Manufacturers are eager to deploy AI, predictive maintenance and more advanced digital tools, but many still lack the reliable maintenance data needed to make those investments pay off. Paul Ross of Limble and Ross Fergerson of RBC Bearings explain why technician-friendly systems, stronger data capture and a phased CMMS rollout can help plants reduce downtime and move beyond reactive maintenance.
Manufacturers have spent the past several years talking about AI, automation and digital transformation as the next frontier of plant performance. But on many shop floors, a more basic problem still stands in the way: Maintenance data is incomplete, disconnected or stuck on paper.
In Episode 13 of the Ctrl+Alt+Mfg Podcast, Paul Ross, chief marketing officer at Limble, and Ross Fergerson, facilities manager at RBC Bearings, explained why that gap is limiting far more than maintenance efficiency. It is also undermining trust across the organization, which makes it harder for operations leaders to improve uptime, justify capital decisions and put AI to work in practical ways.
For maintenance teams, the issue often starts with process maturity. Many organizations still operate in a reactive mode, responding to failures after equipment breaks rather than using structured maintenance data to prevent downtime. According to Ross, that pattern remains widespread even as manufacturers push deeper into digital initiatives.
“If we look at the entirety of our data set, what we found was that overall 75% of organizations are in that reactive state,” Ross said. Manufacturing is somewhat farther along than some other sectors, he said, but many companies are still in the early stages of building preventive and condition-based maintenance programs.
That creates a problem for any manufacturer hoping to climb the maintenance maturity curve toward predictive or prescriptive maintenance. Before plants can use AI effectively, they need cleaner, more complete operating data that systems technicians will actually use.
Maintenance maturity starts with the basics
Computerized maintenance management systems (CMMS) have long been associated with work order management, but their role has expanded. Ross said modern platforms are increasingly expected to function as broader maintenance and asset management systems, helping manufacturers capture information that can support decisions across maintenance, operations and procurement.
“What these systems have evolved from is just managing work orders for maintenance … to being a maintenance and asset management platform,” Ross said.
The strategic value comes from what that information can enable. In a more mature maintenance environment, data can help determine when an asset should be repaired, replaced or monitored more closely. It can also support preventive schedules, condition-based maintenance and eventually more advanced predictive capabilities.
Still, many plants remain stuck at the first step. Maintenance histories are often incomplete, especially when technicians rely on handwritten notes, paperwork orders or spreadsheets that are never consistently entered into a central system. That limits visibility and makes downstream decisions harder.
Ross said the lack of complete information also has a measurable effect on organizational confidence.
“If people aren’t using the system to capture those things, trust from other parts of the organization … drops down to a 4% level of confidence,” he said. “If people have got a complete view of it, it goes up almost 12 times that.”
That trust issue matters because maintenance data does not stay within the maintenance department. It affects how leadership evaluates uptime, how procurement plans for parts and assets, and how finance views spending on repairs, labor and capital replacement.
Legacy systems can deepen maintenance problems
At RBC Bearings, those issues became clear through the limitations of an aging enterprise asset management system. Fergerson said the company’s previous platform had become difficult to support and too slow to deliver value.
“The biggest pain points with the old system that we had were that it was unsupported,” Fergerson said. “It was old, too. It looked like it came straight out of the dot-com era.”
Speed was also a significant issue. “I would see instances where managers would come in in the morning, start a report and go get a cup of coffee because it would take up to 15 or 30 minutes just to generate a single report,” he said.
Those kinds of delays do more than frustrate users. Over time, they can erode confidence in the system itself and encourage workarounds that further weaken data quality. If technicians or managers do not trust the software to help them do their jobs, they are more likely to bypass it altogether. That means valuable maintenance knowledge either remains siloed or disappears.
For Fergerson, replacing the system meant focusing on usability as much as functionality. He said the evaluation process centered on two priorities: ease of use and vendor support. The maintenance staff who would use the platform every day were experienced professionals, but not all were accustomed to new digital tools.
“I wanted to make sure that I found something that was easy to use,” Fergerson said, describing the need for a system that technicians could learn quickly rather than one that would add complexity to their work.
Technician adoption is critical to better data
That focus on usability is central to whether a maintenance platform succeeds. Ross said technician adoption is a core requirement for building reliable asset data and making maintenance more strategic.
“If people aren’t using the tool,” he said, “they’re just going to work their way around it.” The work may still get done, but the organization loses the data needed to improve planning, measure performance and build confidence in decision-making.
That is why mobile accessibility and technician-first workflows matter. Ross pointed to the use of tablets and phones on the plant floor for scanning QR codes, updating work orders and recording information in real time. Instead of asking technicians to return to a workstation later or to fill out paper forms that may never be digitized, the goal is to make data capture part of the workflow itself.
For manufacturers facing labor shortages and an aging workforce, those design choices are increasingly important. Simpler, better-integrated tools can reduce administrative burden, help standardize documentation and preserve knowledge that might otherwise leave with retiring employees.
A phased rollout can speed results
At RBC Bearings, Fergerson took a staged approach to implementation rather than trying to deploy every feature at once. He began by exporting historical data from the old system, cleaning it up and importing it into the new one. From there, he focused on what he called the “meat and potatoes” of the rollout, which included assets, preventive maintenance templates and the work request system.
That phased strategy helped the company complete implementation in eight months, well ahead of the original two-year timeline. The quicker rollout also helped the plant start realizing value earlier. Technicians were equipped with tablets, which reduced the need to print work orders and spend time on manual administrative tasks. In a department where paperwork can consume valuable maintenance hours, that shift mattered.
“Hundreds of pages of paper are being saved because they can take their tablet,” Fergerson said. “They can go complete their work on the go.”
But the benefits extended beyond the maintenance team. Fergerson said internal customers across the plant responded positively to improved visibility and communication. Requests could be seen, tracked and acknowledged more clearly, which helped rebuild trust in the process.
“I would say right off the bat morale was probably one of the biggest [benefits],” Fergerson said.
Practical AI depends on reliable maintenance data
As manufacturers continue exploring AI, Ross said the most effective use cases in maintenance are likely to be narrowly focused and workflow-specifiat rather than broad, generalized applications.
For example, he described using AI to take a photo of an equipment nameplate and automatically capture key asset information for entry into the system. That kind of capability reduces manual input and speeds asset creation without forcing technicians to learn a separate AI tool or interface.
That distinction is important. In maintenance, AI becomes useful when it eliminates friction, improves data capture and supports better decisions. It becomes noise when it is layered on top of broken processes or poor data quality.
That is also why maintenance can no longer be viewed only as a cost center. With the right systems and data discipline, maintenance performance can shape uptime, throughput and capital planning in ways that directly impact business results.
For manufacturers aiming to modernize, the lesson is straightforward. AI may be the attention-grabber, but maintenance data is still the foundation. Plants that want to move from firefighting to foresight will need systems technicians trust, workflows that support real-time data capture and a practical roadmap for improving maintenance maturity over time.
The Ctrl+Alt+Mfg Podcast
Make sure to check out other episodes of the Ctrl+Alt+Mfg podcast, where hosts Gary Cohen and Stephanie Neil discuss a range of digital transformation insights. The last five episodes are listed below:
Ep. 8: Inside the 2026 State of Automation Report, with Mark Hoske, Control Engineering
Ep. 9: When cyberattacks go physical, with Ian Bramson of Black & Veatch
Ep. 10: Modernization vs. digital transformation, with Dan Furrow and Luis Atencio of Wesco
Ep. 11: What plant engineers really want, with Amara Rozgus of Plant Engineering
Ep. 12: Why system integrators matter more than ever in the age of AI, with Adrienne Meyer of CSIA
