The Enduring Relevance of Fundamentals in the Age of Generative AI
“Generative AI is a type of artificial intelligence that can learn from and mimic large amounts of data to create content such as text, images, music, videos, code, and more, based on inputs or prompts.”
— Harvard University (source)
Generative Artificial Intelligence (Gen-AI) has become a daily headline. A quick search on any social platform yields a flood of content—yet barely scratches the surface of what's being explored. Across industries, Gen-AI is already being used to boost performance through cost reduction, increased manufacturing efficiency, and greater employee satisfaction. The impact is real and growing.
In asset-heavy industries—such as manufacturing, shipping, aviation, mining, and energy—Gen-AI has only begun to reveal its potential. Its role in improving reliability is accelerating, and organizations are racing to tap into the growing pool of AI talent emerging from universities. The pressure is on to effectively integrate Gen-AI into operations.
Already, Gen-AI is helping companies understand the root causes of operational failures, better predict and prevent future breakdowns, and optimize system performance. In many cases, it's becoming central to reliability strategies. But for all its promise, Gen-AI does not replace the timeless principles that drive operational excellence. Instead, it highlights an inconvenient truth: fundamentals still matter.
Decades-old reliability practices remain as essential today as ever. Gen-AI reinforces their value and applied correctly can accelerate improvement and increase impact.
Reliability Fundamentals: Then and Now
Terry Wireman, in his work Benchmarking Best Practices in Maintenance Management (published over 20 years ago), laid out foundational concepts such as maintenance cost, wrench time, work order systems, backlog management, and preventive programs. These building blocks continue to define reliability today.
In Lean Maintenance, authors Ricky Smith and Bruce Hawkins walk through the history of Lean, defining maintenance’s role within it and offering practical guidance for applying Lean principles to maintenance operations. Their framework remains a reference point for continuous improvement.
Joel Levitt’s The Handbook of Maintenance Management also emphasizes fundamentals, offering tools to assess and strengthen maintenance strategies.
The roots of this philosophy stretch back even further. In the 1970s and '80s, Seiichi Nakajima introduced Total Productive Maintenance (TPM), as documented in Introduction to TPM – Total Productive Maintenance (1988). His work expanded on the 1950’s principles of planned maintenance and defined organization wide actions required to drive the highest performance. As Nakajima stated, “TPM is profitable,” and it must be “performed on a company-wide basis.”
The Foundations Still Hold
While the tools at our disposal have evolved—analytics engines are far more advanced, and AI applications more prolific—the core ideas remain unchanged. Industry leaders must deploy a strategy that leverages the latest in Gen-AI and the fundamentals. Many organizations struggle to deploy and sustain the time tested principles required to perform at their best every day, not for lack of available knowledge on the topics, as noted above. These organizations often fail to fully understand the potential impact and hard work required. To start, they must:
Understand current operational performance
Quantify the cost of unreliability
Identify the levers for performance improvement
Build a comprehensive improvement plan to drive performance
Align stakeholders across all levels of the organization
Implement with rigor and full leadership engagement
In short, the path to reliability is not paved solely with cutting-edge tools. It’s built on consistent application of time-tested principles—now enhanced, not replaced, by the capabilities of Gen-AI.
Final Thoughts
Today’s AI advancements should not distract us from what truly drives sustainable performance. Leaders must resist the temptation to chase trends at the expense of fundamentals. Instead, they should engage the entire organization to use Gen-AI as a force multiplier—one that strengthens, not supplants, the core practices that have long underpinned operational success.