Manufacturers pour capital into AI, analytics, and condition monitoring every year. Most of that investment underperforms. The reason is rarely the technology itself - it is the absence of a coherent asset management foundation that connects data to the people who make maintenance and operational decisions.
An upcoming IndustryWeek roundtable, "Beyond the Hype: Building an AI-Ready Enterprise Asset Management Strategy," will examine what it takes to close that gap. The session features Tim Gilmer, Senior Industry Consultant at Octave, and Chris Colson, Vice President of Reliability Solutions at Allied Reliability. Together they bring more than six decades of experience in asset reliability, EAM software implementation, and large-scale operational improvement.
The discussion targets a central tension in industrial AI adoption. Condition monitoring sensors and predictive models generate signals, but those signals only create value when they reach a planner who schedules the right work, a technician who executes it correctly, and a manager who funds the right spare parts. Without that connective tissue, intelligence stalls.
What belongs in place before scaling AI
Gilmer and Colson will map the prerequisites that determine whether an AI initiative gains traction or quietly fades. Asset criticality rankings, documented failure modes, and clean data governance standards are not optional prerequisites - they are the difference between a pilot that proves a point and one that changes how a plant runs.
Colson has spent 32 years treating reliability as a capital-efficiency lever rather than a maintenance cost center. His work spans process, discrete, and heavy-asset industries including oil and gas, primary metals, mining, food and beverage, and pharmaceuticals. At Allied Reliability, he leads the Reliability Solutions practice after previously building enterprise-scale reliability organizations of more than 200 professionals across four continents.
Gilmer's three decades of experience center on EAM software implementations, maintenance best practices, and the process standards that let organizations scale without breaking their own workflows. He has led reliability services organizations and managed large-scale projects across multiple industries, focusing on data governance that delivers measurable cost savings.
Where AI creates value across the asset lifecycle
The roundtable will address a practical question executives face: where exactly does AI earn its keep? The answer varies by maturity. Some operations need better failure mode detection. Others already have the data and need the work execution link. Attendees will learn how to identify the points in the asset lifecycle - from design and procurement through operation and retirement - where analytics create meaningful value rather than just more dashboards.
Colson's track record includes a 32% revenue increase in a key business unit over three years and technical advisory roles on three acquisitions that more than doubled in revenue afterward. His client list includes Enbridge, Mosaic, Alcoa, PepsiCo, Pfizer, and Cargill. He said the common thread across those engagements is connecting reliability programs to measurable business outcomes: higher uptime, lower total maintenance cost, extended asset life, and stronger capital efficiency.
Avoiding the traps that stall initiatives
Technology and reliability programs stall for predictable reasons. The roundtable will surface the most common ones: starting with technology selection before defining the problem, ignoring the workflow changes required to act on new data, and failing to demonstrate value early enough to sustain executive sponsorship. Gilmer and Colson advocate starting small, proving the model on a critical asset or line, and building toward enterprise adoption on the back of demonstrated results - not PowerPoint promises.
The session also connects to broader industry standards. Colson chaired the Society for Maintenance & Reliability Professionals' M&R Body of Knowledge Committee from 2010 to 2013, shaping the technical standard the industry certifies against. He now serves on the organization's Government Relations Committee and contributes to the Reliability Leadership Foundation's Sustainability Consortium for Reliability and Asset Management.
Why this matters for executives and strategy leaders
The core message of this roundtable is that AI without an asset management strategy is just expensive noise. For executives allocating capital and setting operational priorities, the takeaway is practical: audit the foundation before funding the next technology wave. If your asset criticality rankings are outdated, your failure mode data is incomplete, or your planners cannot act on the insights your sensors already generate, no AI model will fix that. The organizations that get this right treat asset management strategy as a prerequisite to technology investment - not an afterthought - and they measure success in uptime, maintenance cost per unit of output, and capital efficiency, not in dashboards deployed.
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