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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:
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.
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.
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