Let’s talk about something that’s on a lot of people’s minds these days. I was reading a WSJ article CEOs Trumpet Smaller Workforces as a Sign of Corporate Health during the weekend about how some big company execs (think Bank of America, Verizon, and Wells Fargo) are proudly highlighting smaller workforces as a sign of strength. Instead of layoffs being a last resort, they’re now framed as smart strategy: cutting headcount through hiring freezes, AI, and attrition to boost margins and look lean. It’s a big shift what used to be quietly handled is now openly discussed in earnings calls. But it also raises questions about what this means long-term for job stability and company culture.
AI is here, and it’s not just tweaking how we work, it’s changing who does the work . If you’re in the business you can see it already – some tasks that used to take hours now happen in seconds. And with AI getting smarter every day, it’s fair to ask: are some PLM jobs on their way out?
The short answer is… yes. But the long answer? It’s more interesting than that.
Let’s Clear This Up: Tasks Aren’t Jobs
One of the biggest misunderstandings in the “AI is coming for our jobs” debate is this: AI doesn’t take over roles, it takes over tasks.
Most jobs (PLM included) aren’t made up of one thing. They’re made up of dozens of activities – some routine, some reactive, and many highly contextual. Take something which is very basic for every engineering and manufacturing organization – releasing a drawing. That might involve reviewing the drawing, finding mistakes, understanding the intent behind a design, coordinating with engineering and quality, reviewing part impact, and making judgment calls about timing.
Yes, AI can find mistakes in the drawing (we see some very smart innovation here) , AI can probably automate the process of handling the metadata using modern online services to extract content automatically to manage and store it in a seamless way. But it can’t make a nuanced decision about whether the design is right, or whether the release will trigger downstream issues. Humans connect the dots. That’s where the value is.
So while AI may automate many steps, it doesn’t replace the thinking, the coordination, or the judgment that holds it all together.
First Up: The Jobs AI Will Shrink or Even Eliminate
Let’s start with the hard truth. Some roles in PLM are built almost entirely around repeatable, rules-based tasks. That’s AI’s sweet spot. If your job involves doing the same thing every day, following checklists, filling out forms, or cleaning up data, expect AI to come for that.
💀 PLM Administrator / System Admin
Tasks like managing user permissions, pushing updates, or resetting passwords? AI bots can already handle much of that. Cloud PLM tools auto-update, and intelligent agents are now built to troubleshoot simple issues on their own. You’ll still need a human touch for the complex stuff, but probably not full-time.
💀 Data Steward / PLM Data Manager
Spotting duplicates, checking for missing fields, keeping things “clean” – AI is already better at this than most humans. People will still step in when things get fuzzy or unusual, but the core job is shrinking fast.
💀 Document Control
Version control, release processes, audit trails… AI can handle them. Smart AI agents know when documents are out of sync, when a drawing is outdated, and when to ping people for review. What used to be a team of people is now a service layer. AI agentic workflow that can manage files can be a very interesting path for improvements in this space.
💀 PLM DevOps / IT Support
Server monitoring, backup scheduling, uptime alerts—all tasks AI is good at. Human DevOps folks will still be crucial, but mainly for incident response and long-term architecture. The reactive, repetitive side of the job? Mostly gone.
The Middle Ground: Jobs That Are Changing (Fast)
Here’s where it gets more nuanced. Some roles won’t vanish, but they will look very different soon. If you’re in one of these jobs, your future depends on how willing you are to adapt.
🔄 PLM Engineer / BOM Manager / Application Engineer
Think about what AI is already doing – automating CAD-to-BOM syncs, catching errors, mapping metadata. That frees up engineers to work on higher-value things: tailoring systems to business needs, solving weird integration issues, and guiding AI tools when they need human backup.
🔄 Change Management / ECN Coordinator
AI can identify standard changes and even initiate workflows. But when a change impacts multiple teams, or when policies aren’t clear, it still takes a human to figure it out. The job shifts from filling out forms to managing edge cases.
🔄 Configuration Management
Validation rules? Easy for AI. But defining new configurations for complex products? That’s still human territory. You’ll need to bring both system knowledge and product logic to the table. Here is a passage from Marijin Dullaart’s article:
“… the rise of artificial intelligence, particularly agentic AI that can perceive, decide, and act autonomously, introduces new complexities and opportunities. These autonomous systems require robust baseline configurations to operate effectively within defined parameters while adapting to changing environments”
🔄 PLM Business Analysts
AI will take care of the plumbing—automated data transfers, field mappings, simple reporting. But understanding what different teams really need and how to make systems work together? That’s not going away anytime soon. A very interesting article about it from Rob Ferrone (aka Product Data PLuMber).
The Human Core: What AI Can’t (Yet) Replace
Let’s be honest—there are still plenty of jobs AI isn’t ready to touch. These roles require judgment, leadership, relationship-building, and creativity. In short: they’re deeply human.
✅ PLM Program or Project Manager
AI can send reminders or build Gantt charts. But handling people, aligning priorities, resolving conflicts? That’s still all you. And probably will be for a long time.
✅ PLM Architect
AI might suggest templates or build sample diagrams, but designing a scalable, robust human-centric PLM architecture? That’s about understanding your company’s culture, politics, and long-term needs. That knowledge isn’t in the training data yet. Listen Helena Gutiérrez in her speech at Share PLM Summit 2025 – The Future Is Human: Leading with Soul in a World of AI.
✅ Head of PLM Roles
Setting strategy, aligning with execs, influencing roadmaps—this is leadership work. AI can support decision-making, but it doesn’t drive vision or carry weight in a boardroom.
So What Should You Do About It?
The key isn’t to ask whether AI will replace your job—it’s to analyze your role and ask which tasks are likely to be automated, and which ones depend on human insight.
Take inventory. What parts of your work are repetitive or rule-driven? Those are probably already in AI’s strike zone. Then ask: how could AI actually make me better at the rest of what I do?
Maybe it’s about offloading the manual stuff so you can spend more time solving real problems. Maybe it’s about using AI tools to improve data visibility or streamline team coordination. Either way, the professionals who will thrive in this new era are the ones who lean in, not opt out.
Here’s what that looks like:
- Understanding how AI tools and automations work
- Applying judgment and thinking in systems
- Working across roles, teams, and departments
- Solving the hard, messy, unpredictable problems that machines can’t
Your job isn’t disappearing. But it is evolving. And the sooner you take control of that evolution, the better your future will look.
Final Thought: Are You Training the AI or Competing With It?
The biggest risk to your PLM career isn’t that a robot takes your job.
It’s doing nothing while the world changes around you.
You can’t beat AI at repetitive work—and you shouldn’t try. What you can do is build the kind of value that AI can’t touch: contextual thinking, collaboration, leadership, creativity.
So here’s the question: if your company had to choose between hiring another admin or spinning up an AI assistant to manage tasks—what do you think they’d pick?
That’s not a future scenario. That’s happening now.
Just my thoughts..
Best, Oleg
PS. Let’s keep the conversation going. Which PLM roles do you think are most vulnerable, and which ones are quietly becoming more valuable than ever? Drop me a message—I’d love to hear your thoughts.
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. 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
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