Chris Turlica is the CEO of MaintainX — a leading AI-powered CMMS and EAM platform for maintenance and reliability teams.
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When an AI system misreads a sensor on a production line, the cost isn’t a subpar email draft; it's tens of thousands of dollars per hour in lost output and a workforce that may never trust the technology again. To build transformative AI, we need to tackle tough problems—and that means pushing beyond routine knowledge work tasks and bringing AI to bear on the far bigger challenges found in America’s industrial facilities.
It’s impressive, of course, that LLMs can summarize emails, draft ad copy or write code. But these are unconstrained problems: For most such work, there’s no single right way to execute a task. “Probably right” is good enough; with enough data to train on, it’s relatively trivial to build AI models that pass the sniff test.
Models can fudge their way through routine tasks (and deliver real productivity gains) without ever fully solving fundamental challenges such as eliminating hallucinations, ensuring reliable decision-making or securing worker buy-in. Startups can create workable AI tools without really breaking a sweat; the market is flooded with products that are basically fine but seldom truly excellent.
Except “probably right” doesn’t work in industrial environments; it needs to be absolutely right.
To take AI to the next level and overcome lingering doubts about ROI, we need to aim higher. The best way to do that is by building industrial solutions—and turning the world’s factories into proving grounds for AI technologies.
What makes factories so special? It’s the combination of three things: huge potential upside, clearly defined, challenging and unavoidable constraints and a smart, engaged workforce that’s unafraid of disruption.
Let’s start with the upside: Unquestionably, industrial AI has the potential to deliver huge gains. According to Forrester, some manufacturers already use AI assistants to unlock a 76% boost in efficiency, driving 457% ROI. And assistants are one use case among many: From robots that monitor operations or take over dirty and dangerous tasks to predictive algorithms that slash costly downtime, there are countless pathways for AI to deliver bottom-line results.
This makes industrial AI a tempting target for innovators. The manufacturing AI sector will be worth over $155 billion by 2030, up from $34 billion last year, far outpacing many other enterprise AI segments.
Unlocking that value requires solving several big, thorny, intersecting problems. Those huge downtime bills cut both ways: If an AI glitch knocks out a conveyor belt, orders too little inventory or botches a manufacturing run, the losses can be enormous. For industrial operators, “probably right” isn’t good enough—to be useful, AI has to be utterly dependable.
Then there’s the fact that, by definition, industrial AI must interact with the physical world, controlling machinery and guiding heavy objects around crowded factory floors. That’s something AI models struggle with—and it must be done safely and seamlessly, without disrupting production or putting humans at risk.
Many AI projects face friction because knowledge workers worry about being displaced by automation. Industrial workers, by contrast, are more confident in their utility: AI might help a technician work smarter, far more likely to expand their capabilities than eliminate their role entirely.
As a result, industrial workers are eager to kick the tires on AI tools. Just 4% of manufacturers see skepticism as a barrier to adoption, and 76% believe their teams are eager to use new technologies—the highest of any enterprise sector.
Despite this, challenges remain. Industrial AI relies on operational data that’s currently stored in the heads of technicians and equipment operators. To succeed, companies must externalize that data by embedding AI solutions directly into front-line workflows and capturing the unstructured insights technicians generate as they go about their days.
Done right, that will turn AI tools into repositories of previously unrecorded operational know-how, preserving valuable information and leveraging it to accelerate the flywheel of innovation. But getting there requires a more empathetic approach than is found in many AI projects. Organizations will need to support industrial workers who need upskilling, build tools that deliver real value for front-line workers and then feed human insights back into AI models to unlock benefits across the organization.
These aren’t easy problems to solve—and that’s a good thing. Many tech companies think it’s fine to ship a hallucinating AI tool, then iterate over time. In manufacturing, a single bad decision can trigger production stoppages, injuries or compliance violations. By necessity, industrial AI is held to a higher standard: There’s nowhere to hide and no room for shortcuts.
That makes industrial AI the perfect environment to build and test robust, dependable AI systems. While Silicon Valley chases incremental benchmark improvements, industrial leaders are working to earn their customers’ trust, shift by shift, through validated deployments that deliver concrete benefits.
Surveys show that more than three-quarters of manufacturing executives judge AI based on concrete factors like operational performance and cost savings. Marketing hype won’t cut it: The only way for AI to succeed is to push past the noise and build tools that actually work.
Because industrial AI is challenging, innovation happens more deliberately than in other sectors. Pain points drive adoption: Companies with high downtime costs are twice as likely to invest in AI-powered maintenance. But there’s little of the competitive pressure that's led many enterprises to rush out AI tools without a proven pathway to ROI. While 98% of manufacturers are exploring AI, in fact, only 20% say they’re ready to deploy it at scale.
Because their standards are so high, industrial facilities are quietly becoming the economy’s most rigorous proving ground for AI technologies. Instead of rushing to implement unproven tools, companies are working to build strong data foundations, earn front-line trust and rigorously measure outcomes. They’re partnering not with startups but with established innovators with domain expertise, safety track records and a stake in long-term outcomes.
America’s industrial operators are holding AI to a higher standard—and showing a better path forward for AI innovation.
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