More than a decade ago, I wrote a blog titled “How Many Buttons Do You Need in a PLM UI?” The idea of a PLM system with a single button was at first considered as a half-joke. It sounded absurd back then. But I was coming back to the question of how to improve PLM user experience again and again. But in today’s world of seamless consumer applications, that idea is much closer to reality than we ever imagined.
Think about how we interact with apps like Uber, Spotify, or even Amazon. It is one tap, and the system understands your intent. You don’t “use software,” you just get what you need. That kind of elegance has defined a new standard for user experience.
Now, compare that to traditional PLM software.
Yeah… it’s time for a change.
We’re entering a new era of product lifecycle management where the user experience is not just a UI—it’s an intelligent conversation. But to understand where we’re going, let’s briefly revisit where we’ve been.
4 Generations of PLM User Experience
The evolution of PLM interfaces mirrors broader shifts in software paradigms. Each generation tells a story—not just about technology, but about our expectations as users.
Generation 1: PLM Editor + Buttons (The Excel-ification of PLM)
The early PLM tools looked like database front-ends. Because that’s exactly what they were. Companies even called them “PLM Editors”. Rows and columns. Tabs and toolbars. Every function was a button. Every screen was a grid. Raw data model exposed to the customers.
For many engineers, this was familiar. It resembled database, which meant comfort. People were already running their product structures in tables and spreadsheets, so a slightly fancier table felt like a step forward.
But this model had limits. The UI was rigid, cluttered, and error-prone. You needed training just to navigate the system. And while Excel gave you freedom, early PLM tools gave you rules – lots of them.
This was PLM as a system of records. Powerful, yes – but rarely delightful.
Generation 2: Search-Driven, Google-zed Workflows
As web technology matured, the next wave of PLM UX took inspiration from Google.
Why click through folder trees when you can just search? The idea was simple: make the entire PLM system searchable. Users could type part numbers, descriptions, or change IDs and get instant results.
It was a massive improvement over form-based navigation. But it still required users to know what they were looking for. You had to be precise. And once you found the data, the system left it up to you to figure out what to do next.
It was faster, but still disconnected from actual workflows.
Generation 3: Facebook-lized Social Workflows
Then came the social wave.
Enterprise software vendors tried to bring in elements from platforms like Facebook, Twitter, and (later) Slack introducing activity streams, @mentions, comments, and collaborative notifications.
PLM became a little more human. Now you could see who did what, when, and why. You could discuss change orders in-line. You could tag a colleague for input. Threads became searchable too.
The goal was clear: connect the dots between people and processes.
But for many engineers, this felt like an extra layer. Social features weren’t baked into the DNA of PLM—they were bolted on. And adoption was uneven. Mechanical engineers didn’t want to “like” a change request. They wanted to approve it and move on. But “social” user experience provided a strong foundation and successful implementation in scenarios where “review” (or conversation) was needed. It allowed to capture tribal knowledge that otherwise left in the emails and powerpoints.
This generation helped raise awareness about context and collaboration. But it didn’t solve the fundamental challenge: how to make complex systems feel simple.
Generation 4: Chat + AI Workflows (Phase 1 & Phase 2)
Today, we’re on the edge of something new—and far more transformational.
AI-powered chat interfaces like ChatGPT, Copilot, and Gemini are changing how we interact with digital systems. We’re not searching, browsing, or clicking anymore, we’re talking.
Phase 1: Conversational Interfaces
The first wave is already here.
Users are communicating with systems programmed as chatbots. It felt like asking natural-language questions and getting rich, contextual answers. But, in fact, the backend of these chatbots was programmable workflow providing you “conversational” user experience, but no deep knowledge. But it was clear step forward.
This isn’t science fiction. It’s happening today in tools outside PLM and slowly creeping into engineering domains. Check tools like Drift and similar.
For PLM, it’s a wake-up call.
The expectation now is not to “use a system,” but to talk to a chatbot to get an answer. But those chatbots sometimes are a bit stupid.
Phase 2: Contextual Intelligence and Integrated Workflows
The next wave is even more exciting. Soon, chat interfaces will not just retrieve data—they will understand context and suggest actions. They’ll help you make better decisions, faster. It will be able to answer questions like “What’s the lead time on this component?” “Summarize the last engineering change.” “Which parts failed inspection last quarter?”
For example, ask:
“Which items in this BOM are single-sourced and have price volatility?”
And your system might reply:
“Three items are single-sourced. Two have had a 15% cost increase over the last 6 months. Would you like to explore alternates or run a procurement risk report?”
At that point, PLM becomes a co-pilot and not just a system of record, but a system of decision.
What is Needed to Achieve the New Level of User Experience?
To get to this future of PLM UX, we can’t just slap a chat interface on top of an old system and call it a day. What’s required is a fundamental shift in the architecture of PLM platforms themselves.
We need platforms that are capable of absorbing and aggregating data from across the entire product lifecycle. Not just CAD files or BOMs, but supplier information, manufacturing constraints, maintenance logs, customer feedback, and cost history. This full-body dataset is what I call “Product Memory.”
In traditional PLM systems, data is fragmented. It lives in silos such as CAD vaults, ERP systems, Excel spreadsheets, supplier portals, and email threads. Engineers spend more time finding the right information than actually using it to make decisions. And once they find it, they rarely have the context: why a part was chosen, who changed it last, what impact it had on cost or production.
Product Memory solves this by capturing everything from design to disposal into a single, queryable, contextualized model. It’s not just about storing data; it’s about preserving meaning, intent, and connections. Think of it as a living knowledge graph for your product development process.
When you use a Product Memory with a conversational interface, magic will happen. Now your co-pilot isn’t just smart, but it’s informed with the real time specific information about the specific customer, product line, S/N, etc. It understands how your organization thinks, what your product history looks like, and what decisions were made along the way. It can answer with authority because it has access to everything, in context.
That’s the real foundation for a next-generation user experience. Not just AI. Not just chat. But a memory—a brain for your product development.
What is my conclusion? From Search to Understanding…
Think about your own habits.
How many times this week did you turn to ChatGPT instead of Google?
The old model—type, scan, click—is giving way to a new one: ask, understand, act.
It doesn’t mean Google is obsolete. Just like traditional PLM won’t go away. But the interface to knowledge is changing. We want something that blends data, context, and intent. Something that feels less like a tool—and more like talking to a colleague who’s read every spec, every supplier quote, every change request.
That’s the future of PLM user experience. And maybe, just maybe, it starts with one button after all.
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. 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
