The industrial automation landscape is witnessing a fascinating convergence this week as AI in industrial automation takes center stage in two distinctly different yet equally compelling ways. While Emerson pushes the boundaries of intelligent testing, manufacturers are discovering gold mines in their dusty filing cabinets.
AI Gets Smarter About Testing
Emerson’s latest update to NI Nigel AI represents more than just another software release—it’s a glimpse into how AI in industrial automation will fundamentally change how we approach test and measurement. The addition of code completion and contextual awareness in LabVIEW+ means engineers can now spend less time wrestling with syntax and more time solving actual problems. Having worked with countless test engineers over the years, I can tell you this isn’t just a convenience feature—it’s a productivity revolution waiting to happen.
What’s particularly interesting is how Emerson is positioning this as industry-first AI specifically optimized for test applications. Generic AI tools are great, but industrial testing has its own unique vocabulary, patterns, and failure modes. A specialized AI that understands the nuances of test automation could dramatically reduce the learning curve for new engineers while helping seasoned professionals catch edge cases they might otherwise miss.
The Hidden Treasure in Your File Cabinets
Perhaps even more intriguing is the growing recognition that manufacturers are sitting on decades of untapped intellectual property. Those yellowing manuals, hand-drawn schematics, and typewritten failure reports aren’t just corporate archaeology—they’re training data for the next generation of industrial AI systems. The article about activating 50-year-old documentation hits on something I’ve seen repeatedly: the institutional knowledge walking out the door with retiring engineers could be partially captured and digitized.
Think about it from a practical standpoint. Your plant probably has documentation from equipment installations, modifications, and repairs going back decades. That’s a treasure trove of information about what worked, what failed, and why. Modern AI systems could potentially analyze these patterns to predict maintenance needs or suggest optimization strategies based on historical precedents.
Meanwhile, the industry continues its relentless push toward miniaturization and efficiency. Digid’s nanoscale sensors and NanoIC’s sub-2nm process design kits represent the hardware foundation that will enable the next wave of AI in industrial automation. When sensors become virtually invisible and processing power continues its exponential growth, we’ll see AI capabilities embedded in places we never imagined possible.
The question isn’t whether AI will transform industrial automation—it’s whether your organization is preparing for the transition. Are you cataloging your legacy documentation? Training your teams on AI-enhanced tools? Most importantly, are you thinking strategically about where AI can solve real problems rather than just following the latest trends?