Knowledge Exodus From the Silver Tsunami in the Industrial Products Sector: It Is AI or Bust

The Factory Floor Is Emptying

Here’s the number that should be keeping industrial products executives awake at night: 52.4 percent. That’s how much of their workforce is expected to retire or leave within the next five years, according to a recent survey of industrial products organizations by APQC. Pundits call it the “Great Retirement” or “The Silver Tsunami.”

We’re not talking about easily replaced positions. We’re talking about process engineers who can troubleshoot complex production systems at a glance. Equipment specialists who know the quirks of every machine on the floor. Quality managers who’ve spent decades understanding material tolerances and failure modes. That expertise is literally walking out the door as you read this article. Additionally, next-gen workers prefer to work in “cooler” industries, creating an impending talent and knowledge crisis in this sector.

Concerned Inaction

The C-suite is aware of this crisis, with a whopping 83 percent of C-suite executives considering knowledge loss a “mission-critical,” “moderate,” or “strong” concern. Yet, a mere 7 percent capture knowledge from departing retirees consistently.

When industrial companies do attempt knowledge capture, they are using antiquated methods with a staggering 74 percent relying on people-to-people expertise transfer—hoping that experienced workers will remember to pass along critical operating knowledge to colleagues before their retirement party. Only 9 percent of companies in this sector leverage AI to automatically mine enterprise conversation stores and capture trusted answers from experts. In an industry racing toward Industry 4.0, smart factories, and IoT-enabled operations, this analog approach to knowledge management is a glaring contradiction. What is blocking the knowledge capture from departing employees? Not surprisingly, lack of time and resources top the list because manual methods of knowledge capture are neither sustainable nor scalable.

The AI Paradox

While there is a lot of excitement about AI to automate knowledge management and other business processes, there are barriers to adoption as well. Top concerns in this sector when it comes to AI-enabled knowledge automation include incorrect AI-generated answers, data privacy and security issues, unclear use cases, and compliance. These are legitimate concerns in an industry where knowledge gaps can mean equipment failures, safety incidents, or production shutdowns. But the solution isn’t to avoid AI—it’s to implement AI properly, layered over a trusted, well-governed knowledge infrastructure that ensures AI-generated answers are correct, compliant, and consistent. AI and knowledge are BFFs—AI automates knowledge, and a knowledge infrastructure makes AI trustworthy and successful.

Use Cases Show the Path Forward

The precious few industrial leaders who have already operationalized AI to enable knowledge management use it to automate knowledge discovery, followed by curation and analytics. These use cases demonstrate AI’s power to automatically find, organize, and optimize knowledge, which has proven to be a Sisyphean endeavor.

Benefits they quoted from the use of knowledge include better decision making, reduced cycle times, and streamlined processes. In industrial products, where operational efficiency and manufacturing excellence drive profitability, these improvements directly translate to a competitive advantage.

Preparing the Workforce

AI-savvy industrial companies aren’t just deploying technology—they’re preparing their people. According to the survey, a vast majority of them plan to upskill their existing workforce, while a little over half of them are applying change management strategies while hiring AI-skilled talent.

This human-centered approach matters—you can implement the most sophisticated AI knowledge systems, but if your workforce doesn’t adopt them, you’ve created expensive shelfware (or cloudware in today’s age!) while continuing to lose the expertise that drives your operations.

The Window Is Closing

Every day, decades of manufacturing wisdom, troubleshooting expertise, and process knowledge leave with retiring workers. The answer isn’t more manual documentation or rushed exit interviews. It is a fundamental shift to AI-enabled knowledge capture systems that collect expertise continuously, automatically, and at scale—regardless of when employees retire or leave.

The question isn’t whether you can afford to invest in AI-powered knowledge retention. It’s whether you can afford to keep losing what makes your operations efficient, your products reliable, and your company competitive.

The Great Retirement is already underway. In five years, half your expertise will be gone. What are you doing about it today?