What happens when the dominant tools of an industry are fundamentally replaced after nearly a century of stability? We can imagine the unease of a lathe operator watching a steam engine assume the rhythm once driven by a foot pedal. Or the quiet reckoning of a draftsman confronting AutoCAD in the 1980s, as pencil lines gave way to pixels.
Today, additive manufacturing and artificial intelligence are driving a similar inflection point across industrial production. These are not incremental upgrades. They are redefining how products are designed, validated, manufactured and delivered.
But scaling AI-driven 3D printing is not as simple as installing new equipment or deploying software. It requires technical fluency, disciplined experimentation, workforce reskilling and cultural alignment around software-defined manufacturing.
To explore this convergence of AI in manufacturing and additive production, Automation Alley convened leaders from industry, academia, technology and government. The discussion focused on a central question:
Can AI finally move 3D printing from prototype tool to scalable production system?
Rewriting the Economic Model for Additive Manufacturing
Automation Alley COO and Project DIAMOnD CEO Pavan Muzumdar framed additive manufacturing’s long-standing economic tension: it performs exceptionally well in low-volume, high-complexity environments, but scaling introduces cost and qualification challenges.
Yet the technological environment has shifted.
- Lower-cost industrial sensors now enable real-time monitoring.
- Cloud infrastructure supports distributed manufacturing networks.
- Advanced slicing software embeds AI optimization.
- Secure digital workflows enable traceability and part validation.
These capabilities point toward Smart Product Recipes, or SPRs — machine-agnostic, software-defined instructions that standardize additive manufacturing across locations.
Glen Desmier, product manager at Inductoheat, described the structural shift in digital files themselves.
“SPRs the first format is stl, which is just a triangulated file, then came 3mf which contained properties to make the file more intelligent. What if we took it to the next level of intelligence with this Smart Product Recipe?”
He added, “Now we have the client machine and file with all the intelligence.”
The implication is significant. If additive manufacturing becomes governed by intelligent, validated digital recipes, production can move from isolated machines to coordinated networks.
But Desmier warned that even technical precision can be fragile.
“If you simply change the orientation for money and time, you’ve now lost all the strength and properties of that product.”
That kind of embedded process intelligence, he argued, “could be contained into an SPR.” The broader shift from subtractive to additive manufacturing, he said, requires a new paradigm: “All of these technologies improving is the huge paradigm shift we need from subtractive to additive. So, everything should be new.”
3D Printing Adoption: The Gap Between Intent and Scale
Survey data shows additive manufacturing adoption is expanding, but industrial-scale production remains limited.
According to Protolabs’ 2024 3D Printing Trend Report, only 6.2% of respondents reported printing more than 1,000 parts, up from 4.7% the year prior. Growth is measurable, but high-volume additive production remains the exception.
At the same time, Sculpteo’s 2022 State of 3D Printing report found that among advanced “Power Users,” 69% report end-use or functional mechanical parts as a goal of their additive manufacturing efforts.
The ambition is there. Standardization and repeatability remain the challenge.
One barrier, Desmier noted bluntly, is perception.
“One of the things is looking at additive manufacturing like a toy. It’s a huge thing preventing people from taking it seriously since culture is a hindrance right now since culture drives the technical side.”
Culture, he emphasized, is upstream of technology.
“The technology is there, but culture is a huge barrier.”
AI Adoption in Manufacturing Is Accelerating
While additive manufacturing wrestles with scale, AI adoption across manufacturing is moving quickly.
According to the Manufacturing Leadership Council’s 2024 survey:
- 78% of manufacturers plan to increase AI spending within the next two years.
- 46% report already using generative AI tools in manufacturing operations.
Deloitte-referenced survey findings further indicate 55% of industrial products manufacturers are already using generative AI tools.
Even as 78% of manufacturers plan to increase AI spending within the next two years, according to the Manufacturing Leadership Council’s 2024 survey, many organizations still perceive additive manufacturing as experimental.
That contrast — rapid AI momentum alongside additive hesitation — framed much of the roundtable discussion.
Rob Scipione, manufacturing services manager at MMTC, described the acceleration curve facing manufacturers.
“We need continual education around AM and AI. We are on such an acceleration curve of technology right now. The rapid pace of material scien ces as well with what kind of materials to 3d print, beyond plastic.”
He acknowledged that keeping pace is difficult.
“It is tough for organizations to keep up with this rapid pace. It gives way to a lot of misconceptions about what is and isn’t possible.”
Data, AI and the Untapped Opportunity
Much of the discussion turned to data — and the underutilization of it.
“Our conversation started to focus on the fact that every company has more data than they know what to do with,” Scipione said. “There is a huge need for data analytics. Companies often have a ton of data, but it doesn’t tell them anything.”
He connected that reality to the broader Industry 4.0 narrative.
“In the past years with Industry 4.0, we’ve heard that every company needs to become a technology company. Now that is moving into every company needs to become an AI company.”
The reason, he argued, is practical.
“AI is what can help take this mountain of data and turn it into something that is actionable.”
Fadi Abro, global director of transportation at Stratasys, reinforced that AI’s value extends beyond design optimization.
“AI is a great tool to help with design but we were talking about how it can really help in finding out ROI.”
He also highlighted workflow compression as a competitive advantage.
“Workflow is really important, and we can use AI to shorten the gaps where it makes sense for that option.”
Standards, Scanning and OEM Expectations
From an OEM perspective, Abro outlined three missing elements slowing additive scale.
“What’s missing from an OEM perspective? Awareness, confidence and standards.”
Awareness, he said, can be expanded through ecosystem-building events that bring small and mid-sized manufacturers into the conversation.
Confidence, however, depends on repeatable standards.
“There needs to be more standards and uses developed with additive manufacturing, and that helps us understand what we need to do to fulfill their needs.”
He also pointed to scanning and validation gaps.
“Scanning is a big gap today. How do we make sure the parts will be strong enough.”
The uneven adoption landscape is visible across supply chains.
“GM doesn’t need help with developing its additive strategy,” Abro said, “but I can name million plus parts a year suppliers that have nothing to do with additive manufacturing at all.”
Even at the highest levels of industry, uncertainty remains.
“We have some of the largest companies in the world talking about these same problems as we are today.”
Education, Culture and the Human Element
If technology is advancing rapidly, culture is advancing incrementally.
Desmier sees the transition from multiple vantage points.
“I am fortunate to be on both sides as a manufacturer and professor.”
He emphasized that adoption hinges not just on technical skills but soft skills.
“What brings about the synergy between training skills and culture? You need soft skills. You need soft skills to bring in adoption. Slowly and incrementally bring about change. That is key.”
He added, “We need education and training, but also soft skills.”
Scipione echoed that concern.
“No matter the industry, whether you work for industry or a university, change management is so important because the technology is changing so fast. How do you change and how do you create a culture in line with this rapid pace?”
He cautioned against focusing exclusively on hardware and software.
“We can keep talking about technology, but what is missing is the human element related to this change.”
Diversification, Capability and Market Direction
Padma Kuppa, chief information officer at Project DIAMOnD, raised another strategic question: market positioning.
“We talked about diversification and what the market is looking for. With 3D, there is so much capability from printing things that are small all the way up to the hull of a boat. Where does a company land when figuring out what customers need?”
Capability alone does not determine success. Access and skill matter.
“Also, how do we make sure people have accessibility to CAD tools and the know-how to use them?”
Desmier returned to the technical foundation beneath those questions.
“CAD systems, everything we are seeing today is built in silos. Getting CAD systems talking to each other is difficult.”
Interoperability, standards and intelligent file formats may ultimately determine how quickly additive manufacturing integrates into mainstream production.
The Future: Software-Defined, AI-Driven Manufacturing
The roundtable reinforced a central insight: additive manufacturing’s future is software-defined.
Intelligent digital files, validated workflows and AI-driven analytics are reshaping how production systems are designed and coordinated. The shift from subtractive to additive manufacturing is not merely technical — it is architectural.
As Desmier put it, “How do we get the culture and the mindset to change? All of this will be small incremental successes to take us up to that point with SPRs and SPR-compliant machines.”
AI adoption statistics suggest manufacturers are leaning forward. Additive manufacturing now sits at the convergence point — where digital intelligence meets physical production.
The companies that succeed will not simply purchase equipment.
They will build cultures capable of adapting to it.