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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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