Smart Industry

The Computerized Maintenance Management System has been marketed as the ultimate cure for industrial maintenance woes. Many organizations invest heavily in these platforms under the assumption that the software itself will solve their reliability problems. However, a CMMS is not a magic wand. It is a sophisticated tool that is only as effective as the discipline of the organization using it. When talking with other leaders or clients, I often like to compare a CMMS to a file cabinet.

For years, the conversation surrounding the industrial Internet of Things (IIoT) and predictive maintenance has clustered at two extremes. On one end is the vendor pitch: connected everything, AI-powered insights, and the total elimination of downtime. On the other is the plant-floor reality: “legacy” equipment, integration headaches, workforce skepticism, and pilots that never quite scale.

THAT day has finally arrived, where Smart Industry and our sponsor Cisco reveal the nine organizations that have won our Industrial Transformation Awards for 2026—the inaugural year for these important accolades—in three categories, Manufacturing, Transportation, and Utilities.

AI is stepping up and changing its surrounding world. Physical AI, systems that can perceive, monitor, decide, and act in the real world, are attracting increasing scrutiny. This attention largely comes from the promise of physical AI to maximize efficiency and margins as well as its scalability. But the current conversation around physical AI’s potential role in manufacturing is relatively polarized.

Enterprise AI adoption is accelerating. Budgets are being reallocated. Roadmaps are being rewritten. Boards are demanding measurable outcomes from these adoptions. But beneath the excitement lies an uncomfortable truth: For decades, most companies have drifted away from value and toward process. AI is now forcing corrections, however. This moment isn't just about automation or productivity gains.

When an AI initiative stalls or a supply chain integration breaks down, IT is usually the first phone call. I've watched this happen across enterprises in manufacturing, distribution, and logistics: Something fails, executives want answers, and IT absorbs the fallout. But after more than two decades working on enterprise data infrastructure, I've come to believe that most "IT failures" are actually data failures in disguise.

Smart manufacturing enhances production, streamlines workforce management, and reduces costs. While efforts to embrace smart manufacturing practices can have transformational benefits, older “legacy” infrastructure and clunky systems may undermine their effectiveness by creating data silos and bottlenecks as well as consuming more power. Such inefficiencies serve to defeat the purpose of digital transformation and hamstring sustainability efforts.

Why manufacturing leaders are missing the true drivers of lost production. What you’ll learn: More than half of manufacturers report that excessive downtime has impaired their ability to meet production targets or shipping deadlines in the past year. When operational data is delayed, incomplete, or fragmented across systems, small disruptions escalate into extended stoppages. Manufacturers are increasingly investing in predictive maintenance, AI, and advanced analytics.

Manufacturers are exploring artificial intelligence to find ways to improve operational decision-making, optimize processes, and predict equipment failures. The potential benefits aren’t hard to see: In theory, this leads to improved efficiency, fewer unplanned outages, and tighter control of product quality.

Samuel Pasquier of Cisco returns to the pod, this time to field questions about his company’s new report that sees a widening AI execution gap and strain that adoption is placing... Relay co-founder and CEO Chris Chuang joins the pod to delve into another corner of artificial intelligence in manufacturing: How voice is being turned into data, ending the disconnect...