Yesterday, I was invited to join a diverse group of PLM veterans and innovators hosted by Michael Finocchiaro to debate a provocative question: Is PLM dead?
What emerged wasn’t an obituary but a collective recognition that the meaning of PLM has outgrown its acronym. Christine Longwell argued that PLM will never realize its potential while trapped inside the boundaries of a vendor tool. True business value, she suggested, comes when product data escapes engineering silos and connects to customers, services, and strategy. Martin Eigner envisioned a graph based data model—a digital thread spanning legacy systems—while Patrick Hillberg compared PLM’s faded name to IBM’s, a label that no longer describes what it does. One of my favorites, Rob Ferrone said – for at least my first five years, I didn’t even know we were doing PDM or PLM, but in fact, we did. An interesting reflection came from Brion Carroll (II) – “I often talk to manufacturers about adopting the digital thread and looking upstream—it’s surprising how those driving business continuity from ideation to shelf are still rooted in PLM”.
Across the discussion, a clear theme surfaced: PLM has lost its linguistic and conceptual edge. Jos Voskuil noted that he no longer uses the “P-word” when talking with clients, emphasizing that companies now care about solving end-to-end business problems, not fitting into software categories. Jim Brown echoed this sentiment—arguing that the focus must return to business strategy, not terminology or tools. Brion Carroll pushed for a broader definition of PLM that stretches from ideation to commercialization and back again, while Juliann Grant reminded everyone that PLM still struggles for executive attention compared to ERP because it touches everything yet belongs to no single department.
Watch the recording by navigating to the following link.
My own reflection echoes through this chorus: the TLAs and acronyms are dying because they no longer match the way we work. And everyone has a slightly different definition of PLM to fit the needs, business objectives and organizational politics.
The next evolution won’t be another “XYZ management” system, but it will be a new framework and language of verbs and nouns that describe what teams actually do: design, source, build, deliver, and service. Intelligent agents will act on top of a connected digital thread, linking roles, data, and decisions across the lifecycle. In that sense, PLM as we’ve known it may indeed be dead—but what comes next could finally bring it to life in a more human, data-centric, and agentic form.
The discussion inspired me to write this article to share my vision for the path of PLM transformation—from a TLA-anchored definition to a world of universal data models and intelligent agents.
A Tipping Point: LLM and AI Agents
In 2025, the demand to organize information has never been greater. Engineering and manufacturing teams generate massive amounts of data — models, spreadsheets, specifications, messages, and revisions — all describing the same product, yet rarely aligned. Despite decades of progress in PLM, ERP, and so-called digital transformation, much of the work still feels fragmented, manual, and reactive.
For more than thirty years, the industry has been trying to solve one persistent challenge: how to bring order to the complexity of product information. Each technological wave — from databases and web systems to cloud platforms and now AI — promised a breakthrough. Yet the core problem remains. The issue is not a lack of tools, but a continuing struggle to structure, connect, and make information truly usable.
Looking back, every generation of technology represented another attempt to tame complexity — SQL databases that brought structure to proprietary systems, web applications that added layers of accessibility, social networks that promised collaboration, cloud and SaaS platforms that scaled distribution, and now AI, aiming to make sense of it all. Each wave moved us closer, but none fully solved the fundamental question: how do we organize data so that it works for people and systems alike?
Today, we may finally have the answer. The emergence of intelligent agents—AI systems that can reason, connect, and act—marks a turning point for PLM. The tools have changed before, but the pain has not. The question is whether the Agentic Age of PLM will finally solve what decades of innovation could not.
Beyond Acronyms – From TLA Soup to the Language of Work
For as long as many of us can remember, enterprise software has been built on acronyms. The formula was simple: take a business function, add “Management,” and you have a product. PLM, ERP, QMS, MES, SLM, PDM—each system claimed ownership over a slice of the enterprise.
This “TLA soup” created its own kind of confusion. It turned the simple work of designing, sourcing, and building into a maze of disconnected terms, processes, and systems. Instead of bringing clarity, these acronyms created silos of language that separated teams rather than connecting them.
The next era of PLM is not about new management acronyms—it’s about returning to the language of work itself: Design. Source. Build. Deliver. Service.
These are the verbs and nouns that describe how products come to life. The future of PLM is not a single “management” application—it’s a platform for collaboration, where humans and agents use data as their shared language. It’s a shift from control to context, from forms to flow.
The Universal Data Hammer – Organizing Data Wins
Every company, no matter its size, is fighting the same battle: disconnected, messy, inconsistent data.
Different teams use different tools, each with its own definitions and formats. Data is copied, exported, and re-imported until no one can tell which version is the truth.
What if we approached the problem differently? What if we built a Universal Data Hammer—a framework that shapes and connects every piece of product information into a single, understandable structure?
Imagine data that doesn’t live in isolated systems but in a graph of relationships—parts linked to assemblies, suppliers, configurations, and requirements. Imagine large language models (LLMs) reasoning across that graph, not just reading rows in a table.
This combination—graph models and AI reasoning—is transformative. It allows systems to understand meaning and context, not just store numbers and strings.
The principle is simple: Good data → smart agents. Bad data → unreliable agents.
The future of PLM won’t be defined by who has the biggest platform with the largest number of features, but by who organizes data best. The companies that treat data as a connected, evolving foundation will lead the next generation of product innovation.
From Process-Centered to Human-Centered
Traditional PLM systems were built for process enforcement. Their primary purpose was to maintain control—approvals, gates, compliance checks. This structure ensured traceability but often slowed creativity and frustrated the people doing the real work.
The Agentic Age changes that. It moves PLM from process-centered to human-centered.
Intelligent agents can understand context, remove friction, and automate the small, repetitive actions that consume time and attention.
Imagine an engineer asking: “Find alternates that reduce lead time by 20%.”
The agent instantly searches across suppliers, validates compliance, calculates cost impacts, and presents the options—something that once required multiple systems and manual coordination.
This shift doesn’t eliminate human expertise—it amplifies it. People still make decisions, but they’re supported by tools that understand what they’re trying to achieve.
PLM becomes less about following workflows and more about helping people work naturally.
The tools start adapting to the way engineers think—instead of forcing engineers to think like the tools.
Layers of Verbs and Nouns – Data and The Agentic Stack
Look at how digital transformation reshaped every industry in the past 30 years—music, food, travel, communication, shopping. Each revolution was built around a few essential verbs and nouns: listen, order, share, ride, watch, connect.
In my view, PLM is heading down the same path.
The Agentic Stack redefines PLM as a combination of data and agents working together, structured around the verbs and nouns of engineering and manufacturing.
- Nouns: parts, assemblies, suppliers, requirements.
- Verbs: design, release, order, validate, simulate.
When these elements are defined in the data layer, agents can reuse and recombine them dynamically. The same “release” logic can apply to an assembly in mechanical design or to a configuration in electronics.
This modularity makes PLM repeatable, composable, and flexible—a living system that evolves as businesses grow and technologies change.
More importantly, it enables continuous learning and automation. The data model becomes a foundation not only for management, but for reasoning and action. It’s how PLM turns from a static record into a responsive, adaptive ecosystem.
The Emotional Why – The Human Side of PLM
People don’t adopt PLM because they love workflows or databases. They adopt it because they want less chaos and risk.
Think about the daily struggles that bring teams to PLM:
- Engineers fixing BOM errors at midnight.
- Managers worrying about data accuracy before an audit.
- Teams scrambling to resolve supplier delays.
These are not technical inconveniences—they’re human stresses that come from uncertainty.
Agentic PLM changes that dynamic. It builds trust in data, in processes, and in collaboration. Agents can alert users about risks before they become problems, automatically recommend alternatives, and explain decisions transparently.
When that happens, people can focus on innovation rather than protecting their silo of data.. They can rely on the data layer and agent to be a partner, not a system to protect the walled garden of a specific software or domain..
That is the emotional core of this transformation: PLM becomes not just a system of data, but a system of confidence.
The Big Shift – From Record Keeper to Active Collaborator
For decades, PLM has been defined as a “system of record.” It stored what happened, preserved revision histories, managed changes, and ensured compliance. But it rarely helped teams decide what to do next.
That definition no longer fits.
Yesterday’s PLM was a vault of records.
Tomorrow’s PLM is a collaborator—an advisor, an actor, a doer.
In the Agentic Age, PLM becomes a system of action.
Agents monitor supply chain health, anticipate disruptions, suggest design alternatives, and generate procurement plans proactively. They interact in natural language, explain decisions, and collaborate across domains.
This is not science fiction. It’s a logical outcome of connecting data, context, and intelligence.
The organizations that will lead this transformation are those that combine data-centric architecture with agentic capability—systems that both understand and act.
PLM will no longer be a passive repository—it will be an active participant in the product lifecycle.
A Call to Rethink PLM
For years, the PLM world has thrived on its own vocabulary—full of acronyms, methodologies, and frameworks. But the language of “management” no longer matches the reality of modern work.
It’s time to think differently. Stop thinking in acronyms. Start thinking about universal data layer and about conversations and agentic actions.
The future of PLM will not be written in workflows and forms. It will be expressed in the data layers that can easy recombine to share data and knowledge, shared language between people and their digital collaborators.
The next decade will reward those who invest in data organization, openness, and collaboration. Those who cling to rigid, siloed systems will find themselves struggling to adapt.
The Agentic Age is not about replacing humans with AI—it’s about connecting them more deeply with the data and systems they rely on.
It’s a chance to build a PLM that finally works for people, not defense against them.
Since I started in PDM/PLM 25+ years ago, the technologies have evolved. The question now is whether we’ll use them to build systems of understanding and decisions.
What is my conclusion?
After 50+ years of PLM evolution, one truth stands firm: technology alone doesn’t organize complexity, but data and people do. The arrival of agentic intelligence gives us an opportunity to close the loop between information, context, and action.
The future of PLM is not another acronym or management layer. It’s a connected conversation – a partnership between humans, machines, and the data that defines everything we build.
Just my thoughts…
Best, Oleg
Disclaimer: I’m the co-founder and CEO of OpenBOM, a digital-thread platform providing cloud-native collaborative and integration services between engineering tools including PDM, PLM, and ERP capabilities. Interested in OpenBOM AI Agent Beta? Check with me about what is the future of Agentic Engineering Workflows.
With extensive experience in federated CAD-PDM and PLM architecture, I advocate for agile, open product models and cloud technologies in manufacturing. My opinion can be unintentionally biased.
