Earlier this week, my attention was caught by Forrester Wave report and debates around this. Thanks for Michael Finocchiaro and Aras which clearly was riding the wave on the Forrester Way, the topic triggered much attention and discussion. Check this post on LinkedIn.
The Forrester Wave™: Product Lifecycle Management For Discrete Manufacturers, Q3 2025 struck a familiar chord. The headline was very promising and I like it a lot.
“PLM Evolves From Document-Focused To Data-Centric Collaboration.”
That’s where we all want to go. But as I read through the report, I couldn’t help but reflect on the reality I see every day in the field: 95% of engineering work in discrete manufacturing is still trapped inside CAD files. And 80% of PLM implementations are still centered around check-in/check-out workflows built for managing documents.
So while the report outlines a vision of granular collaboration, embedded AI, and cross-discipline integration, the real question for most companies is far more basic: How can we capture the engineering data that already exists and make it valuable and usable across the business?
The Legacy Trap: File Vaults and Siloed Workflows
Despite all the progress in cloud, automation, and digital thread rhetoric, PLM implementations in many organizations still resemble their 1998 counterparts. Research provided by CIMdata shows that most of PLM related scenarios are still focused on traditional use cases – revision control, engineering changes, and approvals. Engineers spend their days checking in assemblies, managing ECO and approvals. Later on, the data is moving to PDFs, Excels, and neutral file storages. Or just sending documents around via email. This is not granular data collaboration. It’s manual coordination wrapped in a digital wrapper.
Forrester accurately calls this out:
“Our evaluation shows a decisive move away from ‘checking in’ and ‘checking out’ design documents and models toward sharing discrete design elements and using selective control and edit authorizations.”
It’s a welcome direction, the question is how much of this direction and vision is implemented in companies. That would be an interesting statistic to have.
The Real Job: Bridge Old Tools With Modern Data Environments
The most misunderstood part of PLM transformation is this: it’s not about throwing away the old tools. Engineers will continue using their favorite CAD systems, ECAD environments, simulation tools, and spreadsheets for a long time.
Here is my hypotesis. What’s needed is a layer that complements these tools as a way to capture design data, intent, product structures, and decisions without asking engineers to change how they work.
That data must then be:
- Structured
- Linked
- Contextual
- Accessible beyond engineering
The goal is to make product data collaborative by design—not only within engineering but across procurement, manufacturing, quality, and even the extended supply chain.
And it has to work across organizational boundaries. Companies don’t build products alone anymore. Suppliers, partners, and customers are all part of the process. They need access to data, not files. They need to collaborate—not wait for the next PDF export.
What I like about Forrester Review: The Foundations Are Clear
The Forrester report outlines three critical directions that align with this vision. Each requires rethinking PLM as a data service, not a document vault.
1. Granular Collaboration
“… move away from “checking in” and “checking out” design documents and models toward sharing discrete design elements and using selective control and edit authorizations. .”
Designs must be modular, versioned, and traceable at the object level, not locked in file containers. The data from design files needs to be seamlessly available outside of engineering already early in the process.
2. Embedded AI and GenAI
“AI can also help designers comprehensively evaluate all the feasible design options available and avoid the late-stage redesigns that frustrate new product introductions in regulated markets.”
But AI can’t parse a drawing or guess a relationship from a spreadsheet. It needs real data. Structured. Clean. Connected.
3. Bidirectional Interoperability
“Users can thus launch and support a lifetime of engineering change for successful products, satisfying economic, environmental, and market objectives.”
This is about orchestrating change across diverse tools and organizations—not syncing files on a schedule.
What We Need: Data That Works Across the Lifecycle
If I had to sum up my biggest takeaway, it would be this: We must shift our focus from managing documents to capturing and connecting engineering data.
Not for the sake of digitization. But to enable:
- Real-time decision making
- Traceability and compliance
- Faster iteration and product line expansion
- AI-driven validation, simulations, and recommendation
- Seamless supplier collaboration
That means building PLM systems that treat design and product data as living assets, not documents. It also means breaking down the silos that still separate CAD, PLM, ERP, and MES systems, most of which were never designed to share data in the first place.
What is my conlcusion: The First Step Isn’t AI. It’s Access.
The Forrester Wave points in the right direction. But we won’t get to intelligent collaboration, digital twins, or product lifecycle intelligence until we take the first and hardest step: Making engineering data accessible, structured, connected, and usable across the organization.
It starts by acknowledging the file-centric trap we’re still in—and building bridges from there. Not by replacing tools, but by augmenting them. Not by rewriting processes, but by enabling smarter ones. That’s how we’ll unlock real PLM transformation.
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
