How Manufacturing Can Win The AI Race
Leon Lauritsen is the CEO of Aras, a leading provider of digital thread solutions for product lifecycle management (PLM) and engineering AI.
gettyIn my frequent conversations with executives at major product-driven companies, the question of their readiness to leverage advances in AI often comes up. They have witnessed, as have we all, real AI-driven change in software, back-office operations, sales and marketing. But the engineering, manufacturing and delivery of physical products is a different ballgame altogether.
The complexity of mechanical-electronic-software systems, the precision required to ensure physical product quality, and the ethical and regulatory demands of product safety conspire to make it more difficult for inference-driven generative AI to transform the business of physical product delivery. I wrote previously about the dilemma facing product leaders as they race to activate the potential of AI and made the case for them to “take ownership of connected product data as a strategic infrastructure.” As 2026 races on, that call to action has become even more important. As I urge all the executives I speak with, now is the time to make this a strategic priority.
They are smart, tremendously insightful people, and they are highly qualified to solve this challenge. But it’s not going to be easy, and here’s why.
Gartner research notes that many manufacturers risk delaying AI value realization because they lack the organizational readiness needed to successfully scale AI initiatives. That’s not surprising. Manufacturing has been conservative for good reason. You want to think twice before retooling your entire operation around the latest trends when your business involves factories, production lines and a lot of steel.
Now, counterintuitively, that conservatism can work to your advantage. The thing is, for AI to do its magic, it needs a well-structured data landscape. And chances are, you don’t have one. The reason for that is simple: Over decades, changes in how products are designed and manufactured, what goes into them, what regulators require, who your suppliers are, where production takes place and countless other factors have gradually turned your software landscape into a four-dimensional maze.
Those same changes also made it difficult to say no when your most trusted and insightful engineers came asking to buy (or build) a very specific piece of software to solve a small and very specific problem.
At the time, saying yes usually made sense. But looking back, all of those sensible decisions have left many manufacturers with disconnected systems and fragmented product data—the very things AI depends on to deliver value.
Now, you have a challenge, but so does the competition. That’s the good news. If everyone is facing the same challenge, then the companies that move first and strategically have an opportunity to pull ahead. It’s a hard thing to do, but it’s also at the core of what modern leadership is all about. It’s about preparing your organization so it’s ready to respond to the opportunities and challenges AI presents, and to reap the benefits it creates.
Here’s what’s different today. AI hasn’t created the problem, but it has made it impossible to ignore. The shared sense of urgency around AI finally delivers the mandate many manufacturing leaders have been waiting for: the mandate to say no to one more add-on and one more isolated island of data that doesn’t really connect anywhere, and to insist that ownership of your product data can no longer remain fragmented.
This makes product data more than an information-management concern; it makes it a strategic component of your enterprise architecture. Understanding how that data is created, connected and used across the product life cycle is increasingly critical to the business.
If you’ve been making the case for improving your digital foundation for years without gaining traction, the conversation has changed. AI has created a level of urgency that simply didn’t exist before. The discussions that were difficult to have a few years ago are now business priorities.
Appoint a product information architect or ensure there is clear ownership of product data and information flow across the entire product life cycle.
Rethink your product life cycle as a system of digital twins that describe the plans, designs, processes and decisions that go into delivering products to market. Most of these elements are already captured digitally, albeit in disconnected systems, spreadsheets, email threads or meeting minutes. Identify the key information that drives your business from the earliest product ideas through manufacturing, service and support, and end of life.
Determine what to replace, what needs to coexist and what to integrate. Establish an organizational change management and transformation program.
Create a governance board with the authority and mandate to oversee future application purchases and application development.
The manufacturers that win the AI race won’t necessarily be the ones that adopt AI first. They’ll be the ones that build the strongest foundation for AI to deliver meaningful business value.
The transformation required to unlock this value may be greater than many expect, but I believe that’s nothing compared to the rewards. The scale of change may be daunting, but the greater risk lies in resisting it.
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