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AI is moving at breakneck speed, but the plant floor still runs on trust, habit, and hard-earned routines. We sit down with Vinit to unpack the real challenge behind “digital transformation” in reliability: designing tools that work for an experienced workforce that values buttons, PDFs, and proven processes, while also meeting the expectations of early-career engineers who want fast answers, mobile access, and connected systems.

We get practical about what actually closes the gap. Instead of big-bang rollouts, we talk roadmaps, small experiments, and pilots that demonstrate value without risking operations. We also draw a firm line around safety and accuracy: in reliability engineering, there is no room for hallucination. A fault has to be a fault, and any AI-assisted workflow must be validated, secure, and compliant across industries and markets.

From there, we zoom out to connected reliability and why data trapped in folders and silos limits predictive maintenance, pattern recognition, and decision-making. We explore how central platforms such as CMMS and connected ecosystems enable sharing, faster analysis, and better prioritisation, even when legacy equipment and mixed vendors complicate integration. We also touch on how “retro” tech keeps a foothold, and why value is better measured by outcomes than by the physical box in your hand.

If you care about asset reliability, maintenance strategy, industrial IoT, condition monitoring, and the culture that makes change stick, you’ll take plenty from this conversation. Subscribe for more, share it with a colleague who’s wrestling with adoption, and leave us a review with your answer: what would make new tech truly trustworthy in your plant?

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Your CMMS probably knows more about your plant than any one person does, but most of that information is buried in reports, menus and half-completed work orders.

While we were at Accelerate 2026, we sat down with Jay Hack to look at four practical ways generative AI is being used to make maintenance and reliability work easier, faster and more accurate.

The first was the ability to simply talk to your data inside eMaint. Instead of needing to know how to build reports or search through different parts of the system, you can ask a normal question and get an answer back from the CMMS.

But as Jay explains, the AI itself is not necessarily the hardest part. The real challenge is making sure the right people have access to the right information, especially across different sites, departments and levels of the business.

We then looked at two areas that could make a real difference on the plant floor.

The first is automated SOP generation. The system can scan OEM manuals and technical PDFs and turn that information into practical procedures that can be added to work orders. There is still a human involved in checking and approving the content, but it could save a huge amount of time and help improve consistency.

The second is voice-based work requests. Technicians and operators can speak naturally into the system, even while they are out on the plant, and the CMMS can then populate the relevant fields.

Instead of receiving a work request that just says “pump broken”, you can capture what the operator saw, heard or experienced and create a much better maintenance history.

We also discussed how AI could support global teams by searching document libraries, translating manuals and helping standardise maintenance and reliability practices across different sites and countries.

Looking further ahead, there is also the potential for AI-powered competency mapping, using a person’s actual work order history and experience to better understand skills and identify development gaps.

This was a really practical conversation about where AI can genuinely support maintenance teams, rather than just adding more technology for the sake of it.

If you are interested in AI in maintenance, CMMS adoption, preventive maintenance and digital transformation that actually works, give this episode a listen.

What is the first maintenance workflow you would want AI to improve?

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Vibration analysis can be the difference between planned work and an expensive surprise, but only if you collect the right data in the right way. After a week packed with fresh ideas from Fluke’s Accelerate event in Austin, we sit down and get practical about what vibration monitoring can detect, what it struggles with, and why so many programmes fail on strategy rather than technology.

We unpack the fundamentals that quietly shape every result: how long you capture the time waveform, how high your frequency range needs to be, and what that means for bearings, gears, envelope analysis, and early fault detection. From there we compare the three big acquisition routes. Handheld data collection brings high-resolution flexibility and the huge advantage of having an engineer at the machine, but it can become inefficient when experts spend time gathering data on low-risk assets or when the plant isn’t running on survey day. Wireless vibration sensors can fill the gaps with better trending and easier installation, yet they come with real-world constraints: connectivity dropouts, battery life, limited frequency response, and devices that often collect data without understanding load or running state.

Then we make the case for wired continuous monitoring on the most critical equipment: stable sensors in the load zone, long captures for slow-speed machinery, smarter alarms tied to operating conditions, and far fewer blind spots. We also talk capability building, from training teams to collect repeatable data to why you should be cautious of black-box AI recommendations if nobody on site understands the basics.

If you want a vibration analysis strategy that actually reduces downtime, subscribe, share this with your maintenance team, and leave us a review. What assets are you trying to protect right now?

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