You are currently browsing the category archive for the ‘Artificial Intelligence’ category.
December is the last month when daylight is getting shorter in the Netherlands, and with the end of the year approaching, this is the time to reflect on 2025.
For me, it has been an interesting year, and I hope it has been similar for you. I started 2025 with this post: My 2025 focus, sharing the topics that would drive my primary intentions—a quick walk through some of these topics and what to reflect on what I have learned.
Fewer blog posts
It was already clear that AI-generated content was going to drown the blogging space. The result: Original content became less and less visible, and a self-reinforcing amount of general messages reduced further excitement.
As I have no commercial drive to be visible, I will continue to write posts only when relevant to personal situations or ideas, with the intention of being shared and discussed with the readers of my posts – approximate 26 / year.
Therefore, if you are still interested in content that has not been generated with AI, I recommend subscribing to my blog and interacting directly with me through the comments, either on LinkedIn or via a direct message.
More podcast recordings
Together with the Share PLM podcast team, Beatriz Gonzales and Maria Morris, we enjoyed talking with a large variety of people active in PLM, all having their personal stories related to PLM to share—each episode ending with an experience to share and a desired takeaway for the listeners. We did it with great pleasure and learned from each episode.
You can find all the recordings from 2025 (Season 3) here.
In Season 4, we want to add the C-level perspective to our PLM and People podcast discussions.
#DataCentric or #PeopleCentric ?
It was PeopleCentric first at the beginning of the year, with the Share PLM Summit in Jerez and DataCentric in the second half of the year, with activities connected to the PLM Roadmap/PDT Europe conference in Paris.
In case you missed the excitement and lessons learned, here they are:
- The weekend after the Share PLM Summit 2025
- A week after Shaping the Future of PLM – No time to lose!
- A very long week after PLM Roadmap / PDT Europe 2025
Both topics will become even more critical due to the impact of AI tools on our day-to-day work.
Sustainability?
Already an uncomfortable term for some of us at the beginning of 2025, it has become one of the best-kept secrets of 2025. Where traditional countries and companies revert to their short-term bad habits – optimize shareholders value, there are also forward-looking enterprises that are actively rephrasing their sustainable strategies as risk mitigation strategies with the awareness that adaptation is inevitable. Better start early than too late – not a typical human strategy.
In case you are interested, I recommend you read and listen to:
And now it is time to discuss AI.
With all the investments and marketing related to AI, it is unavoidable to neglect it. For sure, it is a hype, but I believe that we are into something revolutionary for society, like the impact of the industrial revolution on our society 150 years ago.
However, there are also the same symptoms of the .com-hype 25 years ago.
Who are going to be the winners? Currently, the hardware, datacenter and energy providers, not the AI-solution providers. But this can change.
Let’s look into some of the potential benefits.
Individual efficiency?
Many of the current AI tools allow individuals to perform better at first sight. Suddenly, someone who could not write understandable (email) messages, draw images or create structured presentations now has a better connection with others—the question to ask is whether these improved efficiencies will also result in business benefits for an organization.
Looking back at the introduction of email with Lotus Notes, for example, email repositories became information siloes and did not really improve the intellectual behavior of people.
Later, Microsoft took over the dominant role as the office software provider with enhanced search and storage capabilities, but still, most of the individual knowledge remained hidden or inaccurate as it missed the proper context.
As a result of this, some companies tried to reduce the usage of individual emails and work more and more in communities with a specific context. Also, due to COVID and improved connectivity, this led to the success of Teams. And now with Copilot embedded in the Microsoft suite, I am curious to learn what companies perceive as measurable business benefits.
The chatbot?
For many companies, the chatbot is a way to reduce the number of people active in customer relations, either sales or services. I believe that, combined with the usage of LLMs, an improvement in customer service can be achieved. Or at least the perception, as so far I do not recall any interaction with a chatbot to be specific enough to solve my problem.
The risks with AI?
Now I may sound like a boomer who started focusing on knowledge management 25 years ago – exploring tacit knowledge.
Tacit knowledge is the knowledge a real expert has by combining different areas of expertise and understanding what makes sense.
Could tacit knowledge be replaced by an external model that gives you all the (correct?) answers?
In verifiable situations, we know when the model is hallucinating – but what if the scope is beyond our understanding? Would we still rely on AI, and could AI be manipulated in ways that we lose touch with the real facts?
Already, the first research papers are coming out warning of reduced human cognitive performance, e.g., this paper: Beware of Metacognitive Laziness: Effects of Generative Artificial Intelligence on Learning, Motivation, Processes, and Performance.
Combined with laziness (a typical human behavior – system 1), these results made me think of a statement made by Sean Illing:
“People love the truth, but they hate facts.”
A statement highly relevant to what we see happening now with social media – we do not think or research deep enough anymore, we select the facts that we like and consider them our truth.
What happens in our PLM domain?
In the PLM domain, companies are indeed reluctant to use LLMs directly, where some of them use RAG (Retrieval-Augmented Generation) to feed the LLM with a relevant context.
Still, the answers require human interpretation, as you cannot avoid hallucinations in your product lifecycle management processes.
As long as the results are based on inconsistent data sources that lack the relevant context, the answers are of low quality.
Meanwhile, every vendor in the PLM space is now offering AI-agents, most of the time within their own portfolio space. The ultimate dream is polygot agents (who are buying them / who are developing them) that can work together and create a new type of agility beyond traditional workflows. An interesting article in this context comes from Oleg Shilovitsky: Why Does PLM Need Task Re-Engineering Before It Can Have AI?
Still, these potential “quick” fixes create a risk for companies in the long term. Buying AI tools does not fix the foundation that is based on legacy.
In particular, related to the Shape the Future of PLM – Together workshop in Paris on Nov 4th, the consensus was that companies need to invest in understanding and implementing domain-specific ontologies and semantic models to provide a data-driven infrastructure that allows AI to make accurate decisions or valid recommendations.
You can read the summary of the event and recommendations here: Accelerating the Future of PLM & ALM on the ArrowHead’s website.
You can also read this post from Ole Olesen-Bagneux: Why will 2026 be the year of the ontologist?
Although the topics in the workshop might look “too advanced” for your company, they are crucial to transform into a long-term, sustainable, data-driven, model-based, and AI-supported enterprise.
Somewhere, you have to cross the chasm from documents to data in context.
Being busy is not an excuse, as you can also read in Thomas Nys’s LinkedIn post: Your Engineers spend 40 % of their time maintaining yesterday’s shortcuts. And you’re wondering why your AI initiative isn’t moving faster. I loved the image.
Human Resources?
The AI revolution will have an impact on society, and it is up to us individuals how well we adapt.
Remember, the first 50 – 100 years of the Industrial Revolution made only a few people extremely rich. James Watt, the Rothschild family, Andrew Carnegie, John D. Rockefeller, Cornelius Vanderbilt, J.P. Morgan, Alfred Krupp and the Schneider family became so rich due to ownership of factories and machinery, the control of raw materials (coal, iron, oil), the use of new technology (steam power, mechanization) combined with access to cheap labor and weak labor laws and limited competition early on.
Most humans moved into urbanized areas to become nothing but cheap resources, even children. And remember, many of us are still human resources!
A new conspiracy?
In 2016, Ida Auken’s lecture at the WEF created traction during COVID among people who believed in conspiracies. Her story focused on a more circular economy with respect for the Earth’s resources. The story was framed into the message:
“In the future, you will own nothing and be happy.”
The conspiracy theorist believed all their possessions would be taken away by the elite in the long term.
I want to conclude with a new message for these conspiracy theorists active on X or other discussion fora:
“In the future, you will know nothing, and you won’t be aware enough to care.”
Conclusion
2026 is going to be an interesting year, where we cannot allow ourselves to sit still and watch what is happening. Active participation is more challenging but also more rewarding than being a consumer. In May 2026, I hope to meet some of you at the Share PLM Summit in Jerez and share the human side, followed by the PDM Roadmap/PDT Europe conference in Q4 in Gothenburg, where we will catch up on the technical and data side.
I am wishing you all a wise and happy/healthy 2026
Last week, I wrote about the first day of the crowded PLM Roadmap/PDT Europe conference.
You can still read my post here in case you missed it: A very long week after PLM Roadmap / PDT Europe 2025
My conclusion from that post was that day 1 was a challenging day if you are a newbie in the domain of PLM and data-driven practices. We discussed and learned about relevant standards that support a digital enterprise, as well as the need for ontologies and semantic models to give data meaning and serve as a foundation for potential AI tools and use cases.
This post will focus on the other aspects of product lifecycle management – the evolving methodologies and the human side.
Note: I try to avoid the abbreviation PLM, as many of us in the field associate PLM with a system, where, for me, the system is more of an IT solution, where the strategy and practices are best named as product lifecycle management.
And as a reminder, I used the image above in other conversations. Every company does product lifecycle management; only the number of people, their processes, or their tools might differ. As Peter Billelo mentioned in his opening speech, the products are why the company exists.
Unlocking Efficiency with Model-Based Definition
Day 2 started energetically with Dennys Gomes‘ keynote, which introduced model-based definition (MBD) at Vestas, a world-leading OEM for wind turbines.
Personally, I consider MBD as one of the stepping stones to learning and mastering a model-based enterprise, although do not be confused by the term “model”. In MBD, we use the 3D CAD model as the source to manage and support a data-driven connection among engineering, manufacturing, and suppliers. The business benefits are clear, as reported by companies that follow this approach.
However, it also involves changes in technology, methodology, skills, and even contractual relations.
Dennys started sharing the analysis they conducted on the amount of information in current manufacturing drawings. The image below shows that only the green marker information was used, so the time and effort spent creating the drawings were wasted.
It was an opportunity to explore model-based definition, and the team ran several pilots to learn how to handle MBD, improve their skills, methodologies, and tool usage. As mentioned before, it is a profound change to move from coordinated to connected ways of working; it does not happen by simply installing a new tool.
The image above shows the learning phases and the ultimate benefits accomplished. Besides moving to a model-based definition of the information, Dennys mentioned they used the opportunity to simplify and automate the generation of the information.
Vestas is on a clear path, and it is interesting to see their ambition in the MBD roadmap below.
An inspirational story, hopefully motivating other companies to make this first step to a model-based enterprise. Perhaps difficult at the beginning from the people’s perspective, but as a business, it is a profitable and required direction.
Bridging The Gap Between IT and Business
It was a great pleasure to listen again to Peter Vind from Siemens Energy, who first explained to the audience how to position the role of an enterprise architect in a company compared to society. He mentioned he has to deal with the unicorns at the C-level, who, like politicians in a city, sometimes have the most “innovative” ideas – can they be realized?
To answer these questions, Peter is referring to the Business Capability Model (BCM) he uses as an Enterprise Architect.
Business Capabilities define ‘what’ a company needs to do to execute its strategy, are structured into logical clusters, and should be the foundation for the enterprise, on which both IT and business can come to a common approach.
The detailed image above is worth studying if you are interested in the levels and the mappings of the capabilities. The BCM approach was beneficial when the company became disconnected from Siemens AG, enabling it to rationalize its application portfolio.
Next, Peter zoomed in on some of the examples of how a BCM and structured application portfolio management can help to rationalize the AI hype/demand – where is it applicable, where does AI have impact – and as he illustrated, it is not that simple. With the BCM, you have a base for further analysis.
Other future-relevant topics he shared included how to address the introduction of the digital product passport and how the BCM methodology supports the shift in business models toward a modern “Power-as-a-Service” model.
He concludes that having a Business Capability Model gives you a stable foundation for managing your enterprise architecture now and into the future. The BCM complements other methodologies that connect business strategy to (IT) execution. See also my 2024 post: Don’t use the P** word! – 5 lessons learned.
Holistic PLM in Action.
or companies struggling with their digital transformation in the PLM domain, Andreas Wank, Head of Smart Innovation at Pepperl+Fuchs SE, shared his journey so far. All the essential aspects of such a transformation were mentioned. Pepperl+Fuchs has a portfolio of approximately 15,000 products that combine hardware and software.
It started with the WHY. With such a massive portfolio, business innovation is under pressure without a PLM infrastructure. Too many changes, fragmented data, no single source of truth, and siloed ways of working lead to much rework, errors, and iterations that keep the company busy while missing the global value drivers.
Next, the journey!
The above image is an excellent way to communicate the why, what, and how to a broader audience. All the main messages are in the image, which helps people align with them.
The first phase of the project, creating digital continuity, is also an excellent example of digital transformation in traditional document-driven enterprises. From files to data align with the From Coordinated To Connected theme.
Next, the focus was to describe these new ways of working with all stakeholders involved before starting the selection and implementation of PLM tools. This approach is so crucial, as one of my big lessons learned from the past is: “Never start a PLM implementation in R&D.”
If you start in R&D, the priority shifts away from the easy flow of data between all stakeholders; it becomes an R&D System that others will have to live with.
You never get a second, first impression!
Pepperl+Fuchs spends a long time validating its PLM selection – something you might only see in privately owned companies that are not driven by shareholder demands, but take the time to prepare and understand their next move.
As Andreas also explained, it is not only about the functional processes. As the image shows, migration (often the elephant in the room) and integration with the other enterprise systems also need to be considered. And all of this is combined with managing the transition and the necessary organizational change.
Andreas shared some best practices illustrating the focus on the transition and human aspects. They have implemented a regular survey to measure the PLM mood in the company. And when the mood went radical down on Sept 24, from 4.1 to 2.8 on a scale of 1 to 5, it was time to act.
They used one week at a separate location, where 30 of his colleagues worked on the reported issues in one room, leading to 70 decisions that week. And the result was measurable, as shown in the image below.
Andreas’s story was such a perfect fit for the discussions we have in the Share PLM podcast series that we asked him to tell it in more detail, also for those who have missed it. Subscribe and stay tuned for the podcast, coming soon.
Trust, Small Changes, and Transformation.
Ashwath Sooriyanarayanan and Sofia Lindgren, both active at the corporate level in the PLM domain at Assa Abloy, came with an interesting story about their PLM lessons learned.
To understand their story, it is essential to comprehend Assa Abloy as a special company, as the image below explains. With over 1000 sites, 200 production facilities, and, last year, on average every two weeks, a new acquisition, it is hard to standardize the company, driven by a corporate organization.
However, this was precisely what Assa Abloy has been trying to do over the past few years. Working towards a single PLM system, with generic processes for all, spending a lot of time integrating and migrating data from the different entities became a mission impossible.
To increase user acceptance, they fell into the trap of customizing the system ever more to meet many user demands. A dead end, as many other companies have probably experienced similarly.
And then they came with a strategic shift. Instead of holding on to the past and the money invested in technology, they shifted to the human side.
The PLM group became a trusted organisation supporting the individual entities. Instead of telling them what to do (Top-Down), they talked with the local business and provided standardized PLM knowledge and capabilities where needed (Bottom-Up).
This “modular” approach made the PLM group the trusted partner of the individual business. A unique approach, making us realize that the human aspect remains part of implementing PLM
Humans cannot be transformed
Given the length of this blog post, I will not spend too much text on my closing presentation at the conference. After a technical start on DAY 1, we gradually moved to broader, human-related topics in the latter part.
You can find my presentation here on SlideShare as usual, and perhaps the best summary from my session was given in this post from Paul Comis. Enjoy his conclusion.
Conclusion
Two and a half intensive days in Paris again at the PLM Roadmap / PDT Europe conference, where some of the crucial aspects of PLM were shared in detail. The value of the conference lies in the stories and discussions with the participants. Only slides do not provide enough education. You need to be curious and active to discover the best perspective.
For those celebrating: Wishing you a wonderful Thanksgiving!
For those of you following my blog over the years, there is, every time after the PLM Roadmap PDT Europe conference, one or two blog posts, where the first starts with “The weekend after ….”
This time, November has been a hectic week for me, with first this engaging workshop “Shape the future of PLM – together” – you can read about it in my blog post or the latest post from Arrowhead fPVN, the sponsor of the workshop.
Last week, I celebrated with the core team from the PLM Green Global Alliance our 5th anniversary, during which we discussed sustainability in action. The term sustainability is currently under the radar, but if you want to learn what is happening, read this post with a link to the webinar recording.
Last week, I was also active at the PTC/User Benelux conference, where I had many interesting discussions about PTC’s strategy and portfolio. A big and well-organized event in the town where I grew up in the world of teaching and data management.
And now it is time for the PLM roadmap / PDT conference review
The conference
The conference is my favorite technical conference 😉 for learning what is happening in the field. Over the years, we have seen reports from the Aerospace & Defense PLM Action Groups, which systematically work on various themes related to a digital enterprise. The usage of standards, MBSE, Supplier Collaboration, Digital Thread & Digital Twin are all topics discussed.
This time, the conference was sold out with 150+ attendees, just fitting in the conference space, and the two-day program started with a challenging day 1 of advanced topics, and on day 2 we saw more company experiences.
Combined with the traditional dinner in the middle, it was again a great networking event to charge the brain. We still need the brain besides AI. Some of the highlights of day 1 in this post.
PLM’s Integral Role in Digital Transformation
As usual, Peter Bilello, CIMdata’s President & CEO, kicked off the conference, and his message has not changed over the years. PLM should be understood as a strategic, enterprise-wide approach that manages intellectual assets and connects the entire product lifecycle.
I like the image below explaining the WHY behind product lifecycle management.
It enables end-to-end digitalization, supports digital threads and twins, and provides the backbone for data governance, analytics, AI, and skills transformation.
Peter walked us briefly through CIMdata’s Critical Dozen (a YouTube recording is available here), all of which are relevant to the scope of digital transformation. Without strong PLM foundations and governance, digital transformation efforts will fail.
The Digital Thread as the Foundation of the Omniverse
Prof. Dr.-Ing. Martin Eigner, well known for his lifetime passion and vision in product lifecycle management (PDM and PLM tools & methodology), shared insights from his 40-year journey, highlighting the growing complexity and ever-increasing fragmentation of customer solution landscapes.
In his current eco-system, ERP (read SAP) is playing a significant role as an execution platform, complemented by PDM or ECTR capabilities. Few of his customers go for the broad PLM systems, and therefore, he stresses the importance of the so-called Extended Digital Thread.
Prof Eigner describes the EDT more precisely as an overlaying infrastructure implemented by a graph database that serves as a performant knowledge graph of the enterprise.
The EDT serves as the foundation for AI-driven applications, supporting impact analysis, change management, and natural-language interaction with product data. The presentation also provides a detailed view of Digital Twin concepts, ranging from component to system and process twins, and demonstrates how twins enhance predictive maintenance, sustainability, and process optimization.
Combined with the NVIDIA Omniverse as the next step toward immersive, real-time collaboration and simulation, enabling virtual factories and physics-accurate visualization. The outlook emphasizes that combining EDT, Digital Twin, AI, and Omniverse moves the industry closer to the original PLM vision: a unified, consistent Single Source of Truth 😮that boosts innovation, efficiency, and ROI.
For me, hearing and reading the term Single Source of Truth still creates discomfort with reality and humanity, so we still have something to discuss.
Semantic Digital Thread for Enhanced Systems Engineering in a Federated PLM Landscape
Dr. Yousef Hooshmand‘s presentation was a great continuation of the Extended Digital Thread theme discussed by Dr. Martin Eigner. Where the core of Martin’s EDT is based on traceability between artifacts and processes throughout the lifecycle, Yousef introduced a (for me) totally new concept: starting with managing and structuring the data to manage the knowledge, rather than starting from the models and tools to understand the knowledge.
It is a fundamentally different approach to addressing the same problem of complexity. During our pre-conference workshop “Shape the future of PLM – together,” I already got a bit familiar with this approach, and Yousef’s recently released paper provides all the details.
All the relevant information can be found in his recent LinkedIn post here.
In his presentation during the conference, Yousef illustrated the value and applicability of the Semantic Digital Thread approach by presenting it in an automotive use case: Impact Analysis and Cost Estimation (image above)
To understand the Semantic Digital Thread, it is essential to understand the Semantic Data Model and its building blocks or layers, as illustrated in the image below:
In addition, such an infrastructure is ideal for AI applications and avoids vendor- or tool lock-in, providing a significant long-term advantage.
I am sure it will take time for us to digest the content if you are entering the domain of a data-driven enterprise (the connected approach) instead of a document-driven enterprise (the coordinated approach).
However, as many of the other presentations on day 1 also stated: “data without context is worthless – then they become just bits and bytes.” For advanced and future scenarios, you cannot avoid working with ontologies, semantic models and graph databases.
Where is your company on the path to becoming more data-driven?
Note: I just saw this post and the image above, which emphasizes the importance of the relationship between ontologies and the application of AI agents.
Evaluation of SysML v2 for use in Collaborative MBSE between OEMs and Suppliers
It was interesting to hear Chris Watkins’ speech, which presented the findings from the AD PLM Action Group MBSE Collaboration Working Group on digital collaboration based on SysML v2.
The topic they research is that currently there are no common methods and standards for exchanging digital model-based requirements and architecture deliverables for the design, procurement, and acceptance of aerospace systems equipment across the industry.
The action group explored the value of SysML v2 for data-driven collaboration between OEMs and suppliers, particularly in the early concept phases.
Chris started with a brief explanation of what SysXML v2 is – image below:
As the image illustrates, SysML v2-ready tools allow people to work in their proprietary interfaces while sharing results in common, defined structures and ontologies.
When analyzing various collaboration scenarios, one of the main challenges remained managing changes, the required ontologies, and working in a shared IT environment.
👉You can read the full report here: AD PAG reports: Model-Based Systems Engineering.
An interesting point of discussion here is that, in the report, participants note that, despite calling out significant gaps and concerns, a substantial majority of the industry indicated that their MBSE solution provider is a good partner. At the same time, only a small minority expressed a negative view.
Would Data-Centric Systems Engineering change the discussion? See table 1 below from Yousef’s paper:
An illustration that there was enough food for discussion during the conference.
PLM Interoperability and the Untapped Value of 40 Years in Standardization
In the context of collaboration, two sessions fit together perfectly.
First, Kenny Swope from Boeing. Kenny is a longtime Boeing engineering leader and global industrial-data standards expert who oversees enterprise interoperability efforts, chairs ISO/TC 184/SC 4, and mentors youth in technology through 4-H and FIRST programs.
Kenny shared that over the past 40+ years, the understanding and value of this approach have become increasingly apparent, especially as organizations move toward a digital enterprise. In a digital enterprise, these standards are needed for efficient interoperability between various stakeholders. And the next session was an example of this.
Unlocking Enterprise Knowledge
Fredrik Anthonisen, the CTO of the POSC Caesar Association (PCA), started his story about the potential value of efficient standard use.
According to a Siemens report, “The true costs of downtime” a $1,4 trillion is lost to unplanned downtime.
The root cause is that, most of the time, the information needed to support the MRO activity is inaccessible or incomplete.
Making data available using standards can provide part of the answer, but static documents and slow consensus processes can’t keep up with the pace of change.
Therefore, PCA established the PCA enterprise reference data cloud, where all stakeholders in enterprise collaboration can relate their data to digital exposed standards, as the left side of the image shows.
Fredrik shared a use case (on the right side of the image) as an example. Also, he mentioned that the process for defining and making the digital reference data available to participants is ongoing. The reference data needs to become the trusted resource for the participants to monetize the benefits.
Summary
Day 1 had many more interesting and advanced concepts related to standards and the potential usage of AI.
Jean-Charles Leclerc, Head of Innovation & Standards at TotalEnergies, in his session, “Bringing Meaning Back To Data,” elaborated on the need to provide data in the context of the domain for which it is intended, rather than “indexed” LLM data.
Very much aligned with Yousef’s statement that there is a need to apply semantic technologies, and especially ontologies, to turn the data into knowledge.
More details can also be found in the “Shape the future of PLM – together” post, where Jean-Charles was one of the leading voices.
The panel discussion at the end of day 1 was free of people jumping on the hype. Yes, benefits are envisioned across the product lifecycle management domain, but to be valuable, the foundation needs to be more structured than it has been in the past.
“Reliable AI comes from a foundation that supports knowledge in its domain context.”
Conclusion
For the casual user, day 1 was tough – digital transformation in the product lifecycle domain requires skills that might not yet exist in smaller organizations. Understanding the need for ontologies (generic/domain-specific) and semantic models is essential to benefit from what AI can bring – a challenging and enjoyable journey to follow!
Together with Håkan Kårdén, we had the pleasure of bringing together 32 passionate professionals on November 4th to explore the future of PLM (Product Lifecycle Management) and ALM (Asset Lifecycle Management), inspired by insights from four leading thinkers in the field. Please, click on the image for more details.
The meeting had two primary purposes.
- Firstly, we aimed to create an environment where these concepts could be discussed and presented to a broader audience, comprising academics, industrial professionals, and software developers. The group’s feedback could serve as a benchmark for them.
- The second goal was to bring people together and create a networking opportunity, either during the PLM Roadmap/PDT Europe conference, the day after, or through meetings established after this workshop.
Personally, it was a great pleasure to meet some people in person whose LinkedIn articles I had admired and read.
The meeting was sponsored by the Arrowhead fPVN project, a project I discussed in a previous blog post related to the PLM Roadmap/PDT Europe 2024 conference last year. Together with the speakers, we have begun working on a more in-depth paper that describes the similarities and the lessons learned that are relevant. This activity will take some time.
Therefore, this post only includes the abstracts from the speakers and links to their presentations. It concludes with a few observations from some attendees.
Reasoning Machines: Semantic Integration in Cyber-Physical Environments
Torbjörn Holm / Jan van Deventer: The presentation discussed the transition from requirements to handover and operations, emphasizing the role of knowledge graphs in unifying standards and technologies for a flexible product value network
The presentation outlines the phases of the product and production lifecycle, including requirements, specification, design, build-up, handover, and operations. It raises a question about unifying these phases and their associated technologies and standards, emphasizing that the most extended phase, which involves operation, maintenance, failure, and evolution until retirement, should be the primary focus.
It also discusses seamless integration, outlining a partial list of standards and technologies categorized into three sections: “Modelling & Representation Standards,” “Communication & Integration Protocols,” and “Architectural & Security Standards.” Each section contains a table listing various technology standards, their purposes, and references. Additionally, the presentation includes a “Conceptual Layer Mapping” table that details the different layers (Knowledge, Service, Communication, Security, and Data), along with examples, functions, and references.
The presentation outlines an approach for utilizing semantic technologies to ensure interoperability across heterogeneous datasets throughout a product’s lifecycle. Key strategies include using OWL 2 DL for semantic consistency, aligning domain-specific knowledge graphs with ISO 23726-3, applying W3C Alignment techniques, and leveraging Arrowhead’s microservice-based architecture and Framework Ontology for scalable and interoperable system integration.
The utilized software architecture system, including three main sections: “Functional Requirements,” “Physical Twin,” and “Digital Twin,” each containing various interconnected components, will be presented. The Architecture includes today several Knowledge Graphs (KG): A DEXPI KG, A STEP (ISO 10303) KG, An Arrowhead Framework KG and under work the CFIHOS Semantics Ontology, all aligned.
👉The presentation: W3C Major standard interoperability_Paris
Beyond Handover: Building Lifecycle-Ready Semantic Interoperability
Jean-Charles Leclerc argued that Industrial data standards must evolve beyond the narrow scope of handover and static interoperability. To truly support digital transformation, they must embrace lifecycle semantics or, at the very least, be designed for future extensibility.
This shift enables technical objects and models to be reused, orchestrated, and enriched across internal and external processes, unlocking value for all stakeholders and managing the temporal evolution of properties throughout the lifecycle. A key enabler is the “pattern of change”, a dynamic framework that connects data, knowledge, and processes over time. It allows semantic models to reflect how things evolve, not just how they are delivered.
By grounding semantic knowledge graphs (SKGs) in such rigorous logic and aligning them with W3C standards, we ensure they are both robust and adaptable. This approach supports sustainable knowledge management across domains and disciplines, bridging engineering, operations, and applications.
Ultimately, it’s not just about technology; it’s about governance.
Being Sustainab’OWL (Web Ontology Language) by Design! means building semantic ecosystems that are reliable, scalable, and lifecycle-ready by nature.
Additional Insight: From Static Models to Living Knowledge
To transition from static information to living knowledge, organizations must reassess how they model and manage technical data. Lifecycle-ready interoperability means enabling continuous alignment between evolving assets, processes, and systems. This requires not only semantic precision but also a governance framework that supports change, traceability, and reuse, turning standards into operational levers rather than compliance checkboxes.
👉The presentation: Beyond Handover – Building Lifecycle Ready Semantic Interoperability
The first two presentations had a lot in common as they both come from the Asset Lifecycle Management domain and focus on an infrastructure to support assets over a long lifetime. This is particularly visible in the usage and references to standards such as DEXPI, STEP, and CFIHOS, which are typical for this domain.
How can we achieve our vision of PLM – the Single Source of Truth?
Martin Eigner stated that Product Lifecycle Management (PLM) has long promised to serve as the Single Source of Truth for organizations striving to manage product data, processes, and knowledge across their entire value chain. Yet, realizing this vision remains a complex challenge.
Achieving a unified PLM environment requires more than just implementing advanced software systems—it demands cultural alignment, organizational commitment, and seamless integration of diverse technologies. Central to this vision is data consistency: ensuring that stakeholders across engineering, manufacturing, supply chain, and service have access to accurate, up-to-date, and contextualized information along the Product Lifecycle. This involves breaking down silos, harmonizing data models, and establishing governance frameworks that enforce standards without limiting flexibility.
Emerging technologies and methodologies, such as Extended Digital Thread, Digital Twins, cloud-based platforms, and Artificial Intelligence, offer new opportunities to enhance collaboration and integrated data management.
However, their success depends on strong change management and a shared understanding of PLM as a strategic enabler rather than a purely technical solution. By fostering cross-functional collaboration, investing in interoperability, and adopting scalable architectures, organizations can move closer to a trustworthy single source of truth. Ultimately, realizing the vision of PLM requires striking a balance between innovation and discipline—ensuring trust in data while empowering agility in product development and lifecycle management.
👉The presentation: Martin – Workshop PLM Future 04_10_25
The Future is Data-Centric, Semantic, and Federated … Is your organization ready?
Yousef Hooshmand, who is currently working at NIO as PLM & R&D Toolchain Lead Architect, discussed the must-have relations between a data-centric approach, semantic models and a federated environment as the image below illustrates:
Why This Matters for the Future?
- Engineering is under unprecedented pressure: products are becoming increasingly complex, customers are demanding personalization, and development cycles must be accelerated to meet these demands. Traditional, siloed methods can no longer keep up.
- The way forward is a data-centric, semantic, and federated approach that transforms overwhelming complexity into actionable insights, reduces weeks of impact analysis to minutes, and connects fragmented silos to create a resilient ecosystem.
- This is not just an evolution, but a fundamental shift that will define the future of systems engineering. Is your organization ready to embrace it?
👉The presentation: The Future is Data-Centric, Semantic, and Federated.
Some of first impressions
👉 Bhanu Prakash Ila from Tata Consultancy Services– you can find his original comment in this LinkedIn post
Key points:
- Traditional PLM architectures struggle with the fundamental challenge of managing increasingly complex relationships between product data, process information, and enterprise systems.
- Ontology-Based Semantic Models – The Way Forward for PLM Digital Thread Integration: Ontology-based semantic models address this by providing explicit, machine-interpretable representations of domain knowledge that capture both concepts and their relationships. These lay the foundations for AI-related capabilities.
Today’s discussions provided valuable insights into how the PLM language is evolving in the AI era and how we can better bridge the gap between business and technology to achieve fundamental transformation.
It’s clear that as AI, semantic technologies, and data intelligence mature, the way we think and talk about PLM must evolve too – from system-centric to value-driven, from managing data to enabling knowledge and decisions.
It’s clear that as AI, semantic technologies, and data intelligence mature, the way we think and talk about PLM must evolve too – from system-centric to value-driven, from managing data to enabling knowledge and decisions.
A quick & temporary conclusion
Typically, I conclude my blog posts with a summary. However, this time the conclusion is not there yet. There is work to be done to align concepts and understand for which industry they are most applicable. Using standards or avoiding standards as they move too slowly for the business is a point of ongoing discussion. The takeaway for everyone in the workshop was that data without context has no value. Ontologies, semantic models and domain-specific methodologies are mandatory for modern data-driven enterprises. You cannot avoid this learning path by just installing a graph database.
These infrastructures are necessary to implement AI meaningfully, which extends beyond data retrieval to managing and exploring knowledge. Due to the limited time we had in this workshop, we were not able to explore other dimensions of this transformation
For the participants of the workshop – stay tuned and we will send you the final conclusions
This week is busy for me as I am finalizing several essential activities related to my favorite hobby, product lifecycle management or is it PLM😉?
And most of these activities will result in lengthy blog posts, starting with:
“The week(end) after <<fill in the event>>”.
Here are the upcoming actions:
Click on each image if you want to see the details:
In this Future of PLM Podcast series, moderated by Michael Finocciaro, we will continue the debate on how to position PLM (as a system or a strategy) and move away from an engineering framing. Personally, I never saw PLM as a system and started talking more and more about product lifecycle management (the strategy) versus PLM/PDM (the systems).
Note: the intention is to be interactive with the audience, so feel free to post questions/remarks in the comments, either upfront or during the event.
You might have seen in the past two weeks some posts and discussions I had with the Share PLM team about a unique offering we are preparing: the PLM Awareness program. From our field experience, PLM is too often treated as a technical issue, handled by a (too) small team.
We believe every PLM program should start by fostering awareness of what people can expect nowadays, given the technology, experiences, and possibilities available. If you want to work with motivated people, you have to involve them and give them all the proper understanding to start with.
Join us for the online event to understand the value and ask your questions. We are looking forward to your participation.
This is another event related to the future of PLM; however, this time it is an in-person workshop, where, inspired by four PLM thought leaders, we will discuss and work on a common understanding of what is required for a modern PLM framework. The workshop, sponsored by the Arrowhead fPVN project, will be held in Paris on November 4th, preceding the PLM Roadmap/PDT Europe conference.
We will not discuss the term PLM; we will discuss business drivers, supporting technologies and more. My role as a moderator of this event is to assist with the workshop, and I will share its findings with a broader audience that wasn’t able to attend.
Be ready to learn more in the near future!
Suppose you have followed my blog posts for the past 10 years. In that case, you know this conference is always a place to get inspired, whether by leading companies across industries or by innovative and engaging new developments. This conference has always inspired and helped me gain a better understanding of digital transformation in the PLM domain and how larger enterprises are addressing their challenges.
This time, I will conclude the conference with a lecture focusing on the challenging side of digital transformation and AI: we humans cannot transform ourselves, so we need help.
At the end of this year, we will “celebrate” our fifth anniversary of the PLM Green Global Alliance. When we started the PGGA in 2020, there was an initial focus on the impact of carbon emissions on the climate, and in the years that followed, climate disasters around the world caused serious damage to countries and people.
How could we, as a PLM community, support each other in developing and sharing best practices for innovative, lower-carbon products and processes?
In parallel, driven by regulations, there was also a need to improve current PLM practices to efficiently support ESG reporting, lifecycle analysis, and, soon, the Digital Product Passport. Regulations that push for a modern data-driven infrastructure, and we discussed this with the major PLM vendors and related software or solution partners. See our YouTube channel @PLM_Green_Global_Alliance
In this online Zoom event, we invite you to join us to discuss the topics mentioned in the announcement. Join us in this event and help us celebrate!
I am closing that week at the PTC/User Benelux event in Eindhoven, the Netherlands, with a keynote speech about digital transformation in the PLM domain. Eindhoven is the city where I grew up, completed my amateur soccer career, ran my first and only marathon, and started my career in PLM with SmarTeam. The city and location feel like home. I am looking forward to discussing and meeting with the PTC user community to learn how they experience product lifecycle management, or is it PLM😉?
With all these upcoming events, I did not have the time to focus on a new blog post; however, luckily, in the 10x PLM discussion started by Oleg Shilovitsky there was an interesting comment from Rob Ferrone related to that triggered my mind. Quote:
The big breakthrough will come from 1. advances in human-machine interface and 2. less % of work executed by human in the loop. Copy/paste, typing, voice recognition are all significant limits right now. It’s like trying to empty a bucket of water through a drinking straw. When tech becomes more intelligent and proactive then we will see at least 10x.
This remark reminded me of one of my first blog posts in 2008, when I was trying to predict what PLM would look like in 2050. I thought it is a nice moment to read it (again). Enjoy!
PLM in 2050
As the year ends, I decided to take my crystal ball to see what would happen with PLM in the future. It felt like a virtual experience, and this is what I saw:
- Data is no longer replicated – every piece of information will have a Universal Unique ID, also known as a UUID. In 2020, this initiative became mature, thanks to the merger of some big PLM and ERP vendors, who brought this initiative to reality. This initiative dramatically reduced exchange costs in supply chains and led to bankruptcy for many companies that provided translation and exchange software.
- Companies store their data in ‘the cloud’ based on the concept outlined above. Only some old-fashioned companies still handle their own data storage and exchange, as they fear someone will access their data. Analysts compare this behavior with the situation in the year 1950, when people kept their money under a mattress, not trusting banks (and they were not always wrong)
- After 3D, a complete virtual world based on holography became the next step in product development and understanding. Thanks to the revolutionary quantum-3D technology, this concept could even be applied to life sciences. Before ordering a product, customers could first experience and describe their needs in a virtual environment.
- Finally, the cumbersome keyboard and mouse were replaced by voice and eye recognition. Initially, voice recognition
and eye tracking were cumbersome. Information was captured by talking to the system and by recording eye movements during hologram analysis. This made the life of engineers so much easier, as while researching and talking, their knowledge was stored and tagged for reuse. No need for designers to send old-fashioned emails or type their design decisions for future reuse - Due to the hologram technology, the world became greener. People did not need to travel around the world, and the standard became virtual meetings with global teams(airlines discontinued business class). Even holidays can be experienced in the virtual world thanks to a Dutch initiative inspired by coffee. The whole IT infrastructure was powered by efficient solar energy, drastically reducing the amount of carbon dioxide.
- Then, with a shock, I noticed PLM no longer existed. Companies were focusing on their core business processes. Systems/terms like PLM, ERP, and CRM no longer existed. Some older people still remembered the battle between those systems over data ownership and the political discomfort this caused within companies.
- As people were working so efficiently, there was no need to work all week. There were community time slots when everyone was active, but 50 per cent of the time, people had time to recreate (to re-create or recreate was the question). Some older French and German designers remembered the days when they had only 10 weeks holiday per year, unimaginable nowadays.
As we still have more than 40 years to reach this future, I wish you all a successful and excellent 2009.
I am looking forward to being part of the green future next year.
After a summer holiday in the south of Greece, it is time to resume my activities. The south of Crete is largely an analogue environment, far from any digital hype.
Tempted by LinkedIn posts, I noticed the summer was full of memories, with Martin Eigner sharing 40 years of PLM experience, Oleg Shilovitsky sharing 30 years of PDM Evolution, and Michael Finochario publishing posts on PLM vendors, CAD kernels, and more.
So where do I stand? While digesting all these historical experiences, I reflected on what we can learn from them and what we didn’t learn from them.
It started with technology.
From 1990 to 1999, I worked with mid-market companies, where data management was the most significant challenge. The introduction of MS Windows made data management more user-friendly, evolving from drawing management systems with version and status management capabilities.
Who remembers Automanager Workflow from Cyco, before SmarTeam came on the market?
For that reason, in the early days, PDM was an IT job. As the PDM system primarily dealt with engineering data, it was relatively easy to implement as an organizational change process. We transitioned from analogue to electronic in the department.
Connecting with other systems, particularly ERP, was a serious IT job and a financial challenge. Connecting with other systems, particularly ERP, was a serious IT job and a financial challenge. The rapid decline of IT components, combined with the rapid growth of global connectivity, has created new opportunities for collaboration.
As part of the Dassault/IBM/SmarTeam organization, I explained and taught these new capabilities worldwide.
In 2008, my VirtualDutchman blog and coaching journey began, evolving from explanations of technology to modern methodologies, which led to organizational change and expectation management – skills not traditionally associated with IT.
Then came digital transformation
With growing connectivity, smartphones and Web 2.0 technology have led to more PLM-like discussions. PLM vendors expanded their scope and developed capabilities beyond mechanical engineering.
The expansion of capabilities was also the moment when the confusion about the term PLM reached its peak: a PLM strategy or a PLM system?
At the time, they were largely considered the same in discussions and advertisements..
Meanwhile, digital transformation was occurring at the marketing and sales levels – companies invested in direct communication with their customers through the web.
Meanwhile, the internal ways of working for R&D, engineering, and manufacturing did not change significantly. Still, they were following linear processes, and despite the existence of 3D CAD, the 2D drawing remained the primary carrier of legal information between engineering, manufacturing, and suppliers.
Note: the option where the most benefits could be achieved – connected supply chains – had the lowest focus in 2017 – something that would change with COVID-19.
Fundamental digital transformation in the PLM domain occurred gradually. ARAS came with its overlay approach (the platform), connecting various disciplines and enterprise systems. In contrast, Dassault Systèmes introduced its 3DEXPERIENCE platform, utilizing its own software brands as platform components.
The Aras overlay approach
Most PLM vendors rapidly countered Aras’ overlay approach with their low-code offerings based on Mendix, ThingWorx or Netvibes, to enable data flows beyond the traditional PDM scope. The Coordinated Digital Thread was born.
The good news is that PLM has now clearly become a strategy based on a federated system infrastructure. The single PLM system no longer exists, although many of us still use the term’ PLM system’ to refer to the main component of a PLM infrastructure – the System of Record.
Moving to a federated PLM infrastructure is already a challenge for companies, not because of the available technology, but first of all because of the legacy data and, closely related to that, legacy processes and people skills.
Legacy is creating the inertia, not technology!
Next came the cloud – SaaS
With the availability of cloud solutions that support real-time interactions between stakeholders, either within an enterprise or in a value chain, a new paradigm has emerged: the connected enterprise.
A connected enterprise no longer needs interfaces to transfer data from one system to another.
Instead, with apps and dashboards, combined data from different online sources is presented in a single, user-friendly working environment – A combination of the Systems of Record with the new environments – the Systems of Engagement.
The technology used to create dashboards and apps is based on modern data-driven technologies and principles (ontologies, graph databases, and the semantic web). The Connected Digital Thread was born.
However, legacy systems play an essential role again, as some systems of engagement can be implemented in a complementary manner to the systems of record, allowing companies to work within an integrated technology model.
People will work in a particular mode, either coordinated or connected, but organizations can operate in both modes simultaneously. A story I have been sharing a lot – it is not about migrations but about an evolutionary approach towards an integrated technology model.
At this point, it becomes essential that business objectives drive the implementation of a PLM infrastructure. Of course, you hear me say we should start from the business; however, the big difference now is that a company should coordinate the technologies, systems, and tools it acquires to avoid isolated islands of information.
Follow Yousef Hooshmand‘s 5 + 1 business transformation steps.
An open SaaS infrastructure enables a company to let data flow almost in real-time. There is a lot of discussion related to data quality and governance, and if you have missed it, please read these three articles I created together with Rob Feronne, the product Digital PLuMber:
- Data Quality and Data Governance – A hype? (part 1)
- Data Quality and Data Governance – the WHY and HOW (part 2)
- Building the Future: Data Quality and Governance in the Digital Age (part 3)
There are some great insights in this dialogue and the associated LinkedIn comments.
Despite the increasing availability of technology, it is the legacy of people, processes, and culture that is hindering progress.
Rob Feronne had a shocking lightbulb moment 😲 in our discussion about the future of PLM, where the participants – see below – answered a question related to the importance of technology in our PLM domain – shocking also for me.
My thumb was up because modern technology matters! The question inspired Oleg Shilovitsky to write a whole blog post on this topic. If you’re truly shocked, read his post, where I agree with the content; the question is too simple to answer with a thumbs up/down.
As technology has become more accessible than before, you no longer need an IT department to establish a PLM infrastructure. And then indeed, the people and process side needs and deserves much more attention..
And now there is AI
If you haven’t read anything about AI recently, you must be living in an isolated location. Regardless of the business discussions you are following, it is all about the potential of AI.
Although AI is not a new concept, the fact that various AI capabilities have now reached the end-user level is what drives the hype. Currently, I believe we are at the peak of the hype.
Last week, I participated in an interesting discussion in the series: The Future of PLM moderated by Michael Finochario, this time talking with the analysts. Click on the link to see Michael’s excellent summary and access to the recording of the event.
It was an interesting discussion for a little more than an hour, and the majority of our discussion was about the potential impact of AI on businesses. First, the impact AI can have on the traditional work of an analyst and next, the effects on the PLM domain.
I believe we agreed that AI at this moment is mainly providing higher user efficiency and performance, very much aligned with the interesting research I have been reading in the MIT NANDA report with the title The GenAI Divide: STATE OF AI IN BUSINESS 2025
The report’s interesting findings included high adoption of tools but low transformation. Despite significant investment in Generative AI (GenAI), most organizations are not achieving meaningful business transformation.
- 95% of organizations report zero return on GenAI investments.
- Only 5% of integrated AI pilots generate millions in value.
- 80% of organizations have explored or piloted tools like ChatGPT, but these primarily enhance individual productivity.
- 60% of organizations evaluated enterprise-grade systems, but only 20% reached the pilot stage, and just 5% reached production.
- Key barriers include brittle workflows, a lack of contextual learning, and operational misalignment.
Therefore, the question is – Is current AI the next bubble?
In 2014, I wrote about the lack of digital transformation in the PLM domain, and two images (below) from a report by The Economist could be used again. The report can be found here: The Onrushing Wave.
Click on the image to read the 2013 predictions.
I realized that my current job, as a recreational therapist and firefighter at the time, was not at risk, and that some of the predictions from 10 years ago had become a reality. Who is still bothered by telemarketers or retail salespersons?
However, many of the AI symptoms mentioned in the MIT NANDA report are similar to the hype surrounding digital transformation.
The only reservation I have now – will it take a decade before we understand and demonstrate the value of AI, or are we accelerating?
In this context, the upcoming PLM Roadmap/PDT Europe conference on 5 – 6 November will be interesting, as here we will discuss reality.
For a few of you interested in more, there is the day before the conference, a (free) workshop where we will discuss with some thought leaders and experts from various companies how the future of PLM could look like – based on standards, AI tools and more. Click on the image below the conclusion.
Conclusion
The summertime was a nice moment to reflect, inspired by others in my network. What is clear is that there is a shift from technology towards people and change. The rapid expansion of AI tools, along with connected technologies, has created an overwhelming array of possibilities. Now it is time for business leadership to understand them and utilize them for significant business improvement, where the fear is that substantial change will always be slowed down by organizational inertia.
In the past three weeks, between some short holidays, I had a discussion with Rob Ferrone, who you might know as
“The original product Data PLuMber”.
Our discussion resulted in this concluding post and these two previous posts:
If you haven’t read them before, please take a moment to review them, to understand the flow of our dialogue and to get a full, holistic view of the WHY, WHAT and HOW of data quality and data governance.
A foundation required for any type of modern digital enterprise, with or without AI.
A first feedback round
Rob, I was curious whether there were any interesting comments from the readers that enhanced your understanding. For me, Benedict Smith’s point in the discussion thread was an interesting one.
From this reaction, I like to quote:
To suggest it’s merely a lack of discipline is to ignore the evidence. We have some of the most disciplined engineers in the world. The problem isn’t the people; it’s the architecture they are forced to inhabit.
My contention is that we have been trying to solve a reasoning problem with record-keeping tools. We need to stop just polishing the records and start architecting for the reasoning. The “what” will only ever be consistently correct when the “why” finally has a home. 😎
Here, I realized that the challenge is not only about moving From Coordinated to Coordinated and Connected, but also that our existing record-keeping mindset drives the old way of thinking about data. In the long term, this will be a dead end.
What did you notice?
Jos, indeed, Benedict’s point is great to have in mind for the future and in addition, I also liked the comment from Yousef Hooshmand, where he explains that a data-driven approach with a much higher data granularity automatically leads to a higher quality – I would quote Yousef:
The current landscapes are largely application-centric and not data-centric, so data is often treated as a second or even third-class citizen.
In contrast, a modern federated and semantic architecture is inherently data-centric. This shift naturally leads to better data quality with significantly less overhead. Just as important, data ownership becomes clearly defined and aligned with business responsibilities.
Take “weight” as a simple example: we often deal with “Target Weight,” “Calculated Weight,” and “Measured Weight.” In a federated, semantic setup, these attributes reside in the systems where their respective data owners (typically the business users) work daily, and are semantically linked in the background.
I believe the interesting part of this discussion is that people are thinking about data-driven concepts as a foundation for the paradigm, shifting from systems of record/systems of engagement to systems of reasoning. Additionally, I see how Yousef applies a data-centric approach in his current enterprise, laying the foundation for systems of reasoning.
What’s next?
Rob, your recommendations do not include a transformation, but rather an evolution to become better and more efficient – the typical work of a Product PLuMber, I would say. How about redesigning the way we work?
Bold visions and ideas are essential catalysts for transformations, but I’ve found that the execution of significant, strategic initiatives is often the failure mode.
One of my favourite quotes is:
“A complex system that works is invariably found to have evolved from a simple system that worked.”
John Gall, Systemantics (1975)
For example, I advocate this approach when establishing Digital Threads.
It’s easy to imagine a Digital Thread, but building one that’s sustainable and delivers measurable value is a far more formidable challenge.
Therefore, my take on Digital Thread as a Service is not about a plug-and-play Digital Thread, but the Service of creating valuable Digital Threads.
You achieve the solution by first making the Thread work and progressively ‘leaving a trail of construction’.
The caveat is that this can’t happen in isolation; it must be aligned with a data strategy, a set of principles, and a roadmap that are grounded in the organization’s strategic business imperatives.
Your answer relates a lot to Steef Klein’s comment when he discussed: “Industry 4.0: Define your Digital Thread ML-related roadmap – Carefully select your digital innovation steps.” You can read Steef’s full comment here: Your architectural Industry 4.0 future)
First, I liked the example value cases presented by Steef. They’re a reminder that all these technology-enabled strategies, whether PLM, Digital Thread, or otherwise, are just means to an end. That end is usually growth or financial performance (and hopefully, one day, people too).
It is a bit like Lego, however. You can’t build imaginative but robust solutions unless there is underlying compatibility and interoperability.
It would be a wobbly castle made from a mix of Playmobil, Duplo, Lego and wood blocks (you can tell I have been doing childcare this summer – click on the image to see the details).
As the lines blur between products, services, and even companies themselves, effective collaboration increasingly depends on a shared data language, one that can be understood not just by people, but by the microservices and machines driving automation across ecosystems.
Discussing the future?
I think that for those interested in this discussion, I would like to point to the upcoming PLM Roadmap/PDT Europe 2025 conference on November 5th and 6th in Paris, where some of the thought leaders in these concepts will be presenting or attending. The detailed agenda is expected to be published after the summer holidays.
However, this conference also created the opportunity to have a pre-conference workshop, where Håkan Kårdén and I wanted to have an interactive discussion with some of these thought leaders and practitioners from the field.
Sponsored by the Arrowhead fPVN project, we were able to book a room at the conference venue in the afternoon of November 4th. You can find the announcement and more details of the workshop here in Hakan’s post:. Shape the Future of PLM – Together.
Last year at the PLM Roadmap PDT Europe conference in Gothenburg, I saw a presentation of the Arrowhead fPVN project. You can read more here: The long week after the PLM Roadmap/PDT Europe 2024 conference.
And, as you can see from the acknowledged participants below, we want to discuss and understand more concepts and their applications – and for sure, the application of AI concepts will be part of the discussion.
Mark the date and this workshop in your agenda if you are able and willing to contribute. After the summer holidays, we will develop a more detailed agenda about the concepts to be discussed. Stay tuned to our LinkedIn feed at the end of August/beginning of September.
And the people?
Rob, we just came from a human-centric PLM conference in Jerez – the Share PLM 2025 summit – where are the humans in this data-driven world?
You can’t have a data-driven strategy in isolation. A business operating system comprises the coordinated interaction of people, processes, systems, and data, aligned to the lifecycle of products and services. Strategies should be defined at each layer, for instance, whether the system landscape is federated or monolithic, with each strategy reinforcing and aligning with the broader operating system vision.
In terms of the people layer, a data strategy is only as good as the people who shape, feed, and use it. Systems don’t generate clean data; people do. If users aren’t trained, motivated, or measured on quality, the strategy falls apart.
Data needs to be an integral, essential and valuable part of the product or service. Individuals become both consumers and producers of data, expected to input clean data, interpret dashboards, and act on insights. In a business where people collaborate across boundaries, ask questions, and share insight, data becomes a competitive asset.
There are risks; however, a system-driven approach can clash with local flexibility/agility.
People who previously operated on instinct or informal processes may now need to justify actions with data. And if the data is poor or the outputs feel misaligned, people will quickly disengage, reverting to offline workarounds or intuition.
Here it is critical that leaders truly believe in the value and set the tone, and because it rare to have everyone in the business care about the data as passionately as they do about the prime function of their unique role (e.g. designer);
therefore there needs to be product data professionals in the mix – people who care, notice what’s wrong, and know how to fix it across silos.
Conclusion
- Our discussions on data quality and governance revealed a crucial insight: this is not a technical journey, but a human one. While the industry is shifting from systems of record to systems of reasoning, many organizations are still trapped in record-keeping mindsets and fragmented architectures. Better tools alone won’t fix the issue—we need better ownership, strategy, and engagement.
- True data quality isn’t about being perfect; it’s about the right maturity, at the right time, for the right decisions. Governance, too, isn’t a checkbox—it’s a foundation for trust and continuity. The transition to a data-centric way of working is evolutionary, not revolutionary—requiring people who understand the business, care about the data, and can work across silos.
The takeaway? Start small, build value early, and align people, processes, and systems under a shared strategy. And if you’re serious about your company’s data, join the dialogue in Paris this November.
Where are you on the AI hype cycle ?
In my first discussion with Rob Ferrone, the original Product Data PLuMber, we discussed the necessary foundation for implementing a Digital Thread or leveraging AI capabilities beyond the hype. This is important because all these concepts require data quality and data governance as essential elements.
If you missed part 1, here is the link: Data Quality and Data Governance – A hype?
Rob, did you receive any feedback related to part 1? I spoke with a company that emphasized the importance of data quality; however, they were more interested in applying plasters, as they consider a broader approach too disruptive to their current business. Do you see similar situations?
Honestly, not much feedback. Data Governance isn’t as sexy or exciting as discussions on Designing, Engineering, Manufacturing, or PLM Technology. HOWEVER, as the saying goes, all roads lead to Rome, and all Digital Engineering discussions ultimately lead to data.
Cristina Jimenez Pavo’s comment illustrates that the question is in the air.:
Everyone knows that it should be better; high-performing businesses have good data governance, but most people don’t know how to systematically and sustainably improve their data quality. It’s hard and not glamorous (for most), so people tend to focus on buying new systems, which they believe will magically resolve their underlying issues.
Data governance as a strategy
Thanks for the clarification. I imagine it is similar to Configuration Management, i.e., with different needs per industry. I have seen ISO 8000 in the aerospace industry, but it has not spread further to other businesses. What about data governance as a strategy, similar to CM?
That’s a great idea. Do you mind if I steal it?
If you ask any PLM or ERP vendor, they’ll claim to have a master product data governance template for every industry. While the core principles—ownership, control, quality, traceability, and change management, as in Configuration Management—are consistent, their application must vary based on the industry context, data types, and business priorities.
Designing effective data governance involves tailoring foundational elements, including data stewardship, standards, lineage, metadata, glossaries, and quality rules. These elements must reflect the realities of operations, striking a balance between trade-offs such as speed versus rigor or openness versus control.
The challenge is that both configuration management (CM) and data governance often suffer from a perception problem, being viewed as abstract or compliance-heavy. In truth, they must be practical, embedded in daily workflows, and treated as dynamic systems central to business operations, rather than static documents.
Think of it like the difference between stepping on a scale versus using a smartwatch that tracks your weight, heart rate, and activity, schedules workouts, suggests meals, and aligns with your goals.
Governance should function the same way:
responsive, integrated, and outcome-driven.
Who is responsible for data quality?
I have seen companies simplifying data quality as an enhancement step for everyone in the organization, like a “You have to be more accurate” message, similar perhaps to configuration management. Here we touch people and organizational change. How do you make improving data quality happen beyond the wish?
In most companies, managing product data is a responsibility shared among all employees. But increasingly complex systems and processes are not designed around people, making the work challenging, unpleasant, and often poorly executed.
I like to quote Larry English – The Father of Information Quality:
“Information producers will create information only to the quality level for which they are trained, measured and held accountable.”
A common reaction is to add data “police” or transactional administrators, who unintentionally create more noise or burden those generating the data.
The real solution lies in embedding capable, proactive individuals throughout the product lifecycle who care about data quality as much as others care about the product itself – it was the topic I discussed at the 2025 Share PLM summit in Jerez – Rob Ferrone – Bill O-Materials also presented in part 1 of our discussion.
These data professionals collaborate closely with designers, engineers, procurement, manufacturing, supply chain, maintenance, and repair teams. They take ownership of data quality in systems, without relieving engineers of their responsibility for the accuracy of source data.
Some data, like component weight, is best owned by engineers, while others—such as BoM structure—may be better managed by system specialists. The emphasis should be on giving data professionals precise requirements and the authority to deliver.
They not only understand what good data looks like in their domain but also appreciate the needs of adjacent teams. This results in improved data quality across the business, not just within silos. They also work with IT and process teams to manage system changes and lead continuous improvement efforts.
The real challenge is finding leaders with the vision and drive to implement this approach.
The costs or benefits associated with good or poor data quality
At the peak of interest in being data-driven, large consulting firms published numerous studies and analyses, proving that data-driven companies achieve better results than their data-averse competitors. Have you seen situations where the business case for improving “product data” quality has led to noticeable business benefits, and if so, in what range? Double digit, single digit?
Improving data quality in isolation delivers limited value. Data quality is a means to an end. To realise real benefits, you must not only know how to improve it, but also how to utilise high-quality data in conjunction with other levers to drive improved business outcomes.
I built a company whose premise was that good-quality product data flowing efficiently throughout the business delivered dividends due to improved business performance. We grew because we delivered results that outweighed our fees.
Last year’s turnover was €35M, so even with a conservatively estimated average in-year ROI of 3:1, the company delivered over € 100 M of cost savings or additional revenue per year to clients, with the majority of these benefits being sustainable.
There is also the potential to unlock new value and business models through data-driven innovation.
For example, connecting disparate product data sources into a unified view and taking steps to sustainably improve data quality enables faster, more accurate, and easier collaboration between OEMs, fleet operators, spare parts providers, workshops, and product users, which leads to a new value proposition around minimizing painful operational downtime.
AI and Data Quality
Currently, we are seeing numerous concepts emerge where AI, particularly AI agents, can be highly valuable for PLM. However, we also know that in legacy environments, the overall quality of data is poor. How do you envision AI supporting PLM processes, and where should you start? Or has it already started?
It’s like mining for rare elements—sifting through massive amounts of legacy data to find the diamonds. Is it worth the effort, especially when diamonds can now be manufactured? AI certainly makes the task faster and easier. Interestingly, Elon Musk recently announced plans to use AI to rewrite legacy data and create a new, high-quality knowledge base. This suggests a potential market for trusted, validated, and industry-specific legacy training data.
Will OEMs sell it as valuable IP, or will it be made open source like Tesla’s patents?
AI also offers enormous potential for data quality and governance. From live monitoring to proactive guidance, adopting this approach will become a much easier business strategy. One can imagine AI forming the core of a company’s Digital Thread—no longer requiring rigidly hardwired systems and data flows, but instead intelligently comparing team data and flagging misalignments.
That said, data alignment remains complex, as discrepancies can be valid depending on context.
A practical starting point?
Data Quality as a Service. My former company, Quick Release, is piloting an AI-enabled service focused on EBoM to MBoM alignment. It combines a data quality platform with expert knowledge, collecting metadata from PLM, ERP, MES, and other systems to map engineering data models.
Experts define quality rules (completeness, consistency, relationship integrity), and AI enables automated anomaly detection. Initially, humans triage issues, but over time, as trust in AI grows, more of the process can be automated. Eventually, no oversight may be needed; alerts could be sent directly to those empowered to act, whether human or AI.
Summary
We hope the discussions in parts 1 and 2 helped you understand where to begin. It doesn’t need to stay theoretical or feel unachievable.
- The first step is simple: recognise product data as an asset that powers performance, not just admin.
Then treat it accordingly. - You don’t need a 5-year roadmap or a board-approved strategy before you begin. Start by identifying the product data that supports your most critical workflows, the stuff that breaks things when it’s wrong or missing. Work out what “good enough” looks like for that data at each phase of the lifecycle.
Then look around your business: who owns it, who touches it, and who cares when it fails? - From there, establish the roles, rules, and routines that help this data improve over time, even if it’s manual and messy to begin with. Add tooling where it helps.
- Use quality KPIs that reflect the business, not the system. Focus your governance efforts where there’s friction, waste, or rework.
- And where are you already getting value? Lock it in. Scale what works.
Conclusion
It’s not about perfection or policies; it’s about momentum and value. Data quality is a lever. Data governance is how you pull it.
Just start pulling- and then get serious with your AI applications!
Are you attending the PLM Roadmap/PDT Europe 2025 conference on
November 5th & 6th in Paris, La Defense?
There is an opportunity to discuss the future of PLM in a workshop before the event.
More information will be shared soon; please mark November 4th in the afternoon on your agenda.
First, an important announcement. In the last two weeks, I have finalized preparations for the upcoming Share PLM Summit in Jerez on 27-28 May. With the Share PLM team, we have been working on a non-typical PLM agenda. Share PLM, like me, focuses on organizational change management and the HOW of PLM implementations; there will be more emphasis on the people side.
Often, PLM implementations are either IT-driven or business-driven to implement a need, and yes, there are people who need to work with it as the closing topic. Time and budget are spent on technology and process definitions, and people get trained. Often, only train the trainer, as there is no more budget or time to let the organization adapt, and rapid ROI is expected.
This approach neglects that PLM implementations are enablers for business transformation. Instead of doing things slightly more efficiently, significant gains can be made by doing things differently, starting with the people and their optimal new way of working, and then providing the best tools.
The conference aims to start with the people, sharing human-related experiences and enabling networking between people – not only about the industry practices (there will be sessions and discussions on this topic too).
If you are curious about the details, listen to the podcast recording we published last week to understand the difference – click on the image on the left.
And if you are interested and have the opportunity, join us and meet some great thought leaders and others with this shared interest.
Why is modern PLM a dream?
If you are connected to the LinkedIn posts in my PLM feed, you might have the impression that everyone is gearing up for modern PLM. Articles often created with AI support spark vivid discussions. Before diving into them with my perspective, I want to set the scene by explaining what I mean by modern PLM and traditional PLM.
Traditional PLM
Traditional PLM is often associated with implementing a PLM system, mainly serving engineering. Downstream engineering data usage is usually pushed manually or through interfaces to other enterprise systems, like ERP, MES and service systems.
Traditional PLM is closely connected to the coordinated way of working: a linear process based on passing documents (drawings) and datasets (BOMs). Historically, CAD integrations have been the most significant characteristic of these systems.
The coordinated approach fits people working within their authoring tools and, through integrations, sharing data. The PLM system becomes a system of record, and working in a system of record is not designed to be user-friendly.
Unfortunately, most PLM implementations in the field are based on this approach and are sometimes characterized as advanced PDM.
You recognize traditional PLM thinking when people talk about the single source of truth.
Modern PLM
When I talk about modern PLM, it is no longer about a single system. Modern PLM starts from a business strategy implemented by a data-driven infrastructure. The strategy part remains a challenge at the board level: how do you translate PLM capabilities into business benefits – the WHY?
More on this challenge will be discussed later, as in our PLM community, most discussions are IT-driven: architectures, ontologies, and technologies – the WHAT.
For the WHAT, there seems to be a consensus that modern PLM is based on a federated
I think this article from Oleg Shilovitsky, “Rethinking PLM: Is It Time to Move Beyond the Monolith?“ AND the discussion thread in this post is a must-read. I will not quote the content here again.
After reading Oleg’s post and the comments, come back here
The reason for this approach: It is a perfect example of the connected approach. Instead of collecting all the information inside one post (book ?), the information can be accessed by following digital threads. It also illustrates that in a connected environment, you do not own the data; the data comes from accountable people.
Building such a modern infrastructure is challenging when your company depends mainly on its legacy—the people, processes and systems. Where to change, how to change and when to change are questions that should be answered at the top and require a vision and evolutionary implementation strategy.
A company should build a layer of connected data on top of the coordinated infrastructure to support users in their new business roles. Implementing a digital twin has significant business benefits if the twin is used to connect with real-time stakeholders from both the virtual and physical worlds.
But there is more than digital threads with real-time data. On top of this infrastructure, a company can run all kinds of modeling tools, automation and analytics. I noticed that in our PLM community, we might focus too much on the data and not enough on the importance of combining it with a model-based business approach. For more details, read my recent post: Model-based: the elephant in the room.
Again, there are no quotes from the article; you know how to dive deeper into the connected topic.
Despite the considerable legacy pressure there are already companies implementing a coordinated and connected approach. An excellent description of a potential approach comes from Yousef Hooshmand‘s paper: From a Monolithic PLM Landscape to a Federated Domain and Data Mesh.
You might recognize modern PLM thinking when people talk about the nearest source of truth and the single source of change.
Is Intelligent PLM the next step?
So far in this article, I have not mentioned AI as the solution to all our challenges. I see an analogy here with the introduction of the smartphone. 2008 was the moment that platforms were introduced, mainly for consumers. Airbnb, Uber, Amazon, Spotify, and Netflix have appeared and disrupted the traditional ways of selling products and services.
The advantage of these platforms is that they are all created data-driven, not suffering from legacy issues.
In our PLM domain, it took more than 10 years for platforms to become a topic of discussion for businesses. The 2015 PLM Roadmap/PDT conference was the first step in discussing the Product Innovation Platform – see my The Weekend after PDT 2015 post.
At that time, Peter Bilello shared the CIMdata perspective, Marc Halpern (Gartner) showed my favorite positioning slide (below), and Martin Eigner presented, according to my notes, this digital trend in PLM in his session:” What becomes different for PLM/SysLM?”
2015 Marc Halpern – the Product Innovation Platform (PIP)
While concepts started to become clearer, businesses mainly remained the same. The coordinated approach is the most convenient, as you do not need to reshape your organization. And then came the LLMs that changed everything.
Suddenly, it became possible for organizations to unlock knowledge hidden in their company and make it accessible to people.
Without drastically changing the organization, companies could now improve people’s performance and output (theoretically); therefore, it became a topic of interest for management. One big challenge for reaping the benefits is the quality of the data and information accessed.
I will not dive deeper into this topic today, as Benedict Smith, in his article Intelligent PLM – CFO’s 2025 Vision, did all the work, and I am very much aligned with his statements. It is a long read (7000 words) and a great starting point for discovering the aspects of Intelligent PLM and the connection to the CFO.
You might recognize intelligent PLM thinking when people and AI agents talk about the most likely truth.
Conclusion
Are you interested in these topics and their meaning for your business and career? Join me at the Share PLM conference, where I will discuss “The dilemma: Humans cannot transform—help them!” Time to work on your dreams!
Join us to discuss the (Intelligent) PLM dream
In the last two weeks, I have had mixed discussions related to PLM, where I realized the two different ways people can look at PLM. Are implementing PLM capabilities driven by a cost-benefit analysis and a business case? Or is implementing PLM capabilities driven by strategy providing business value for a company?
Most companies I am working with focus on the first option – there needs to be a business case.
This observation is a pleasant passageway into a broader discussion started by Rob Ferrone recently with his article Money for nothing and PLM for free. He explains the PDM cost of doing business, which goes beyond the software’s cost. Often, companies consider the other expenses inescapable.
At the same time, Benedict Smith wrote some visionary posts about the potential power of an AI-driven PLM strategy, the most recent article being PLM augmentation – Panning for Gold.
It is a visionary article about what is possible in the PLM space (if there was no legacy ☹), based on Robust Reasoning and how you could even start with LLM Augmentation for PLM “Micro-Tasks.
Interestingly, the articles from both Rob and Benedict were supported by AI-generated images – I believe this is the future: Creating an AI image of the message you have in mind.
When you have digested their articles, it is time to dive deeper into the different perspectives of value and costs for PLM.
From a system to a strategy
The biggest obstacle I have discovered is that people relate PLM to a system or, even worse, to an engineering tool. This 20-year-old misunderstanding probably comes from the fact that in the past, implementing PLM was more an IT activity – providing the best support for engineers and their data – than a business-driven set of capabilities needed to support the product lifecycle.
The System approach
Traditional organizations are siloed, and initially, PLM always had the challenge of supporting product information shared throughout the whole lifecycle, where there was no conventional focus per discipline to invest in sharing – every discipline has its P&L – and sharing comes with a cost.
At the management level, the financial data coming from the ERP system drives the business. ERP systems are transactional and can provide real-time data about the company’s performance. C-level management wants to be sure they can see what is happening, so there is a massive focus on implementing the best ERP system.
In some cases, I noticed that the investment in ERP was twenty times more than the PLM investment.
Why would you invest in PLM? Although the ERP engine will slow down without proper PLM, the complexity of PLM compared to ERP is a reason for management to look at the costs, as the PLM benefits are hard to grasp and depend on so much more than just execution.
See also my old 2015 article: How do you measure collaboration?
As I mentioned, the Cost of Non-Quality, too many iterations, time lost by searching, material scrap, manufacturing delays or customer complaints – often are considered inescapable parts of doing business (like everyone else) – it happens all the time..
The strategy approach
It is clear that when we accept the modern definition of PLM, we should be considering product lifecycle management as the management of the product lifecycle (as Patrick Hillberg says eloquently in our Share PLM podcast – see the image at the bottom of this post, too).
When you implement a strategy, it is evident that there should be a long(er) term vision behind it, which can be challenging for companies. Also, please read my previous article: The importance of a (PLM) vision.
I cannot believe that, although perhaps not fully understood, the importance of a data-driven approach will be discussed at many strategic board meetings. A data-driven approach is needed to implement a digital thread as the foundation for enhanced business models based on digital twins and to ensure data quality and governance supporting AI initiatives.
It is a process I have been preaching: From Coordinated to Coordinated and Connected.
We can be sure that at the board level, strategy discussions should be about value creation, not about reducing costs or avoiding risks as the future strategy.
Understanding the (PLM) value
The biggest challenge for companies is to understand how to modernize their PLM infrastructure to bring value.
* Step 1 is obvious. Stop considering PLM as a system with capabilities, but investigate how you transform your infrastructure from a collection of systems and (document) interfaces towards a federated infrastructure of connected tools.
Note: the paradigm shift from a Single Source of Truth (in my system) towards a Nearest Source of Truth and a Single Source of Change.
* Step 2 is education. A data-driven approach creates new opportunities and impacts how companies should run their business. Different skills are needed, and other organizational structures are required, from disciplines working in siloes to hybrid organizations where people can work in domain-driven environments (the Systems of Record) and product-centric teams (the System of Engagement). AI tools and capabilities will likely create an effortless flow of information within the enterprise.
* Step 3 is building a compelling story to implement the vision. Implementing new ways of working based on new technical capabilities requires also organizational change. If your organization keeps working similarly, you might gain some percentage of efficiency improvements.
The real benefits come from doing things differently, and technology allows you to do it differently. However, this requires people to work differently, too, and this is the most common mistake in transformational projects.
Companies understand the WHY and WHAT but leave the HOW to the middle management.
People are squeezed into an ideal performance without taking them on the journey. For that reason, it is essential to build a compelling story that motivates individuals to join the transformation. Assisting companies in building compelling story lines is one of the areas where I specialize.
Feel free to contact me to explore the opportunity for your business.
It is not the technology!
With the upcoming availability of AI tools, implementing a PLM strategy will no longer depend on how IT understands the technology, the systems and the interfaces needed.
As Yousef Hooshmand‘s above image describes, a federated infrastructure of connected (SaaS) solutions will enable companies to focus on accurate data (priority #1) and people creating and using accurate data (priority #1). As you can see, people and data in modern PLM are the highest priority.
Therefore, I look forward to participating in the upcoming Share PLM Summit on 27-28 May in Jerez.
It will be a breakthrough – where traditional PLM conferences focus on technology and best practices. This conference will focus on how we can involve and motivate people. Regardless of which industry you are active in, it is a universal topic for any company that wants to transform.
Conclusion
Returning to this article’s introduction, modern PLM is an opportunity to transform the business and make it future-proof. It needs to be done for sure now or in the near future. Therefore PLM initiatives should be considered from the value point first instead of focusing on the costs. How well are you connected to your management’s vision to make PLM a value discussion?
Enjoy the podcast – several topics discuss relate to this post.
Jos Voskuil's Weblog 
[…] (The following post from PLM Green Global Alliance cofounder Jos Voskuil first appeared in his European PLM-focused blog HERE.) […]
[…] recent discussions in the PLM ecosystem, including PSC Transition Technologies (EcoPLM), CIMPA PLM services (LCA), and the Design for…
Jos, all interesting and relevant. There are additional elements to be mentioned and Ontologies seem to be one of the…
Jos, as usual, you've provided a buffet of "food for thought". Where do you see AI being trained by a…
Hi Jos. Thanks for getting back to posting! Is is an interesting and ongoing struggle, federation vs one vendor approach.…