I’ve seen companies spend millions building AI from scratch, only to realize they could have achieved more in less time by leveraging flexible, pre-built cloud tools from providers like Microsoft, AWS, or Google.

This isn’t about outsourcing your strategy—it’s about using the right infrastructure to reduce friction, tap into existing expertise, and free your team to focus on high-value innovation.

Compliance by Design

In manufacturing, compliance shouldn’t be an afterthought—it should be built into the process from day one. The same way you wouldn’t design an aircraft without considering safety regulations, you shouldn’t deploy AI without accounting for lifecycle management, transparency, risk mitigation, and governance.

Leaders who embrace compliance as part of the design process end up with solutions that are not only effective but also trusted—by regulators, by customers, and by their own workforce.

Breaking the “It’s Too Hard” Myth

One of the most common misconceptions I encounter is that manufacturing is too complex or too risky for AI. The truth is, manufacturers already work with complex systems every day. Adopting AI doesn’t mean overhauling everything at once—it means starting small, solving a specific and impactful problem, and building internal maturity from there.

Yes, it requires investment—in people, tools, and change management. But the resources and examples are out there, right here in Canada. You just need to be willing to take the first step.

Why I Work at the Intersection of Tech, Policy, Education, and Strategy

For me, it comes down to scale. I want to help individual companies, but I also want to create the kind of resources—articles, courses, books—that can reach hundreds or thousands of leaders at once.

This intersection keeps me learning every day. I’m constantly absorbing new information, processing it, and figuring out how to explain it in a way that’s clear, actionable, and relevant. It’s challenging, yes—but it’s also what keeps me energized and committed to helping manufacturing leaders not just understand AI, but truly make it work for them.

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About the Author

I’m Adrián González Sánchez, a global AI architect, educator, and author with more than 15 years’ experience leading AI strategy, ethical AI implementation, and technological innovation across industries. I’ve worked with organizations from Microsoft to IVADO Labs, delivering AI projects that range from supply chain optimization to responsible AI governance. I also design and teach AI programs for universities, governments, and industry, aiming to make advanced AI both accessible and impactful. My mission is to help leaders turn AI understanding into AI advantage. Connect with me on LinkedIn.