What will it take to build intelligent, connected, and truly autonomous product lifecycle systems?
I attended ACE 2025, a community event hosted by Aras Corp in Boston. A vision started by Peter Schroer has grown into an amazing community of PLM enthusiasts and supporters who have built businesses and developed solutions using the Aras Innovator platform.
The keynote on the second day was delivered by Rob McAveney, CTO of Aras. Rob reflected on the “What if” questions that Aras developers have been asking over the past two decades while building the Aras Innovator platform. Michael Finocchiaro captured some great insights from Rob’s presentation here.
The question Rob asked this year was about PLM singularity. It posed a bold and exciting question: Are we approaching a PLM Singularity?
Imagine a future where all product data, processes, and decisions are unified across the digital thread. A system where AI, real-time collaboration, and automation drive every step of the product lifecycle—from early design to end-of-life. A kind of “holy grail” for product development.
I wanted to reflect on the Rob’s presentation. It made me think about how far we’ve come and how much further we still need to go.
Because the truth is, we’re not there yet.
Most of today’s PLM platforms are still rooted in old tech and organizations are still stuck in the old thinking. To reach the singularity moment, we need more than just better tools—we need to rethink the entire foundation of PLM.
In my article today, I want to speak about seven key changes that I believe necessary to make this leap to PLM singularity.
1. PLM Data Models Need a Reboot
The complexity of modern product development and manufacturing is skyrocketing. At the same time, most current PLM systems are built on SQL-based object models developed over 20 years ago. These architectures are rigid and don’t scale well for today’s data needs.
What if we reimagined PLM data using modern graph-based models? With graphs, we can model relationships, dependencies, and systems in a way that opens the door to advanced analytics and AI. We can move from limited data models to real-time, connected intelligence.
2. Openness and Interoperability Must Be Real
PLM vendors love to say they’re “open,” but we all know the reality is more complicated. Integration is still messy. Data doesn’t flow freely between tools. Customers worry about lock-in and complex migrations.
So the real question is: How will data and intelligence can be shared and move between systems and organizations? Until PLM platforms can truly communicate to each other, we’ll stay stuck in silos.
3. The Extended Enterprise Needs a Real PLM Backbon
Companies no longer operate in isolation—they’re part of vast, dynamic networks of suppliers, partners, and contractors.
Yet most PLM tools are built for internal use. They weren’t designed to share data across company boundaries. Trust models, access control, and real-time collaboration across the extended enterprise are still immature.
To reach PLM Singularity, we need PLM systems that work like the real world—connected and distributed.
4. AI + Human = Teamwork, Not Just Automation
AI is no longer a future concept—it’s here. But it’s not just about replacing humans with machines.
The real challenge is figuring out how AI and humans will work together.
What decisions should be automated? When should humans intervene? And how do we build confidence in the AI’s suggestions? We’re only beginning to define the new roles in this collaboration.
5. Ethics, Governance, and Trust Must Be Built In
As AI takes on more responsibility, we can’t afford to ignore questions of trust.
How will we ensure transparency in how AI makes decisions? What happens when AI systems make mistakes? Can we trace decisions back and audit them?
Without strong governance, even the smartest PLM system could become a liability.
6. Lifecycle Continuity Has to Be a First-Class Citizen
Managing the full product lifecycle isn’t just a slogan—it’s a necessity.
How do we preserve knowledge and traceability across decades? Especially in industries like aerospace or automotive, where products may last 20+ years?
Lifecycle data must be continuous, durable, and trusted—far beyond the engineering phase.
7. New Economics and Business Models Will Be Needed
Technology alone won’t get us there.
We also need to rethink how companies work, organize, and create value. What happens to traditional org charts when decision-making is distributed and AI-assisted? How do teams reskill? What new business models emerge around intelligent, data-driven development?
These changes will be just as important as the tools we build.
What is my conclusion?
PLM community is standing at a turning point. The rise of AI, modern data models, and demand for connected information is pushing PLM toward something radically different.
But technology is just one part of the story.
Can we align tech with people, business models, and organizations? Can we finally break the silos and build PLM systems that think, learn, and connect like we do?
That’s the real challenge—and opportunity—of PLM Singularity.
Just my thoughts.
Best, Oleg
Disclaimer: I’m the co-founder and CEO of OpenBOM, a digital-thread platform providing cloud-native collaborative services including PDM, PLM, and ERP capabilities. With extensive experience in federated CAD-PDM and PLM architecture, I’m advocates for agile, open product models and cloud technologies in manufacturing. My opinion can be unintentionally biased
