Additive manufacturing (AM) has long relied on a deep well of expertise that is notoriously hard to document.
That expertise can be found in the nuances involved in setting up machines. The calibration of specific parameters. The unspoken understanding of how a material behaves under different environmental conditions. And it’s in the insights about why a part that appears flawless in CAD might fail halfway through a build.
Much of what enables 3D printing to succeed in real-world applications isn’t written in manuals but is held within the minds of experienced operators, engineers, and technicians.
That reliance on tribal knowledge has become a liability for the industry, especially as the manufacturing sector faces an imminent workforce crisis.
By 2033, manufacturing is expected to report 3.8 million new job openings, but only about half of those positions are projected to be filled. Even more concerning, roughly three-quarters of that talent gap is expected to come from retirements, taking decades of hands-on machine and process knowledge with them.
Additive manufacturing, still grappling with a shortage of experienced operators, will feel this more acutely than most. However, there is a tool at the industry’s disposal that can soften the impact. If implemented strategically, AI has the potential to retain the decades of AM expertise that will soon be walking out the door.
Why Additive Manufacturing Is Especially Exposed
Additive processes are evolving at a rapid pace. While certain standards exist, we know that best practices can vary significantly by machine model, material batch, software version, and even operator intuition. Two 3D printers with identical specs can behave drastically differently depending on the individual operating them.
As a result, AM organizations frequently rely on a small number of “go-to” experts who know how to diagnose failures, tweak parameters, or qualify parts. These individuals become the glue that holds production together. When they retire or move on, operations can come to a grinding halt. New hires may take months or even years to reach the necessary level of productivity, which impedes a company’s ability to scale. So, this is not just a hiring problem – it’s about enabling knowledge continuity.
Documentation Alone Isn’t Enough
For years, the common solution has been better documentation, such as more SOPs, checklists, binders and PDFs. While this approach has its place, it clearly falls short in additive manufacturing.
Anyone can document a process, but documenting the ‘why’ behind a process is far more complex. It requires an understanding of why it works, when it fails, and how to adapt it in edge cases – the type which can cause serious equipment malfunction or failure. Much of that insight is experiential, learned over time, and deeply contextual, making it nearly impossible to transfer through traditional documentation.
That’s where a new approach, predicated on AI, is emerging and offers substantial returns for those willing to adopt it.
AI as a Knowledge Multiplier, Not a Replacement
Perhaps the most promising use of AI in additive manufacturing isn’t autonomous printing or generative design, but knowledge capture and accessibility.
Traditionally, expertise is viewed as something that must be passed on from one individual to another over years of mentorship. We see this particularly in manufacturing, where human oversight has been relied on since inception and still to this day. Now the industry must ask itself how it can prevent the inevitable haemorrhaging of intelligence as key people leave. Leveraging AI systems to organize, contextualize, and surface institutional knowledge would unlock the ability to preserve this expertise on an unprecedented scale.
Let’s look at how it works.
AI can make use of historical build data, tying specific machines, materials, and outcomes together to create a comprehensive record that operators and engineers can easily reference. It can also connect process decisions and parameter adjustments directly to the success or failure of a print, offering insights into what works and what doesn’t.
Expert annotations that explain the rationale behind key decisions can also be easily incorporated, helping to capture the context behind each choice.
AI can also identify patterns across various jobs. This brings to light hidden connections and insights that would otherwise go unnoticed – something that no single person could track on their own.
When all of this information is made structured and searchable, it transforms scattered, disjointed knowledge, which was once confined to the minds of a select few individuals, into a constantly evolving and accessible reservoir that everyone can draw from.
The impact on the workforce would be far-reaching. New engineers would be able to learn faster, and operators would make better decisions with guidance rooted in years of experience. Experts would also spend less time answering recurring questions and more time solving genuinely novel problems.
Crucially, this doesn’t deskill the workforce. It raises the baseline while allowing room for deepening expertise.
A More Resilient Additive Workforce
As additive manufacturing evolves from experimentation to mainstream production, the need for resilience becomes paramount. A big part of that is minimizing dependence on any single individual and ensuring that knowledge is preserved, even as roles change or workers retire.
AI-driven knowledge systems won’t single-handedly solve the labor shortage in additive manufacturing, but they can profoundly enhance how effectively the industry absorbs new talent while preserving intelligence accrued over decades.
In a field where success and failure often hinge on insights gained from years of hands-on experience with specific machines, the ability to share that knowledge could become one of the most significant competitive advantages.
Le’ora Lichtenstein. Image courtesy of Corbel.
The tribal knowledge crisis is already here. The question now is whether the industry continues to restrict expertise to individuals or if it will embrace the AI-powered systems that democratize this knowledge, ensuring it is accessible to the next generation of talent.
About the Author
Le’ora Lichtenstein is the cofounder and CEO of Corbel, an AI-powered CPQ (Configure, Price, Quote) platform that modernizes industrial equipment sales by turning complex product data into intelligent, data-driven quoting and financing workflows. Under her leadership, Corbel has raised seed funding to expand deployments with equipment manufacturers across sectors, including metalworking, woodworking, and additive manufacturing machinery. Lichtenstein has a background in structured credit and early-stage investing, holds a BSc in Finance, and is a CFA charterholder.