Conway’s Law – the PLM killer!

October 14, 2026 in Agentic AI, Blocking change, Change, collaboration, Conway's Law, digital thread, Multidisciplinary, PLM | Leave a comment

During my summer break, I have been thinking about why companies struggle to start a digital transformation and how businesses keep going, struggling to change.

I ended up with Conway’s Law as a demonstration of the biggest inhibitor to digital transformation in companies.

But before that, I need to make two basic statements that form the basis of my plea.

 

1.    PLM is a strategy

When we mention PLM without the word system, we should think of it as a strategy supported by connected, federated systems.

Most of the time, when I hear people talk about PLM, it sounds like they are talking about their PLM system. A core system needed to implement a Product Lifecycle Management strategy.

For me, PLM (Product Lifecycle Management) starts with the core business processes a company needs to implement to remain a long-term (sustainable) product or solutions provider. More on the solution provider dimension later.

Every company that sells products has a product lifecycle management strategy – see the image above.

When a company focuses only on product data, I am tempted to call it PDM (Product Data Management), which can still support different disciplines.

For me, PLM is the operational environment that helps companies manage their products/solutions throughout the product lifecycle, which means it is influenced by sales, R&D, engineering, manufacturing, service and, increasingly, regulations.

This broad scope makes it clear to me that no single PLM system can effectively support all these demands while providing a user-friendly interface. Even if you are SAP or one of the three major PLM vendors.

To remember:

PLM (Product Lifecycle Management) is a strategy supported by a federated infrastructure, where a PLM or PDM system/platform can be one of the main components.

 

2.    Conway’s Law

If you have read my blog posts before, you might have come across Conway’s Law; although it is not a real law, more a description of a phenomenon, it’s largely true. Compare it to Moore’s Law or Murphy’s Law.

Let’s start with THE LAW:

Conway’s Law is an observation about how organizations design systems, formulated by computer scientist Melvin Conway in 1967:

“Any organization that designs a system… will inevitably produce a design whose structure is a copy of the organization’s communication structure.”

Conway meant that the organizational architecture of what you build tends to mirror the shape of the team that builds it. If you have a siloed organisation with Sales, R&D, Manufacturing and Service, you will build processes and install systems to support those siloes.

The reason: People can only design what they can communicate about. Coordination across team boundaries is harder than coordination within a team, so design decisions cluster along organisational boundaries.

This observation makes what I often call the COORDINATED approach, the approach companies are following and feel comfortable with – image below:

 

People can work in their silos, then share information in a linear, document-driven process when requested and needed. Sometimes it is not a document but a Bill of Materials. Handling the BOM is a  great example of Conway’s Law that still exists.

 

An example: The PDM(PLM) – ERP integration

One topic that keeps our community busy is PDM-ERP integration, or, in a more advanced setting, PLM-ERP integration, especially when you consider the MBOM.

The ongoing debate between two of my respected peers, Prof. Dr. Jörg W. Fischer and Martin Eigner, leads to many comments and discussions.

A recent debate can be found here: Turbulent times in PLM: The EBOM-MBOM chain is shaking, AI is rattling everything.

Without going into the debate, as there are many dimensions in the PLM domain, the first question to ask is: WHY DO WE PERCEIVE IT AS DIFFICULT?

Let’s have a look through Conway’s eyes.

  • Two silos need to collaborate: engineering on one side and manufacturing preparation, purchasing and manufacturing on the other. Each side has its favorite system, and that’s where the problem starts.
  • Instead of analyzing the preferred process, the focus shifts to how to get data into my preferred system easily and reliably. Instead of People, Processes, Data and Tools, it becomes a Tools, Data and Process discussion. People are not considered anymore.

This approach can lead to ludicrous situations, where an engineer has to request a part number when designing a new product, or, in another case, use a temporary part number until the part is mature enough to be registered in ERP, according to the ERP standard. These are real examples I encountered.

Historically, SAP drove these scenarios, as its material master message and MDM approach were built around a single connected manufacturing environment.

For SAP, the bridge to engineering is built through custom solutions at the CAD level (CIDEON) or EBOM  level, e.g., ECTR (DSC).

Technology has never limited a fluent PDM-ERP interaction, as long as you start at the process level before touching the systems.

I have written many posts around this topic in the past too. Here are a few of them:

2026: The BOM revisited – what are the challenges

2023: The rise and fall of the BOM

2020: Learning from the past

For me, the discussion is closed; many companies already know the theory and best practices from the past or for their current operations. But will they work on a better future?

 

The future and Conway’s Law

In my network, many PLM experts, like Prof. Jörg Fischer or Martin Eigner, can describe an ideal future approach to product lifecycle management. But Oleg Shilovitsky, Benedict Smith, Doug Macdonald and many other voices also describe a potential future, often inspired by an additional graph database layer, sometimes better supported by an ontology. The whole idea is that such an environment is a needed foundation for agent AI.

For example, I like the concept Martin Eigner expresses in the technical architecture. On paper (or slide), the concept looks explainable, though the details are missing.

And of course all the PLM software vendors have similar offerings related to the concept above – the only question to ask yourself as an enterprise is:

“Who owns the data and who owns the knowledge – be aware of a lock-in”

At the upcoming PLM Roadmap & PDT Europe conference in Gothenburg on November 4th & 5th, we will learn about and discuss interesting future concepts, including the application of agentic AI.

Here, I am curious about the following sessions I saw in the agenda:

  • Key Findings in the Arrowhead fPVN Project – A Knowledge Graph-Based Digital Twin for Semantic Lifecycle Interoperability
  • AI in PLM – Balancing Innovation with Stable Enterprise Architecture
  • Digital Threads for Digital Agents & Collaboration: Connecting ALM, Mechanics, E/E, Software and Production
  • Ontology-based Data Governance and Enterprise Data Modelling: Standards, AI Readiness and Governance Processes for PLM Data Management, a “Data Town Plan” Approach
  • Practical AI Transformation in Enterprise PLM

Of course, there are more interesting presentations to watch and discussions to have, so if you are interested in a potential technical future, join us in Gothenburg; there are still places left; last year the conference sold out.

 

How to escape from Conway?

The digital thread is one of those topics that appeared even before AI and has now become a theme companies should implement, supported by their software vendors and system integrators. In many boardrooms, the term “Digital Thread” is mentioned to address the lack of end-to-end visibility in the company, finally enabling full traceability.

However, more important, I believe, is the horizontal business thread, where disciplines work together in real time. Technology and, for sure, AI agents can facilitate this approach; however, as long as every silo in the organisation is optimized for its own performance (Conway’s Law again), no horizontal business thread will emerge.

I wrote about this topic in June, addressing the mood in the field at the time: The Human Thread, breaking the (PLM) blame game, where I look into the alerting messages that PLM is failing, already with Conway behind the scenes.

I don’t believe Product Lifecycle Management is failing; businesses are failing to transform their organisations, which will lead to new, better operating models.

Too many future strategies are built on an existing siloed organisation, blocking the potential change needed.

A great book to read in this context is Reshuffle by Sangeet Paul Choudary; he describes the fundamental business changes required to break Conway’s trap.

The challenge is that you cannot delegate this job to middle management; top management needs to understand and execute it once they have mastered the potential strategy.

I consider this one of my major topics for the coming years: educating and coaching organisations to escape Conway’s Law and fully benefit from an AI-supported digital transformation.

Conclusion

This time, a short conclusion – Conway is the elephant in the room  – what do you think?

 

 

 

Talking Design for Sustainability with Pierre David (CIMPA)

October 4, 2026 in Design for Sustainabillity, digital thread, PLM, Supplier Collaboration, Sustainability | 1 comment

The Design for Sustainability team withing the PLM Green Global Alliance, Matthew Sullivan (NCC) and Erik Rieger (PTC) have been active in the background during the summer months to further explore the actions for the Design for Sustainability workgroup.

One of their actions was to reconnect with Pierre David, with whom we, as a PGGA, spoke in a broader context about a year and a half ago when discussing sustainability activities within his company, CIMPA PLM Services: PLM and Sustainability: talking with CIMPA.

Follow the link to learn more about this interview.

In a recent PGGA discussion, Pierre David shared his perspective on how Design for Sustainability (DfS) is evolving from an environmental assessment activity into a core part of product development. Central to this evolution is a shift from traditional “eco-design” towards Design to Value, where sustainability is considered alongside cost, technical performance and other engineering objectives rather than being managed as a separate workstream.

You can watch and listen to the full discussion here on our YouTube channel:

What we discussed

A recurring theme throughout the conversation was the growing maturity of sustainability reporting, but with a lack of integration with engineering methods. Many organisations now have well-established processes for measuring and communicating sustainability performance, driven by Corporate Social Responsibility reporting, Science Based Targets initiative commitments and sustainability ratings.

However, Pierre highlighted a disconnect between these activities and the engineering teams that design products. While sustainability metrics are increasingly available at a corporate level, they are not always accessible at the point where engineering decisions are made. As a result, sustainability can become an activity that reports on outcomes rather than influences them.

Part of the challenge is expertise!

Established engineers often lack DfS training, so those who want to work on sustainability may lack the technical skills. Additionally, engineers are typically driven by improvements in cost, weight or performance, whereas sustainability’s financial benefits are often less clear.

The second challenge is tooling!

While PLM providers are increasingly introducing sustainability capabilities, the current landscape remains fragmented. Designers often have to work across multiple specialist applications, databases and data sources, making sustainability analysis hard to embed in day-to-day engineering workflows. Engineers already work within established processes and digital toolsets, and introducing additional sustainability activities can create friction if they are not embedded within existing workflows.

What to do?

To address this challenge, Pierre discussed EFCAT (Environmental and Financial Cost Analysis Technology), a parametric modelling approach now integrated within 3DEXPERIENCE, designed to provide designers with rapid environmental and cost insights during the early stages of product development.

One key finding of EFCAT is that environmental and costing models are about 80% similar in their inputs, structures and assumptions. Both disciplines require materials, manufacturing processes, transportation routes and supplier information.

Rather than treating environmental analysis and cost analysis as separate activities, Pierre advocates bringing them together through a common Design to Value approach. This lets engineers assess environmental and financial impacts simultaneously, reducing duplicated effort and improving decision-making.

 

Looking Ahead

Looking ahead, Pierre highlighted several challenges likely to shape the sustainability landscape in the coming years.

  • The first is the growing focus on Digital Product Passports (DPPs). As new regulations begin to take effect, including the battery passport requirements arriving in 2027, sustainability teams are expected to spend significant time preparing the data, governance and processes needed to support compliance.
  • He also identified supplier data as one of the biggest unresolved challenges. Accurate Scope 3 reporting relies on environmental data from suppliers, yet widespread adoption will require greater trust across the supply chain. Suppliers must be able to share environmental information without exposing commercially sensitive data that could reveal their cost structures or affect future negotiations.
  • Finally, Pierre highlighted the need to better support smaller suppliers. While large organisations may have dedicated sustainability teams, many lower-tier suppliers lack the resources to employ sustainability specialists. The industry therefore needs to provide simpler methods, clearer guidance and practical rules that allow smaller businesses to calculate and share Scope 1 and Scope 2 emissions data without creating a significant financial burden.

 

Key Takeaways

The discussion highlighted that the challenge facing Design for Sustainability is no longer simply measuring environmental impact. Instead, the focus is shifting towards integrating sustainability into engineering decisions and existing product development workflows.

Pierre’s challenge to the PLM Green Global Alliance is to compile a benchmark of PLM providers to map their actual sustainability features and identify gaps between platforms, providing key insight into the sustainability tooling at engineers’ fingertips.

The DfS team has started working on it, and feel free to contact us if you represent a (PLM) provider supporting design for sustainability in a broader sense than impact analysis. Matthew or Erik will be happy to connect with you.

 

Join us at the PLM Roadmap & PDT 2026 for more discussions related to PLM and Sustainability. Have a look at the agenda.

 

 

The week after CONTACT Open World 2026

September 28, 2026 in Agentic AI, Artificial Intelligence, digital thread, Observations, PDM, PIM, Platform, PLM | Leave a comment

Back on track after a side step (see: I am back), I restarted my PLM activities, and WOW,  was I lucky with the first conference on my list: CONTACT Open World 2026 in Bonn.

Melanie Stadtler invited me as a guest (thanks, Melanie 🙏), and for the first time I connected in depth with CONTACT Software, their partners and customers.

I knew CONTACT Software from a few brief interactions, but now I could experience their full ecosystem. As CONTACT Software has historically been active in the DACH region, our paths rarely crossed – most of my activities were elsewhere in Europe.

So my key question was: Who is CONTACT Software, and where would I position them in an SAP/Teamcenter-dominated market?

Around 650 people attended this year’s biannual event. The World Conference Center Bonn, along the Rhine, was a great venue for sessions, networking and evening entertainment.

The conference theme was Industrial AI – from top floor to shop floor.

Before the event, there was a great warm-up party with dinner and a variety show. Beyond the impressive show, it was a great first chance to network and catch up with peers. And then the event started.

 

Industrial AI — from Top Floor to Shop Floor

Maximilian Zachries, Chief Operating Officer (COO) at CONTACT software, kicked off day 1 sharing CONTACT’s ambition and strategy to grow, with a repackaging of their solution – the CONTACT elements as the image below shows.

In the domain of PLM, I would position CONTACT software as a platform with a few foundational layers and op top of that customers can implement Elements, functional, pre-configured blocks on the platform or connect to other systems in the enterprise architecture.

Maximilian Zachries showed the unification of CIM database, Office, Project and IoT into a single collaboration platform based on Elements – coordinated PLM in my terminology.

Openness is a key driver for CONTACT software, so connectivity is crucial.

The image also shows the intelligence layer, named Fourier, which CONTACT has released and includes Elements related to its AI capabilities.

Initially focused on enhancing functionality within the platform and, in upcoming releases, also adding support for MCP connectivity, agentic AI, AI coding and even the option to bring your own model (BYOM)

The mood of the session was much more down-to-earth and factual than at other conferences I used to go to. The understated German culture was evident in this session and in other sessions too, which was a relief for me as a VirtualDutchman.

CONTACT’s intention to grow is also visible in its recent acquisitions and partnerships, including a significant investment in team neusta SE.

 

You snooze, you lose.

Markus Dietrich (CEO, team neusta) then illustrated the complementary value they bring: modern front-end development, data-driven capabilities and dashboards on top of the CONTACT platform – the modern connected PLM in my terminology.

The trend, as Markus stated, is from components to systems, and in the long run from selling products to delivering products as a service.

Driven by economic survival, it is crucial for a sustainable, circular economy.

Companies should start exploring data-driven capabilities now – AI only performs reliably when the underlying data is correct and in the right context.

Hence the title: you snooze, you lose.

 

From Product Truth to Possible Futures – AI and the future of lifecycle management

Frank Patz-Brockmann, Chief Product Officer at CONTACT Software, gave an interesting presentation, discussing AI’s impact on PLM—particularly in engineering.

We already see what AI does in software development. And as the image below illustrates, the real human value is in dealing with the intent—the WHY behind what we do.


Of course, we should not forget that software changes are relatively cheap. A code change typically doesn’t affect the stock in the warehouse or your selected and approved suppliers, as it would in a mechanical world—one reason manufacturers push as many product features as possible into software.

Still, as Frank mentioned, creating engineering artifacts today is expensive and requires specific skills. As AI advances, authoring tools will likely become capabilities of the engineering system, while humans will still be needed to decide intent—the broader context AI cannot address through its trained models.


His ultimate conclusion: in the long run, traditional PLM systems built as the “Single Source of Truth” will be replaced or superseded by a collection of federated data sources, accessed and managed by AI agents. A statement that is supported by many of my forward-looking peers in the PLM community.

From systems to platforms to knowledge infrastructures.

After the guided tours—an excellent way to get a quick impression of sponsors grouped around a theme—it was time for the afternoon sessions.

 

From Connected R&D to Intelligent R&D: Apollo Tyres’ Digital Transformation Journey

I listened with great interest to Hizmy Hassen, Chief Digital Officer at Apollo Tyres. Having worked with tyre companies before, and having a soft Dutch spot for their Vredestein brand, much of his story felt familiar.

What caught my attention even more was their “New House strategy” as shown below:

Too often, I have seen companies start their transformation from their pains and wishes, wrapped in a project based on the known As-Is situation. External consultants like this approach too—it lets them sell interviews and reporting while getting paid to learn the company. It is the traditional business model, and I understand it.

However, I believe in a different approach: define the desired To-Be status from the company’s business strategy for the coming years, then derive the enterprise processes and the underlying data model needed to get there.

From there, you define the journey.

Instead of looking back and fixing what went wrong—an inherently negative mindset—the company looks forward, fostering a positive, aligned attitude when everyone is informed and on board.

Of course, Organizational Change Management becomes crucial here, as systems, the organization, and even business models can and must change.

I saw exactly this mindset in Hizmy’s New House strategy.

One of his best examples: introducing AI/automation as the natural way of working with data in a context where data is available. And as his concluding slide showed, this forward-looking approach is already delivering its first wins.

 

Success Under Extreme Conditions – What Enables Innovation Under Pressure

During the afternoon sessions, attendees chose from various tracks, and we concluded the day with an inspiring keynote from Jutta Kleinschmidt, a famous race car driver (Dakar), physicist and author, who shared how to succeed in an unpredictable environment.

After a great networking evening on board the Ocean Diva, sailing on the Rhine, we started Day 2 with an interesting opening session: the CONTACT software product management team shared their plans and roadmap.

 

Roadmap CONTACT Elements: opening the next chapter

What surprised me first was the roadmap’s level of detail. Most vendors talk about roadmaps but rarely share specifics or commit to capabilities before a release is actually available. Here, I saw the opposite.

Some random thoughts I noted down:

  • CONTACT Elements is primarily a platform of connected capabilities. It provides a foundation through its Elements, and customization remains possible. I did not hear any Out-Of-The-Box messaging.
  • The focus on delivering 3D Search capabilities surprised me somewhat, as I have seen this capability in typical PLM systems before. I also assumed there would be vendor-independent solutions in the market to connect to. Apparently not.
  • The roadmap suggests the current mindset is still to deliver more and more standard, traditional PLM capabilities. This makes it harder to distinguish between being a platform and being a PLM system.
  • Combining ALM and MBSE into a single Element suggests that the full complexity of managing systems—hardware and software—through a model-based approach is not yet fully developed. Both ALM and MBSE could be domains on their own, with many elements.

Of course, these are first impressions, and as I am not a CONTACT software insider, I am eager to learn more.

 

In the afternoon, there were more thematic sessions. I want to highlight one close to my heart:

 

Circular by Design – Anchoring Sustainability in Product Strategy

Comparing the agendas of the 2022 and 2024 editions with this year’s, I noticed sustainability has become less prominent — less hype, fewer dedicated topics. Understandable, as we’re overwhelmed by other immediate global challenges, despite the extreme climate warnings we’re facing.

That’s why I was glad to attend this session. In Europe, we see fewer specialized sustainability discussions; however, more and more companies are being forced to anchor sustainability in their product strategy—mainly due to regulations and, hopefully soon, genuine business benefits and customer demands.

Sebastian Weber gave a great overview of the industry’s current state and CONTACT Software’s approach, where other speakers shared their detailed sustainability experiences (this blog space is too short to mention everything. 🙄 I had much more to tell and digest, but I already passed my 1500-word deadline.)

As a core team member of the PLM Green Global Alliance (PGGA), I am happy to announce we’ll publish a dedicated interview with Sebastian on our PGGA channel in mid-October. There, we will definitely dive deeper into CONTACT software’s contribution to sustainability.

 

Conclusion

The CONTACT Open World conference was an eye-opener for me for several reasons. First, it was a warm, open community where I could discuss the typical challenges a medium-sized enterprise faces. Next, the clarity of the presentations and discussions helped me better understand where to position and recommend CONTACT software in the PLM domain as an exploration every potential customer should do.

CONTACT software is currently a niche player, but an interesting company that’s willing to expand. Keep them on your radar.

Next big event to meet

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The PLM Green Global Alliance talks with the INCOSE Sustainability WG.

August 23, 2026 in Circulair Economy, Climate Change, MBSE, PLM, Sustainability | Leave a comment

Back to Work, Back to Reality

After the holidays, we return to work with greater urgency. The big wildfires show that climate change is real.

While addressing climate change requires a long-term vision and the will to move away from fossil fuels, another problem is also emerging: the limits of our planet:

The increasing scarcity of the resources needed to build and maintain the products we use.

Addressing these challenges requires a new approach for both manufacturing companies and consumers.

The heavy, carbon-intensive linear take-make-dispose approach to products needs to be replaced by a systemic approach that accounts for carbon emissions across manufacturing and use, optimizes business models to minimize resource waste, and incorporates thresholds to safeguard biodiversity.

The challenging transformation upon us is often evoked as a twin transition of “Sustainability and Digitalization”. Image below from my 2023 presentation at the PLM Roadmap/PDT conference.


Our last PGGA interview with Adrian Segens from Cambridge Design Partnership addressed Design for Sustainability.

As a reminder: Have a look at our last PGGA interview to get ready for this episode: Design for Sustainability: The PLM Green Global Alliance talks with Adrian Segens.

 

The INCOSE Sustainability Workgroup

We were extremely happy to connect with the INCOSE Sustainability working group, particularly the Sustainability Together team, as it is like our PGGA: a group of experts with diverse backgrounds who have begun addressing sustainability with a systemic approach grounded in Systems Engineering and in modeling a circular and regenerative economy.

We spoke with Hamza Bassam, Trainer, Consultant, Coach and Author at OOSE. and Kai Constantini, Consultant, Trainer, Facilitator and Art Thinking Expert, both with a broad interest and work on sustainability-related topics

Enjoy the 37-minute interview below:

You can download the slides presented (and more) HERE

 

What we have learned

  1. The Essential Role of Systems Engineering (SE) and MBSE: Systems engineering provides a holistic perspective that is crucial for sustainability, as traditional linear processes (design, produce, use, retire) are insufficient to address the complexity of a circular economy and the complex techno-socio-ecological phenomena such as the “Rebound” effect.
  2. Shifting from Eco-Efficiency to Regenerative Design: The session highlights a move beyond “eco-efficiency”—which may simply make a bad process more efficient—toward “eco-effectiveness” and regenerative design.
  3. The Need for New Mental Models and Proven Profitability: A significant barrier to sustainability is the current business mental model, which often prioritizes short-term profit over long-term regenerative practices
  4. The “Sustainable Together” Open-Source Project: a virtual lab to test these concepts, also open for non-INCOSE members (see link in the next paragraph) – a group similar to our PGGA.
  5. Leveraging AI and Data-Driven Environments: Future sustainability efforts will increasingly rely on AI and automated agents to handle complex tasks like Life Cycle Assessments (LCA), which are currently cumbersome for organizations. They will also account for Biodiversity data capture to inform our decision-making process across business and multiple socio-economic layers

 

Want to learn more?

 

Conclusion

The INCOSE Sustainability WG and the PLM Green Global are a perfect match to address sustainability topics. While the PGGA focuses primarily on PLM-related topics, sharing information and educating the field, the INCOSE Sustainability working group enables people from around the globe to discuss and apply these topics on real projects.

To strengthen our relationship, Hamsa Bassam has agreed to join the PGGA core team, and Jos Voskuil will be actively involved in the INCOSE Sustainability Together project starting in Q4 2026.

Feel free to join us too!

 

 

The Human Thread – breaking the (PLM) blame game.

June 15, 2026 in Artificial Intelligence, Brain, Change, collaboration, Data centric, Digital Enterprise, digital thread, Digital Twin, Education, Human Centric, intelligence, PLM | Leave a comment

In nearly twenty years of coaching PLM implementations, I’ve noticed something striking: these projects often mirror politics—not just in complexity, but in the blame game that follows when things go wrong.

When something goes wrong, people rarely see it as an opportunity to solve the issue together. They look for someone to blame instead.

That happens in politics and in Product Lifecycle Management. I wrote about it in 2019, The PLM Blame Game—and most of those observations still hold—although the emphasis has shifted.

But what if the real issue isn’t the system or the technology? What if it’s the human connections—or lack thereof—that determine success?

Political systems/ PLM approaches

In democracies, everyone debates priorities, but progress is slow. Stakeholders defend their own interests, consultants favor preferred solutions, and vendors promise the moon. Long-term plans such as digital transformation often stall.

The result is familiar: each leadership change resets ambitions, leaving users with mixed messages and less commitment – sounds familiar in PLM?.

From: Communication charts around the world

Then there are the autocracies, where a single dominant view determines the path. Usually, that view comes not from the CEO but from the CFO or CIO. These leaders often have a limited understanding of product lifecycle management and instead rely on trusted networks.

That is why some companies choose SAP because “all enterprises run on SAP” or Teamcenter because “everyone in automotive uses Teamcenter.” Strategic consultants reinforce the same pattern with their own preferred solutions.

The result: Surface-level alignment, but resistance beneath the surface—another familiar PLM scenario.

From: Communication charts around the world – 2014 China

In smaller companies, a populist version often appears. Without a strong strategic layer, the loudest voices from vendors and implementers shape the company’s view. That is the riskiest setup because vision and strategy are effectively outsourced. Early in my career, I often heard:

“You know solution XYZ, so tell us what to do.”

The result is predictable: no one in the company feels a true sense of ownership of the business outcome – the type of situations I have been mediating the most.

Of course, the analogy is imperfect. Countries usually lack competition, so citizens cannot simply switch. Still, it is a useful way to frame what happens in PLM.

 

They – not us – are the problem!

In the past, debates focused on who was to blame for project problems, often blaming the stakeholder who was not at the table.

Vendors and implementers blamed customers, vendors and customers blamed implementers, and implementers blamed vendors. My role in PLM mediations was to get everyone into the same room.

 

But one issue always remained:
Blaming the customer is difficult when the customer is assumed to be right – They are paying the bill and not always with pleasure.

 

 

Why 70 % of PLM implementations fail – or not?

For decades, we have heard horror stories about failed PLM implementations, each supposedly explained by one simple cause.

Depending on who tells the story, the culprit is the software, the company culture, poor user involvement, or unrealistic ambitions without a budget or understanding.

But the truth is more nuanced: many of these implementations did not actually fail completely.

People react strongly to the word failure because no one wants to be associated with it.

Yet, in software, ‘failing fast’ is often celebrated—it’s a way to adapt early. PLM is slowly catching on, with the rise of Minimum Viable Product (MVP) approaches. Instead of waiting for a ‘perfect’ big-bang rollout, companies now start with a working foundation and iterate as needs emerge.”

That only works if the company owns its vision and strategy. An MVP approach also demands end-to-end stakeholder involvement, because everyone contributes to the solution. At the same time, our limbic brain works against us: it pushes us to protect what we know and react strongly to change.

That reaction shows up in PLM projects too. The loudest critics get the most attention, which makes it easy to conclude a program failed—even when it is working for most people who have adapted to the change.

And now, a new trend has emerged:
PLM systems are failing!

Now, a new claim is gaining traction: PLM systems themselves are failing. With the rise of AI, traditional vendors are being blamed for failing to provide the right infrastructure or opportunities for AI-enabled capabilities.

After years of success built on legacy platforms, vendors now face growing pressure from opinion leaders calling for change.

Martin Eigner has made this point in several posts:

Oleg Shilovitsky has made similar arguments:

Prof. Dr. Jörg W. Fischer wrote:

Doug Macdonald wrote about the shortcomings of Legacy PLM, which most companies imagine/practice:

I agree with much of this critique, for sure, if you still consider PLM a system rather than a product lifecycle management strategy implemented through a federated infrastructure of systems.

The posts I referred to highlight real problems from the past and suggest that new insights and AI might help us build better businesses. The question is whether that promise will be fulfilled.

 

Creating the human thread

AI could help businesses break through organizational silos by pulling together information across functions.

That would make concepts such as the digital thread and digital twin easier to implement without relying on dedicated interfaces.

This shift creates both opportunity and risk.

If AI reduces the need for siloed optimization, traditional middle-management roles will change. The key question is whether companies are willing to rethink their structures or stay constrained by Conway’s Law.

It could also make many methodology debates less important.

Today, we as consultants often promote methodologies shaped by our own experience or vendor narratives. The long-running eBOM–mBOM debate is a good example. Across industries and platforms, the answer is often more straightforward than the discussion suggests.

As AI absorbs more collective knowledge, the role of PLM experts and consultants will shift. At the Share PLM Summit in Jerez, we discussed what should come next: a stronger focus on human connection.

 

That is why I use the term human thread: the network of relationships that connects people across the business. Michael Finochario (Fino) touches on the same shift in his post on the changing balance between humans and technology, in his review of my session in Jerez.

Others are moving in the same direction. This week, Helene Älander shared a post that makes a similar point.

Helene’s post and the related discussion suggest a growing belief that transformation depends less on technology alone and more on human connection and motivation inside the company.

A quote from Helene’s post, and I recommend reading the full post and thread.

One lesson has stayed with me ever since:
Transformation rarely fails because of technology. It slows down when the distance between executive ambition and middle-management reality becomes too large.

For now, I call this the need for the human thread. A successful transformation starts with an end-to-end human connection across the business, with people treating that connection as a shared priority.

Because people are intrinsically motivated by a human connection.

The human thread requires a new approach, new forms of workshops and learning sessions where leaders, managers, and employees work together on the desired business flow.

Helene Älander points in this direction, and Share PLM supports it through initiatives such as Share The Nest.

Also this year at the Share PLM Summit in Jerez, Andreas Wank described how Pepperl+Fuchs made a breakthrough by bringing people together. As Fino in his review post quoted:

No one on the team wanted to make a decision because every decision affected someone else. So they put 30 people in one room for a week and forced them to make decisions. Not perfect decisions. Working hypotheses. That was a critical insight: In PLM, waiting for perfect certainty kills momentum.

The year before, at the 2025 Share PLM Summit, Andrea Järvrén already shared a similar lesson, describing how Tetra Pak used design sprints to advance its PLM work by prioritizing human interaction.

It is an unstoppable trend – the human thread popping up in more and more conversations.

Conclusion

The time for blaming systems, technology, and methodology should fade into the background. Companies need to focus on building business flow through the human thread—the human connections that drive commitment, motivation, and change.

So, here is the question: Are we ready to stop blaming systems and start building the human thread? Or will we keep repeating the same patterns, just with fancier technology?

Design for Sustainability: The PLM Green Global Alliance talks with Adrian Segens

June 15, 2026 in Change, Circulair Economy, Climate Change, Data centric, Design for Sustainabillity, Digital Enterprise, Green Development, Life Cycle Assessment, PLM, Sustainability | Leave a comment

Erik Rieger and Matthew Sullivian have been active last year, organizing a workgroup related to Design for Sustainability, as you might recall from earlier posts: Towards a shared definition of Design for Sustainability.

As part of this exploration, Erik Rieger and Jos Voskuil had a conversation with Adrian Segens, an experienced professional at Cambridge Design Partnership and a thought leader in packaging, recycling, and sustainability, contributing to industry discussions, reports, and LinkedIn posts.

For that reason, we were happy to record an interview with Adrian, discussing his background in sustainability, the connection to businesses and the concept of Product As A Service – a must for a circular economy.

Enjoy the 36-minute interview below:

The images presented during this recording can be found HERE.

 

What we learned

  • Sustainability is an Economic Imperative: the effort to sustain a “working and livable economy and society” for a global population of eight billion people. The transition to a circular economy is an economic necessity because current resource use and climate emissions are tied directly to how we make and consume products.
  • The shift to ‘Product as a Service’ (PaaS) is essential: Manufacturers must retain ownership of material flows—a cornerstone of circularity and a sustainable economic model. This approach replaces the unpredictability of one-time sales with steady, predictable revenue, long-term customer value, and a reduced reliance on virgin resources.
  • Recycling is a Low Priority on the “Ladder of 10”: A major misconception is that the circular economy is primarily about recycling. In reality, recycling is ranked eighth in a ten-stage hierarchy of circularity. Higher-value strategies include rethinking the business model (the second step), reusing products (the most preferred method), and refurbishing equipment.
  • Digitalization is essential for Scaling: The circular economy cannot scale without digitalization, as we need full traceability of materials and outcomes. Technologies like Digital Product Passports (DPP) and Product Lifecycle Management (PLM) are necessary to provide the end-to-end visibility required to track every product in the field, manage complex return logistics, and collect metadata to improve future designs.

 

Want to learn more?

Adrian recommends that we dive deeper following these links:

 

Conclusion

We all agree that the transition to a circular economy is an economic necessity,  requiring a fundamental shift toward product-as-a-service models. Understanding that product design is the most effective lever for reducing environmental impact, prioritizing high-value actions like reuse and refurbishment over recycling.

For us as a PLM community, the circular economy cannot function at scale without digitalization. Success relies on end-to-end visibility, enabled by modern, data-driven PLM infrastructures, to manage material flows and leverage data for continuous improvement.

We have work to do

PLM and Sustainability / LCA: Talking with Siemens Digital Industries Software

June 8, 2026 in Artificial Intelligence, Data centric, Design for Sustainabillity, Life Cycle Analysis, Life Cycle Assessment, PLM, Sustainability | Leave a comment

It has been quiet at the beginning of this year, with presentations and interviews from the PLM Green Global Alliance, mainly due to several activities from the core PGGA team in the background.

Rich McFall stepped back, and with him, the management of the PGGA website.

Sabine and Klaus Brettschneider have meanwhile migrated the website to a new environment with a more modern look and feel, complemented by a redesign of our logo by our partner CIMPA PLM Services. Sabine’s expertise in e-commerce and digital user experience played a key role in shaping the new platform, while Klaus contributed his experience in sustainability and PLM.

As a group of volunteers, we are proud of the result and continue to work to improve it where needed, while encouraging our members to make active contributions.

Have a look at the website plmgreenalliance.com and give us your feedback (and support).

 

Back to the interviews

Despite political headwinds, businesses have been implementing more sustainability initiatives, and we were curious to hear from PLM vendors and implementers about what they are currently observing and offering to the field.

You can always read about these interviews on our PLM Green Global Alliance website or subscribe to our YouTube channel, @PLM_Global_Green_Alliance, where we share interview recordings.

This time, Klaus Brettschneider, our LCA moderator, and Jos Voskuil spoke with Eduardo Salva from Siemens Digital Industries Software, who was recommended to us as the expert on the global Teamcenter product portfolio, particularly the Sustainability Lifecycle Assessment environment.

 

Siemens Digital Industries

We don’t think Siemens Digital Industries needs an introduction in the world of PLM; with its broad portfolio, you might miss some of its sustainability initiatives and offerings.

Therefore, we were happy to speak to Eduardo Silva about his personal passion and his professional activities within Siemens Digital Industries related to Sustainability.

Enjoy the 36-minute discussion here:

 

What we have learned

  • Siemens enterprise-wide commitment to Sustainability under the “DEGREE” framework, a holistic sustainability program with measurable KPIs across ethics, governance, and environmental impact, targeting full implementation by 2030. Siemens Impact 2025
  • Sustainability at the engineer’s desktop: AI-Driven Sustainability solution within Teamcenter, through a partnership with Makersite, supporting “one-click” LCA calculations. The result: Automated ISO-compliant LCAs/PEFs/EPDs built by engineers to assess eco-design decisions early in the design phase (“shifting left”).
  • Sustainability is no longer optional: it is regulation-driven. Under ESPR, the Digital Product Passport (DPP) will require manufacturers to provide verified, lifecycle-based product data (including carbon footprint), forcing OEMs—especially in automotive and batteries—to pass these reporting requirements down to their suppliers
  • Sustainable engineering is shifting from document-based reporting to structured product data. Regulations today require standardized lifecycle data, creating the foundation for advanced analytics, digital twins, and AI-driven optimization. Platforms like Catena-X are emerging to enable secure data exchange across the value chain

 

Want to learn more?

 

Conclusion

The conversation with Siemens Digital Industries shows that sustainability in PLM is moving from ambition to execution, if you recall our earlier interviews. Now LCA must become part of everyday engineering work, supported by structured product data, automation, and collaboration across the value chain.

With regulations such as ESPR and the Digital Product Passport accelerating the need for reliable lifecycle information, companies that embed sustainability early in design will be better prepared for compliance while also creating opportunities for innovation, transparency, and competitive advantage.

 

 

The long week after the Share PLM Summit in Jerez

June 1, 2026 in Agentic AI, Artificial Intelligence, Brain, Change, Data centric, Human Centric, intelligence, Observations, PLM | Leave a comment

Those who follow my blog know that whenever I visit an event, I push myself to write a review the weekend after to share the experience. However, this time after the conference, I have been exploring further the Andalusian culture, making me realize that this is exactly what makes the conference different and stand out.

Where traditional conferences are often in cold high-tech places, efficiently to reach, making it for attendees an event in their comfort zone, the Share PLM Summit is held in a grand bodega in the unhurried scenery of Jerez de la Frontera.

An experience best described by Helena Alander in her recent post: “I have never taken the time to invest in myself.” – Read the post, she shares a great reflection.

As the focus of Share PLM is to focus on human-centric transformations, there is much more focus on the human experiences of people implementing transformations in their companies.

The human focus translates into a diverse audience and one big common theme for all, instead of a traditional industry or technology focus.

With more than a hundred attendees, the conference felt like a big family gathering where you can easily connect and learn from everyone. I believe this type of conference will be the future in the age of AI.

The many sponsors that joined the conference were also a part of the success. Without their support and human-centric messages, it would be hard to make this event sustainable 😉

If you want to read an impression of each session, Michael Finocchareo made a great effort to share the highlights of each session – you can find all his excellent reviews here: Share PLM Summit – Fino Summary Post Index.

And now some of my personal highlights from the conference!

 

The Role of People in Transformation Programs: Experience LEAN

An interesting learning experience was the session from Javier Sánchez, who is a plant manager at Kerry in Spain, about the implementation of LEAN at several plants in Spain.

Where initially we might think that PLM and plant operations are two different worlds, Javier was able to take us on the journey of implementing a LEAN program for the Spanish plants. There was so much communality to PLM implementations when dealing with behavioural change and the uncertainty of people.

Change can only happen when people in the organisation understand and trust what is going to happen and that they are part of a change for their benefit.

TRUST is the word that I noted down, and to build trust, you can see how Javier shared the org chart of the Kerry Sevilla plant – upside down – people at the top and the manager at the bottom to support everyone.

This image above really illustrates that you have a people-first approach.

Javier further elaborated on the difference of such an approach and how an organization can be fully engaged, as the picture below illustrates.

And after a successful implementation, Javier also warned about the rebound effect.

Where initially the excitement and energy come from the new situation, companies might slowly fall back into the traditional, as over time people have and bring their habits, the pyramid falls over as the image to the left illustrates and Javier shared several potential causes for such a rebound.

Important to see C-level change as #1 point, a point I have seen popping up in many PLM implementations too. After starting with a great vision, new people at the C-level come in questioning the vision (and strategy).

Note: GENBA is a term coming from LEAN, and is also relevant to PLM. It refers to the location where value is created—such as a factory floor, construction site, sales floor, or any workplace where the core work happens.

The concept emphasizes the importance of direct observation and engagement at the source of the action, rather than relying on reports or second-hand information.

It is a key principle in lean manufacturing and continuous improvement methodologies, encouraging leaders to “go to the GENBA” to understand problems and opportunities firsthand.

Combined with his great storytelling skills, Javier took us on an interesting story, very relevant for a human-centric approach and showing that we can learn from other disciplines.

 

Adapting PLM implementation strategy in evolving organizational realities

Susanna Mäentausta, also a guest in our Share PLM season 2 podcast with that time the title The ROI of Digitalization: A Deep Dive into Business Value  gave an interesting lecture about her experiences with the PLM implementation at her current company, Novartis.

She excels in keeping her focus on both PLM business value and strategies to achieve this.

I knew Susanna from her earlier presentation at the Product Innovation 2019 conference in London. Here she stood out because of her strategic and tactical approach to implementing PLM – at that time at Kemira – where she was able to get PLM business benefits to be discussed at the C-level – it was more than a technical story.

You can read my observations from that time here: The weekend after PI PLMx London 2019

In her session this time, she explained all the challenges that she had to address at her current employer. It was not such a nice, linear step-by-step approach as presented by Andreas Wank earlier that day, talking about his implementation challenges at Pepperl & Fuchs.

Susanna’s tactics were all about securing the progress of the PLM project – design for change and awareness in the organisation. Javier Sanchez mentioned that the change at the C-level had a serious impact on the roadmap, as did both Susanna and Andreas. It remains a continuous point of attention when you want to guarantee a long-term outcome.

The image below says it all:

Susanne ended with some tactics:

  • Design for non-removable anchor points that keep the PLM vision connected even when priorities shift.
  • Define the non-negotiable cornerstone for the future: process & design frameworks and necessary authorities.

Her final recommendation was also interesting – the core team should have a clear long-term understanding of the future and work in an entrepreneurial mindset, meanwhile shielding execution from organizational politics.

Don’t get involved in politics – a recommendation that I also often shared, as politics is so much about emotions and subjective arguments, it’s better to work around it in silence.

 

The Human Advantage: Working and Leading in the Age of AI

Helena Gutierrez, well known as one of the founders of Share PLM and her AI-related newsletter, shared her positive view on how AI, in one way, destroyed Share PLM’s actual business practices, as a lot of material development now could be done with the help of AI in hours, compared to days of actual design work.

Companies won’t pay anymore for weeks of development of specific materials, she also pointed to the need for human skills in the future.

I think we agree on the fact that with AI, we will need people who can bridge and work with agentic AI to achieve unmet benefits for organisations. These people will have a special role; they are there for their human skills, combining emotion and logic, potentially a highly rewarding job, however, in smaller quantities than current knowledge workers in companies.

In my session Are Humans Still Resources?, I shared a pessimistic viewpoint and an optimistic end.

The pessimistic part is based on the fact that we humans run on our old biological hardware, the limbic brain, which urges us to save energy (think fast) and may lead to cognitive surrender. This situation might push companies to invest even more in AI and consider humans as a difficult resource to handle – are we back in the early days of the industrial revolution?

The optimistic side, which was also mentioned by others, and we see it happen, is that thanks to AI, the entrepreneur has a much easier life. A lot of the supporting activities for an entrepreneur can now be done using AI agents – the image below from this post by  Dr.Sam Zolfagharian  says it all:

For sure, the discussion will go on between the optimists and the realists (pessimists in disguise)

 

Scaling human capabilities

I am closing this impression with a train of thought that I can’t get out of my head. We can scale tools and resources with AI, leaving only a space for people with a combination of specific human skills – not only deep thinking, but also emotional and empathic roles, like healthcare providers, coaches and entertainers.

These roles are hard to scale – you become a coach by learning from experiences, and AI will not have an “experience transfer function.” How will business scale in the future, as we also see that junior roles in an organisation disappear due to AI?

The topic was also discussed in the interesting AI panel discussion – image above – with a mix of participants. It was a balanced discussion between tech, vision and reality and one of the highlights was the response from Susanna to a question from the audience:

“I don’t know.”

Have you ever seen someone honestly say this in a panel discussion? And what would AI respond? Great to see the human presence.

Where will humans build their experience and skills to think? I wrote about this already in March and have not yet answered: PLM, AI, and the Risk of Cognitive Surrender: A Call to Stay Sharp!

How to stay sharp in an AI-dominated world?

 

Conclusion

The Share PLM summit demonstrated again that a human-centric approach related to product lifecycle management has many benefits, as these shared experiences and outcomes from the discussions are directly applicable. Big kudos to the Share PLM team that dared to invest in such an event last year and exceeded expectations this year.

For those who want to learn more, join us at the upcoming event, Putting People in PLM: A Share PLM Summit Recap! and get excited.

For those who are interested in a lifetime, full-time job, watch this excellent short movie below:

Do we need artificial product memory?

May 4, 2026 in Agentic AI, Artificial Intelligence, Digital Enterprise, Human Centric, PLM | 1 comment

Let me start with a confession: as a kid, I was a classic nerd, drawn to soccer and exact sciences. Math and physics weren’t just subjects—they were my playground.

During my education to become a teacher in Physics and Mathematics, I discovered something even more captivating: programming. It started with my first Apple IIe, where I tackled the challenge of programming with limited memory using machine language, analogue/digital interfaces, Pascal, and C.

Later, I turned to Visual Basic and C++, writing programs to simulate math scenarios, automate AutoCAD tasks, and later develop solutions on top of SmarTeam.

It was not just work—it was how I relaxed – structuring my thinking – would we call it now Vibe coding?

The upside of this experience? Technical and physical concepts never intimidated me – they helped me to see the bigger picture. I was wired to think deeply, patiently, and persistently—skills that have stayed with me ever since.

 

The switch to human

But then I got involved in training and mediating in PLM implementations, where I discovered that technical skills were needed; however, more important were understanding human behavior (not software), communication and  PLM methodology skills.

Many implementations at that time stalled because everyone started with great enthusiasm until the results failed to materialize. The solution was not as expected, too unstable or not possible. And from the point of view of the users, it is too complex and frustrating for them. You can read one of my experiences from that time: Where is my ROI. Mister Voskuil

One of those many interesting discussions

But the budget was often finished, and the enthusiasm was gone. One of my favorite quotes at that time was:

“You never get a second first impression.”

indicating that from the start, you need to anticipate user acceptance, don’t think of a big bang approach and start with understanding and agreeing on the big picture before diving into the details.

 

How many of you have been in this situation?

Although the majority of people in the PLM community agree that human behavior can make or break a PLM implementation, the majority of discussions and focus are most of the time targeting tools and technologies.

#12 – digital skills related to the transformation

Organizational Change Management is often considered too soft to address, particularly in so-called result-driven organizations. Shut up and do the work!

Recently, some PLM software vendors mentioned OCM  as an important activity, sometimes even provided by them. Their business model is to sell as many software licenses as possible, and therefore, they promise best-case scenarios and coverage of business scenarios.

Would you buy your PLM software from a company that says:

 “Our software is great; however, you also need to address a business change program.”

Or would you buy from

“We are a market leader in your business, and thousands of users are currently working happily with our software.”

I believe,  with the experience as a PLM coach, that every PLM implementation should be a people and business discussion first – preferably sponsored at C-level – before jumping on the solutions.

The challenge of this approach is that a human-centric approach depends on people, often hard to scale, as it is a people business, not a software tools business.

 

Digital Transformation is failing

While preparing for the upcoming Share PLM summit in Jerez on May 19-20, I was looking back at why real digital transformation in the PLM domain is still failing – we keep on working mostly in a linear document-driven operating model.

My opinion at this moment: For existing organizations, the move from coordinated to coordinated and connected is too complex for humans.

Despite a great white paper from McKinsey on how organisations could move away from a linear, often document-driven organisation to an organisation working in multidisciplinary product teams, there is no real progress in most organisations.

Changing the organizational structure appears to be so difficult, and this relates to Conway’s Law, which states that systems reflect the organizational structure, presenting a challenge in determining where to start.

Not starting means not failing. And failing is the worst thing you can do at the C-level.

 

And now there is “product memory.”

Is the “product memory” based on an agentic AI layer and an underlying ontology, the next big thing after the connected digital enterprise? Initially formulated by  Benedict Smith and later translated to a more PLM-specific scope by Martin Eigner and Oleg Shilovitsky, we are trying to combine the (boring) systems of record data with all the reasoning and decision-making  – that’s where the knowledge is sitting.

Benedict shared his journey exploring AI and PLM through his True Intelligence newsletter, which I recommend you subscribe to. What I admire about Benedict is the fact that he does his research based on experiments and dialogues with others, without a commercial drive to sell a product or service (at the moment).

You can follow the thought experiments when reading the True Intelligence newsletters from the start.

A theme that came up also in other “the future of PLM” discussions was that traditional PLM only stores the results of a development and delivery process, but the reasoning is missing.

In my opinion, Colab Software was one of the first complementary to PLM startups, with a focus on capturing the discussions and decisions during a design review, as the older image below shows – also, Colab Software is now much more advanced with an AI-supported infrastructure.

Still, the image shows the value; the reasoning that was captured from the communication between different stakeholders in the product development process during design reviews.

More in the traditional PLM domain, Martin and Oleg started developing the tconcept of an agentic AI enterprise driven by a graph-based layer on top of existing enterprise systems as Martin’s image illustrates below.

Where Oleg stays (for me) more in the traditional PLM enterprise world:

e.g., his post  Product Memory Architecture: How PLM Loses Engineering Knowledge and What Comes Next,

Martin zoomed in on his day-to-day customer base in Germany when writing

this post:  The Actual Concept of Product Memory based on a Digital Thread with a vision for the upcoming 5 years.

In addition, less PLM-focused but very data-driven, Jan Bosch wrote a complementary post on his blog related to
the agentic AI approach:  From Copilot to Colleague – the rise of agentic AI.

An interesting quote from this post, valid for us all:

Agent systems require investment in data architecture, workflow mapping, governance frameworks and operational monitoring. Those investments compound. The organization that has deployed agents across its revenue cycle, supply chain and finance operations simultaneously develops deep operational expertise in running agentic systems, which is itself a form of competitive advantage.

And while finalizing this post, there was an interesting discussion related to product memory at The Future of PLM: Introducing Product Memory organized by Fino, also known as Michael Finocchiaro

As a “techie,” I was able to enjoy and follow the discussion about a future infrastructure related to product knowledge. The term “product memory” seems a little overhyped, as if information that is not directly accessible through agents is a cause of failure. The big elephant in the room is where and how to start.

Enjoy the dialogue here:

 

What about a product memory trauma?

In the past, when discussing knowledge graphs, I already posed the question:

“How can knowledge graphs unlearn?”

In the techie world, there was always a hypothetical response for this question, but will it happen in a product memory environment where not everything is 100 percent exact and correct?  Patrick Hillberg,  one of the few PLM teachers, can educate you all about seemingly small mistakes with a big impact.

During the product memory discussion, I heard a statement that only validated data is allowed to be part of the memory.

Has anyone thought about the utopia of this statement?

The ambitious statement that product memory would lead to a single source of truth is, for me, also a utopia. 100 percent correct data does not exist, nor will 100 percent accurate decisions exist. It will be the most likely truth for the moment.

Now compare this with the human brain; when a serious accident happens, the person involved might have trauma from that. Then you need a psychiatrist to fix the trauma, meaning create other memory constructs – rewiring the brain.

While seeing this interesting dialogue with Rob Ferrone (the original Product Data PLuMber) about how Quick Release became a significant consultancy firm with the pragmatic focus on making the data flow (old image below), I had a new thought.

With Rob’s entrepreneurial skills, he might be able to start a new company soon, fixing product memory traumas – as data-governance becomes a commodity.

Will the product data plumber become the first product memory shrink?

 

Conclusion

We are experiencing a fast-moving convergence on future PLM concepts, where the image from Martin Eigner nicely represents such a possible architecture based on “product memory”. The challenge I see is whether we would be able to implement such an architecture to be reliable and supported by humans. Because humans still have their old hardware, the limbic brain, that will try to escape from the perfect world with a single source of truth – they like their truth

This was 2025 – this year, same atmosphere, more experienced & bigger and more to discuss.

Join us here (18) – 19 & 20 May in Jerez

PLM, AI, and the Risk of Cognitive Surrender: A Call to Stay Sharp!

March 23, 2026 in Artificial Intelligence, Brain, Change, Digital Enterprise, Human Centric, Innovation, PLM, Social Media | Leave a comment

Last week  I listened to a Dutch podcast that gave me an unexpected inspiration. The podcast “Zo Simpel is het Niet” (“It is not that simple “in English) is a podcast with a focus on economic topics and trends, not at all about PLM, sometimes a little about the effects of digital transformation and AI is more and more mentioned.

The episode I listened to was about the decline of literacy in the Netherlands. The conclusion is based on research discussed from the  PISA test from the OECD. Learn more about the OECD and PISA test here on this Wiki page.

I asked Notebook LM to make an English slide deck from the podcast – You can download the deck HERE.

The conclusion: We are in a free fall in the Netherlands (image below) and likely in other countries too, because we are reading less, writing less, and consequently thinking less deeply.

Social media was identified as one of the root causes. Short-form content, the endless scroll, the dopamine loop of likes and shares — it is all rewiring how we process information – short-term, quick results without building deeper skills.

And then the connection between social media and the drop in literacy and science skills hit me. We are doing the same thing at the moment with AI!

 

Third Way of Thinking

In my posts, I sometimes refer to Daniel Kahneman’s book and his research: Thinking Fast and Slow, as this is for me a foundational theory for understanding human behavior.

Kahneman describes our brain as a combination of  System 1, which is the fast, intuitive brain (low energy), and  System 2, which is the slow, deliberative one (burns energy). As humans, we avoid using energy when thinking, although nowadays, outside our brains, we are hooked to fossil energy 😉.

Next, I read a research from Steven Shaw and Gideon Nave, from The Wharton School of the University of Pennsylvania,  that indicated that AI is going to have an additional impact on our behavior as humanity.

Their paper Thinking—Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender introduces System 3 as artificial cognition. External, automated, data-driven reasoning that lives not in your brain but in the cloud.

Their central finding is something they call “cognitive surrender” –  access to AI made people more confident, regardless of whether the AI was right or wrong. An enforcement of the Dunning-Kruger effect?

The most vulnerable are people with higher trust in AI, lower need for cognition, and lower fluid intelligence, who showed the greatest cognitive surrender. The least critical thinkers delegate most, and then feel most certain about the result.

 Benedict Smith, my True Intelligence friend, pointed to the same pattern in his post: When the Graph talks back. Read it and think!

Both their conclusions made me even more worried, combined with the results of the Dutch literacy developments – are we all racing downhill?

I believe our brain is a muscle. Like any muscle, it needs resistance to stay strong. You do not become a better cyclist by riding an eBike everywhere — the motor does the work, and your legs lose the real strength needed when you are without your bike. The same applies to cognitive effort.

 

We Have Been Here Before

It is not the first time a transformative technology arrived with enormous promise and created a deeply unequal outcome. The Industrial Revolution reduced most workers to resources while a few became extraordinarily wealthy.

John D. Rockefeller (oil & railroad industries), Andrew Carnegie (steel industry), J.P. Morgan (financing the new industries) and Cornelius Vanderbilt (shipping and railroads) as examples.

These industry leaders did not care so much about humans, and it took roughly a hundred years — and the rise of labour unions — to begin correcting that imbalance.

The AI revolution is moving much faster! And if history teaches us anything, it is that working more efficiently with new tools does not automatically raise your value. The more companies invest in AI solutions, the more pressure there will be to develop your individual skills.

Efficiency without insight is a commodity.

My friend Helena Guitierrez wrote this weekend this supporting post: Preparing for AI Adoption

 

What does it mean for Product Lifecycle Management?

Purposefully, I wrote Product Lifecycle Management to focus on the strategy, and not an all-around capable PLM system, as PLM systems have never quite delivered on their original promise.

The PLM vendors benefited from selling the dream, the consultants benefited from its complexity and the users, initially engineers and later more stakeholders in the product lifecycle, often suffered under rigid processes and complex systems. As the systems were designed to store information. User-friendlyness was not a priority.

Will AI, being layered on top of PLM and other enterprise systems, be the solution for these underperforming systems?

Oleg Shilovitsky believes in that, as you can read in his recent post: PLM’s OpenClaw Moment: How AI Agents Will Break Closed Systems

The risk is that we repeat the same pattern. AI will be positioned as the solution to problems actually caused by poor implementation and insufficient investment in the human side of change.

There is an interesting discussion ongoing about the future of PLM infrastructures, well described recently by Rainer Mewaldt in his post:  What would a 𝗣𝗟𝗠 𝘀𝘆𝘀𝘁𝗲𝗺 look like if it were 𝗱𝗲𝘀𝗶𝗴𝗻𝗲𝗱 𝗳𝗼𝗿 𝗔𝗜 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗴𝗿𝗼𝘂𝗻𝗱 𝘂𝗽?  

The principles are there, like 10 years ago, before AI became hyped, when we discussed digital transformation as moving from a coordinated infrastructure to a coordinated and connected infrastructure, with a mix of Systems of Record and Systems of Engagement.

I have not seen much progress with the customers I have been working with in the past five to ten years. The change did not seriously happen due to the need for new ways of working, different people skills and organizational change.

Will it happen now with the AI-wave? A question Ilan Madjar also asked this weekend.

 

What should we – companies and individuals – actually do?

As an approach, the research from Wharton suggests that using rewards (incentives) and providing feedback can help people stay mentally engaged and avoid disengaging or losing motivation.

In other words, these strategies can combat the tendency to mentally “check out” or stop trying.

When participants were rewarded for accuracy and received immediate feedback, their override rates on faulty AI roughly doubled.

This observation means we should design AI-assisted workflows so that people remain accountable for outcomes and receive clear feedback when AI-assisted decisions go wrong. Work to do for startups and existing PLM vendors to develop the best combination of agents and content.

BUT: do not let the AI absorb the accountability while the human takes the credit – System 3!

 

Companies are in an uncomfortable situation. Before AI became the focus for improving businesses, the most heard statement was:

“Our employees are our assets – they create the value of our company,”

And this is one of the reasons that HR departments exist. Although not all HR departments are there for the employees – their role is to balance the HUMAN RESOURCES in a company – we are still talking about resources.

With AI, the new statement might be

“Our AI-supported employees are our assets, where part of the asset value comes from the AI tools used”.

This raises the question of who will remain as the AI-supported employee. We already see that entry-level jobs in any type of business get replaced by AI, creating stress on the job market. This, combined with the observed reduced mastery of deep-skills in math, reading and science, as described in the PISA research earlier shared in this post, puts a generation at risk.

Like the winners of the Industrial Revolution, the winners of the AI revolution do not care about humans – they care about profits.

 

As individuals, we need to keep on training our brain-muscles without AI where the muscle matters. As the Dutch podcast mentioned: write your first draft before asking Claude to improve it, think through a problem before asking ChatGPT to solve it, and read a book of 100 pages.

In PLM, judgment and contextual reasoning are the core of what people need to do. You should protect the practice of doing that work yourself. Use AI to accelerate and refine, not to replace the effort that builds competence.

Invest in yourself to remain independent. Read broadly — actually read, not skim AI summaries. The people who will remain valuable in an AI-saturated world are not the ones who prompt best, but the ones who can evaluate, challenge, and contextualise what AI produces.

And with that, I want to come back to the post from Helena Guitierrez that I mentioned before, where she focused on what we can do as individuals.

Helena is one of the founders of Share PLM, a company with the purpose: BUILDING A HUMAN-CENTERED DIGITAL FUTURE, as is written on their website. We both believe that in order to enjoy the AI revolution, we have to invest in ourselves. The above image illustrates steps to take- click on the image for the full post.

Talking about the human-centered digital future, have a look at the agenda of the upcoming Share PLM Summit on 19-20 May in Jerez de la Frontera (Spain) – our sessions and discussions will, of course, be based on PLM and AI experiences, with the focus on what it means for humans – it will not be a technology conference.

 

Conclusion

The Wharton researchers close with a question worth “What happens when our judgments are shaped by minds not our own?” Their answer so far: we become more confident, less accurate when the AI is wrong, and largely unaware of the difference – a big risk.

The challenge is not artificial intelligence. The challenge is whether we will remain genuinely intelligent in the presence of it – take care of yourself!

What do you think?  I am looking forward to your comments and feedback.

 

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  1. Ben's avatar
  2. Viska Clanella Nugroho's avatar
  3. Oleg Shilovitsky's avatar

    Hi Jos, Knowing your background in methodology and education, I wanted to share a longer article with you: “What is…

  4. Bart Willemsen's avatar

    Interesting reflection, Jos. In my experience, the situation you describe is very recognizable. At the company where I work, sustainability…