A few weeks ago, I was at a manufacturing and digital engineering conference, and one topic kept surfacing in almost every conversation I had on the sidelines: Collaborative Innovation.
The term itself isn’t new. We’ve been talking about collaboration in PLM for well over a decade now. What’s changed is how organizations collaborate, and what technology now quietly makes possible in the background.
During one of the panel discussions, leaders from manufacturing, automotive, and technology companies were asked to share their view on innovation. Everyone agreed, unsurprisingly, that innovation matters. But one line from the discussion stayed with me longer than the rest: innovation rarely happens in isolation. It happens when knowledge flows freely across people, departments, and, increasingly, intelligent systems. That’s what got me thinking about how far we’ve actually come.
Innovation doesn’t always come from a formal process
Years ago, I used to talk about how the “missed call” became an innovative mode of communication in India. Nobody designed it as a feature. It evolved because people found a clever, unplanned way to solve a very practical problem how do you send a message without spending money on a call?
I see the same pattern today, just with a very different technology.
Take Generative AI. Nobody set out, at least not deliberately, to have engineers use
conversational AI to summarize requirements, draft technical documentation, compare design
alternatives, or pull relevant knowledge out of thousands of legacy documents. Yet that’s
exactly what a lot of engineering teams are experimenting with right now, often without any
formal mandate to do so.
It’s a reminder that innovation often starts with people finding a new way to use something that
already exists. Organizations don’t always need to invent new technology to innovate
sometimes they just need to apply what they already have a little differently.
Collaboration has evolved quietly, but significantly
A decade ago, “collaboration” in PLM largely meant discussion forums, document sharing, and Facebook-style walls bolted onto engineering systems. That felt novel at the time.
Today collaboration looks a lot broader than that. Engineering teams are spread across countries and time zones. Suppliers get pulled into development much earlier than before. Manufacturing teams give feedback while a product is still being designed, not after it’s frozen. Service organizations feed field experience back into engineering instead of letting it sit in a ticketing system somewhere. And now, AI has quietly become another participant at that table not a person, but something engineers increasingly turn to as part of the conversation.
Collaboration, in other words, is no longer just between engineers. It’s between people, enterprise knowledge, and intelligent systems working alongside each other.
Why context still matters more than the tool
One thought from the panel resonated with me in particular: collaboration only becomes valuable when everyone is looking at the same context.
Marketing shouldn’t just hand engineering a document of requirements. They should be able to look at the same digital mock-ups, the same customer feedback, the same concepts engineering is working with. Manufacturing should be validating producibility well before a design is frozen, not after. Quality teams should be catching issues before the first prototype is even built. Service engineers should be feeding lessons learned from products already out in the field back into the next design cycle.
When everyone is collaborating around the same product definition, decisions simply move faster, and they tend to be better decisions too. This is exactly where modern PLM platforms have made real progress.
PLM is becoming the digital backbone, not just a CAD repository
Today’s PLM platforms have moved well past being a place to store CAD files. They’re becoming the digital backbone that ties together engineering, manufacturing, suppliers, simulation, requirements, quality, and service.
Concepts like the Digital Thread and Digital Twin are what make this possible in practice. Instead of teams exchanging disconnected documents and hoping everyone is looking at the latest version, organizations now collaborate around a product definition that’s continuously updated. Everyone sees the same, current information. Everyone works off the same source of truth. That single shift changes the quality of collaboration far more than any social-media-style feature ever did.
Where AI actually fits into all this
One thing that simply wasn’t part of this conversation a decade ago is AI, and I think it’s worth being precise about its role. AI isn’t replacing collaboration it’s enhancing it.
Picture an engineer asking a simple question: has anyone solved a similar problem before? Instead of digging through thousands of documents by hand, AI can now surface previous projects, similar designs, past validation reports, applicable standards, and lessons learned, often in seconds rather than days.
I’m seeing this play out in smaller, practical ways too AI helping engineers find reusable parts, summarize lengthy requirements, sanity-check a bill of materials, compare engineering changes side by side, draft technical documentation, or pull together information scattered across PLM, ERP, and other enterprise systems that never used to talk to each other cleanly. None of this replaces engineering judgment. What it does is make an organization’s own knowledge far more accessible than it used to be.
Collaboration still needs governance
For all the excitement around AI, one thing hasn’t changed at all: engineering collaboration has to happen inside a secure environment. Product intellectual property is still one of the most valuable things a manufacturing organization owns, and that hasn’t gotten any less true with AI in the picture if anything, it’s gotten more important. Modern PLM platforms provide the governance to let knowledge flow across functions without that openness turning into a liability. Getting the balance right between openness and control is only going to matter more as AI becomes a routine part of everyday engineering work, not less.
Where this leaves us
Looking back at what I wrote more than a decade ago, the core idea hasn’t really changed. What’s changed is the technology available to support it. Collaboration is no longer limited to people talking across departments it now includes enterprise knowledge, connected data, digital threads, and AI-assisted decision-making, all working together.
The organizations that manage to bring engineering, manufacturing, suppliers, service teams, and AI onto one common digital platform will innovate faster not necessarily because they have better ideas, but because they can turn those ideas into products far more efficiently than everyone else. That, at least in my view, is where PLM is heading next.
Rahul Deshpande
He is the CEO & Founder of BrainWave Consulting with over 30+ years of industry experience in PLM, digital engineering, and digital transformation.
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