For the better part of three years, the manufacturing conversation has centered on one question: Should we adopt AI (artificial intelligence)? That question has now been answered. It is a clear yes. Now comes the hard part.
The reality is the more important question today is much more difficult: Can manufacturers actually scale AI in a way that transforms the business? That is the trillion-dollar question on the table today for the manufacturing industry.
A recent Parsec global survey of 1,200 manufacturing leaders across executive, operational, and technical roles found 72% of manufacturers have adopted AI in some capacity, yet only a small fraction has deployed it across their operations. That gap is far more telling than the adoption number itself. It suggests the industry has embraced AI conceptually, but operationally, many organizations are still standing at the starting line. And from my point of view this is different than any other discussion about a strategy of technology we have discussed for the past several decades. And with good reason.
This shouldn’t surprise anyone who has spent time in manufacturing. Factories are among the most complex operating environments in the world. Legacy equipment sits beside modern automation. Data lives in multiple systems. Production priorities shift daily. In fact, even in this survey, it finds more than two-thirds (69%) of manufacturers operate with a hybrid mix of legacy and modern equipment, highlighting the industry’s unique straddling of several generations of technology.
Successfully introducing AI into that environment ultimately means creating an ecosystem where data, people, and processes all work together. The same words exist today that we have been talking about for years: If the underlying operation is fragmented, AI simply helps organizations move faster in the wrong direction. If the data is inconsistent, AI produces inconsistent outcomes. If employees don’t trust the information, they won’t trust the recommendations either.
What I also found interesting in the Parsec survey is more manufacturing leaders worry about moving too slowly with AI than moving too quickly. The research finds more leaders are worried about being too hesitant with AI (60%) than being too aggressive (40%). That represents a remarkable shift in executive thinking. Just a few years ago, conversations centered on the risks of adopting AI. Today, the greater concern appears to be falling behind competitors.
Competitive pressure can certainly accelerate innovation, or it can encourage organizations to mistake activity for progress. Launching pilots across multiple plants may generate headlines internally, but pilots are not business strategies. Manufacturers ultimately create competitive advantage by operationalizing technology, standardizing best practices, and scaling successful initiatives across the enterprise.
That requires governance, data strategies, leadership alignment, and perhaps most importantly, patience. The organizations that ultimately lead this next industrial era will be the ones that build the foundation necessary to scale those capabilities repeatedly, securely, and confidently.
The pilot phase has served its purpose, but now manufacturing must prove it can scale. And that is truly going to be the hard part.
And remember it’s about having the right data, for the right purpose, for the right goal that you can act on.
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