For years, manufacturers expected software to answer one fundamental question:
What happened?
ERP systems managed business transactions. MES platforms monitored and managed production operations. CMMS and EAM systems organized maintenance activities and asset performance. In many companies, however, that information also lived in spreadsheets, homegrown databases, disconnected applications, or even paper records. Few manufacturers built their technology environments all at once, and most operate with a mix of systems implemented over many years.
Regardless of how those environments evolved, the goal has remained the same: create an operational record that helps manufacturers run the business.
That foundation remains essential. Manufacturers still need accurate inventory, production history, maintenance records, financial information, and clean master data. Systems of record are not going anywhere.
The question is no longer whether manufacturers have information. Most have more operational and financial data than ever before. The challenge is turning that information into action quickly enough.
- What's happening now?
- What needs my attention?
- What should happen next?
- How do we coordinate work across people, systems, equipment, and facilities?
Answering those questions requires software to do more than store information or automate individual tasks. Manufacturers increasingly expect technology to identify priorities, connect operational context, and support better decisions.
That shift is driving the next evolution of manufacturing software, with AI emerging as one of the key enablers. That doesn't mean AI replaces ERP, MES, CMMS, EAM, or other core systems. Those platforms remain the operational foundation. Instead, AI is enabling software companies to build more intelligent capabilities into those platforms, helping manufacturers better use the information they already have and turning operational data into actionable insight.
The result is less time spent gathering and interpreting information—and more time solving problems before they become larger operational disruptions.
Manufacturing Has Been Preparing for This Moment
This shift has unfolded over multiple generations of manufacturing software, with each stage expanding what companies expected technology to do.
While every manufacturer's technology environment looks different, the overall direction has been remarkably consistent: better systems, more connectivity, improved interoperability, and richer operational data.
The first layer was systems of record. ERP, MES, CMMS, and EAM platforms captured and organized operational information, replacing spreadsheets, paper records, and disconnected information with a more reliable operational foundation.
Then came systems of visibility. Dashboards, reports, monitoring platforms, and analytics tools made that information easier to understand across the business. Manufacturers could identify problems faster, but people still had to determine the best response.
The next layer was systems of action. Workflow automation, scheduling systems, maintenance coordination tools, and production optimization platforms helped manufacturers move from insight to execution. As labor became tighter and operations became more complex, helping people execute work became just as important as helping them see it.
Now manufacturing software is evolving toward systems of intelligence and orchestration. That doesn't mean AI replaces human judgment. It means software is becoming better at recognizing patterns, connecting operational context, and helping manufacturers coordinate decisions across the business.
Imagine a predictive maintenance system: Vibration data indicates a critical production asset is likely to fail within the next several days. Rather than discovering the problem during tomorrow's production meeting after downtime has already occurred, intelligent software alerts maintenance, identifies the best production window for repairs, verifies replacement parts are available in inventory, evaluates customer delivery commitments through the ERP system, and recommends the least disruptive maintenance schedule.
None of those decisions happen in isolation; they depend on connected systems, quality data, and software capable of coordinating information across the operation.
Most manufacturers already have the data. The challenge is connecting it in ways that help people make faster, better decisions before small operational issues become larger business problems.
Manufacturing Is Where AI Meets Reality
This interconnectedness is also why manufacturing has become one of AI's most demanding proving grounds.
Manufacturing doesn't reward technology for being interesting. Manufacturing environments are physical, process-driven, and full of exceptions. Every decision carries consequences as companies balance machines, materials, people, maintenance schedules, quality requirements, customer commitments, and production deadlines. Technology must perform in that environment, not just in a product demonstration.
On the plant floor, success is measurable. Does it improve throughput? Increase uptime? Reduce manual work? Help teams respond faster to disruptions? Improve OEE? Improve maintenance planning? Strengthen scheduling? Reduce quality issues? Give managers better visibility into operational constraints before they become larger problems?
If the answer is no, manufacturers will move on quickly.
Paul Lachance
That's why AI itself isn't the value proposition. The value comes from how software companies are using AI to build capabilities that help manufacturers operate more efficiently, make better decisions, and respond more effectively to changing conditions.
Manufacturers have good reason to be skeptical of broad claims or one-size-fits-all solutions. Industrial environments expose gaps that aren't obvious in a demonstration. Even well-designed software can struggle when it encounters incomplete information, competing priorities, production variability, and the countless exceptions that define manufacturing operations. Success depends not only on the capabilities of individual systems, but also on how effectively they share information and work together across the operation. At the same time, manufacturers shouldn't lose sight of where software is heading. The evolution from systems of record to systems of visibility, action, intelligence, and orchestration is already underway.
Manufacturing has always depended on making good decisions under pressure. As operations become more connected and more complex, software will help organizations recognize change sooner, understand its impact more completely, and coordinate the right response across the business.
The question isn't whether AI will change manufacturing technology—it already is. The more important question is which companies will use it to build software that helps manufacturers operate smarter, faster, and with greater confidence. As always, the winners won't be determined by the technology itself, but by how effectively they apply it to solve real operational challenges.
Paul Lachance is a leading voice in industrial and manufacturing software, contributing to industry publications, speaking at conferences and executive forums, and publishing research on AI, digital transformation, and the technology trends shaping vertical software. He founded and successfully exited Smartware Group (BigfootCMMS) and Symbiotic Systems, pioneering early cloud-based CMMS/EAM and industrial operations software. His flagship platform evolved into Brightly Software's Asset Essentials, now part of Siemens, and today supports more than 110,000 users across 2,200 organizations worldwide. Paul now serves as Chief Industry Strategist, Technologist, and Evangelist at Software Equity Group (SEG), where he advises software founders, executives, private equity firms, and strategic acquirers on growth strategy, AI, market positioning, and M&A. Reach him at [email protected].
