When experience retires: How AI helps manufacturers preserve operational know-how

Posted: February 26, 2026

Across industries, around the world, the workforce is aging. In manufacturing, the trend is building toward a wave of retirements that threatens to crash down on business as usual. By 2030, McKinsey estimates the industry could face a shortage of 1.5 to 2.5 million workers.^[1] And as those workers retire, they take with them something much more difficult to replace than headcount: experience.

Veteran operators don’t just follow procedures. They know what matters, what doesn’t, and what’s likely to happen next. They recognize subtle patterns, anticipate issues, and make tradeoffs under pressure that newer operators may not see.

What do we really mean by experience?

We often treat experience as a sort of abstraction, something earned over time, hard to define, even harder to replace. And yet there’s nothing abstract about its impact. Even in low-complexity roles, high-skilled workers are up to 50% more productive. In high-complexity tasks, the gap can widen to as much as 800%.^[2] Extrapolate that divide to match the scale of workforce turnover soon underway, and the problem manufacturers face is not just a labor shortage, but an existential threat to operational effectiveness.

To understand how AI can blunt the impact of lost experience, it helps to break experience down into its functional components. Seasoned operators consistently outperform their less-tenured peers because they excel at three interrelated functions:

• Knowledge: Accessing the right, contextualized information instantly
• Foresight: Anticipating failures and outcomes before they occur
• Interpretation: Understanding what’s happening, why it matters, and what to do next

In this blog, we’ll explore the ways that industrial AI can help manufacturers embed the functional capabilities of experience—knowledge, foresight, and interpretation—directly into their operations.

Knowledge: Delivering captured information at speed

Industrial AI amplifies the value of information that’s already been captured in systems by making the right information easy to find.

To be successful in their day-to-day work, operators need a wealth of information to draw from. Some of that knowledge lives in their heads, but much of it is captured elsewhere, across data historians, control systems, operational logs, training manuals, and other systems and documents.

Of course, knowledge capture isn’t new. Manufacturers have long documented procedures, recorded operational data, and built repositories of institutional know-how. AI doesn’t transform knowledge capture itself or generate raw information. What it transforms is the value of captured knowledge. You could have all the information in the world, but if you can’t find it in the moment a decision needs making, it might as well not exist.

Rather than a brainpower multiplier, industrial AI functions here as a speed multiplier, radically compressing the time it takes to find the right information and delivering it in context exactly when it’s needed.

In that sense, AI is much like human operators. It requires a robust repository of contextualized data, spanning historians, operational systems, and cloud-based sources, to deliver answers in the moment. The quality of that AI output depends on the quality of the underlying data infrastructure. Without trusted, integrated sources, even the most sophisticated AI can’t provide actionable insights.

Foresight: Anticipating what comes next

Industrial AI can mimic the instincts of experienced operators by continuously predicting failures and process outcomes based on historical data.

After years or decades of experience on the plant floor, seasoned operators develop valuable intuition, a gut feeling. Maybe they recognize, just by a subtle change in sound or vibration, that a machine isn’t behaving the way it should. Maybe they know which assets are most likely to fail after a long shutdown, or which small process deviations tend to cascade into bigger problems. This kind of intuitive foresight is hard to capture in manuals or procedures, but it’s easy to lose as more operators head off to enjoy retirement.

Again, industrial AI can’t generate this gut instinct itself, but it can recreate the end result of instinct: the ability to anticipate. In this case, that AI-powered anticipation comes in the form of advanced and predictive analytics.

The line between advanced analytics and predictive analytics is porous, with overlapping AI and machine-learning techniques, but they tend to be applied in different ways. Predictive analytics is often applied to asset health and maintenance, identifying early warning signs of failure before downtime occurs. Advanced analytics, by contrast, is commonly used to predict process outcomes—such as quality, yield, or energy consumption—and to understand how changes in operating conditions will affect results.

In oversimplified terms, predictive analytics provides operations teams with failure-oriented foresight (something like maintenance intuition), and advanced analytics provides outcome-oriented foresight (something like process intuition). Together, they help operators anticipate both when systems are likely to falter and how processes are likely to behave.

Even better, this AI-powered foresight doesn’t just compensate for the loss of veteran intuition. It operates continuously, at scale, far faster than even the most experienced operator, identifying issues days, weeks, or even months before failure.

Interpretation: Turning signals into judgment

By surfacing what matters, why it matters, and what to do next, industrial AI supports operators as they interpret complex information in real time.

Over the course of their careers, veteran operators develop the skill of interpretation: a quickness to understand what’s happening, why it matters, and what to do next. Interpretation is core to confident, situational judgement, including the ability to:

• Know what to act on and what to ignore 
• Prioritize tradeoffs under pressure and weigh consequences in context
• Intuit causation rather than just correlation 
• Turn complex or conflicting signals into contextualized understanding and action 

On the plant floor, interpretation is everywhere, but that doesn’t mean it’s easy to formalize and pass along to new operators. From an organizational perspective, it lives between systems. From an operator’s perspective, it lives at the crossroads of memory, comparison, and synthesis. It’s rarely written down, and it happens fast, even reflexively. As systems grow more complex and data volumes continue to rise, even skilled operators will struggle to access and synthesize information quickly enough to make confident decisions in the moment.

Interpretation has traditionally been transferred through mentorship: learning beside someone who can answer questions in the moment, explain tradeoffs, and connect current situations to past experience. Mentorship, however, is difficult to scale. It draws on the limited attention of experienced operators and largely passes judgment along one operator at a time.

This is where AI-powered decision support tools can help fill the gap. These AI-powered solutions—such as generative chat tools and AI assistants—are just beginning to emerge on the market and onto production floors. They allow operators to ask plain-language questions about current conditions, recent changes, or likely causes, and receive responses rooted in operational data and analytics. Rather than adding more dashboards or alerts, these tools help interpret signals, surfacing what matters most, why it matters now, and how it relates to similar past situations.

In effect, these tools can function like always-available digital assistants. When time is short or situations are unfamiliar, AI-powered decision support can give operators the instant context they need to make better, more informed decisions.

Human judgment, amplified by AI

Experience takes time to build, and time is something manufacturers increasingly don’t have. Even if the talent pipeline were overflowing, today’s plants, processes, and supply chains are evolving at a pace that often outstrips the time it takes operators to build deep, firsthand experience.

Industrial AI can’t automate experience. But it can scale its impact by putting an organization’s captured knowledge and insights to work in the moments decisions are made, and ensuring that hard-earned lessons don’t walk out the door at the end of a shift or disappear at the end of a career.

^{[1] Perez, F., Salguero, J., Lambert, D., Bristol, H., & Freeman, T. (2024, September 17). Is your frontline workforce strategy right for where you are? McKinsey & Company. https://www.mckinsey.com/capabilities/operations/our-insights/is-your-frontline-workforce-strategy-right-for-where-you-are
[2] Weddle, B., Mellors, N., Malinauskaite, A., Voelker, A., & Cassady, S. (2025, July 2). Investing in the manufacturing workforce to accelerate productivity. McKinsey & Company. https://www.mckinsey.com/industries/aerospace-and-defense/our-insights/investing-in-the-manufacturing-workforce-to-accelerate-productivity}