LCTS Industry Insight
Industrial companies are facing a challenge that cannot be solved simply by adding more technology:
How do you preserve decades of operational knowledge when experienced engineers and operators retire?
A recent Chemical Processing article, “AI and Digital Twins Race to Capture Vanishing Plant Expertise,” explores how energy and chemical companies are using AI copilots, digital twins, 3D models, and immersive training to capture and transfer the knowledge of experienced personnel.
Read the full article from Chemical Processing
For LCTS, this raises an important engineering question:
Can technology capture experience—or only the information that experience leaves behind?
The Industrial Knowledge Gap
Experienced operators and engineers often carry knowledge that may never be completely documented in procedures or manuals.
They may know:
- How a particular unit behaves under unusual conditions
- What changes in equipment behavior can indicate
- How different feedstocks affect operations
- What happened during previous startups and shutdowns
- How equipment responds as it ages
- Which solutions worked in real operating conditions
This is often referred to as tacit knowledge—knowledge developed through years of practical experience.
When experienced personnel leave, some of that knowledge can leave with them.
That creates more than a workforce challenge.
It can become an operational and reliability challenge.
Can Technology Preserve That Experience?
The Chemical Processing article highlights how companies are turning to digital twins, AI copilots, video-based training, and immersive technologies to make plant knowledge easier to capture and transfer.
Digital technologies can help connect equipment with:
- Operating procedures
- Maintenance information
- Training materials
- Historical information
- Equipment models
- Operational data
AI can also make large amounts of information easier to search and analyze.
These technologies can make knowledge more accessible.
But accessibility is not the same as expertise.
AI Can Support Knowledge Transfer—But Human Judgment Still Matters
One of the most important considerations is how AI is used.
AI can analyze information, identify patterns, and support decision-making.
But industrial operations still require people who understand the real-world environment.
A digital system may identify an abnormal trend.
An experienced operator may recognize why that trend is happening.
A model may recommend an action.
An engineer still needs to determine whether that recommendation makes sense given the actual condition of the facility.
AI can provide information. Engineering judgment provides context.
More Data Does Not Automatically Mean Better Decisions
Modern industrial facilities generate enormous amounts of data.
Sensors, control systems, monitoring platforms, and digital tools can provide valuable information about equipment and processes.
But organizations still need to ask:
- Is the data accurate?
- Does it represent actual operating conditions?
- Are there gaps in the information?
- What assumptions are being made?
- What does the data mean in the context of the process?
Without the right engineering context, even sophisticated analytics can produce limited value.
The goal should not simply be to collect more information.
The goal is to turn information into better decisions.
Training the Next Generation
Technology can also help organizations transfer knowledge to newer engineers and operators.
Experienced personnel can contribute through:
- Recorded troubleshooting sessions
- Lessons learned
- Startup and shutdown scenarios
- Simulation exercises
- Equipment case studies
- Site-specific training
Digital tools can then make this information easier for the next generation to access.
This creates an opportunity to combine experience with technology rather than treating them as competing approaches.
The objective is not to create a digital replacement for experienced personnel.
It is to help the next generation learn faster while preserving knowledge that took decades to develop.
The Risk of Losing Institutional Knowledge
Consider a simple question:
If an experienced operator leaves tomorrow, how much of what that person knows is actually documented?
The answer may reveal a significant gap.
Some information may exist in manuals.
Some may exist in maintenance records.
Some may exist in databases.
But some may exist only in the experience of the person who has spent years working with the facility.
That knowledge can influence decisions involving:
- Equipment behavior
- Process changes
- Troubleshooting
- Reliability
- Startup and shutdown
- Abnormal operating conditions
Preserving that knowledge should therefore be considered part of long-term asset management.
The LCTS Perspective
At Lucke Consulting Technology Services (LCTS), we believe technology is most valuable when it strengthens engineering and operational decision-making.
AI, digital twins, analytics, and other digital tools can help organizations capture and use information more effectively.
But successful industrial operations still require:
Data + Technology + Engineering Expertise + Operational Experience
Bringing these elements together can help companies preserve institutional knowledge while improving the way future engineers and operators make decisions.
What Should Industrial Companies Be Asking?
Instead of asking only:
“How can we use AI?”
organizations should also ask:
- What knowledge are we at risk of losing?
- Which expertise exists only in individuals?
- How can we capture lessons learned?
- Can our current digital systems preserve operational knowledge?
- How will experienced personnel validate AI-generated recommendations?
- Are new engineers being given enough practical context?
These questions shift the conversation from technology adoption to long-term operational capability.
Final Thought
AI, digital twins, and immersive technologies can become powerful tools for preserving industrial knowledge.
But technology should not be viewed as a replacement for experience.
The stronger approach is to combine technology with the people who understand the process, the equipment, and the realities of operating a complex facility.
The goal is not simply to preserve information.
The goal is to preserve the ability to make good decisions.
How LCTS Can Help
As industrial facilities become more complex and experienced personnel transition out of the workforce, organizations need practical engineering insight to evaluate their assets, processes, technology, and operational risks.
LCTS provides independent engineering and technical consulting services designed to help industrial organizations make informed decisions, improve performance, and manage complex operational challenges.
Need another set of eyes on your industrial project or operation?
Contact LCTS to discuss your engineering challenge and identify practical opportunities for improvement.
Visit Lucke Consulting Technology Services
SOURCE
Jennifer Markarian, Chemical Processing, “AI and Digital Twins Race to Capture Vanishing Plant Expertise,” September 14, 2026.
