After a summer holiday in the south of Greece, it is time to resume my activities. The south of Crete is largely an analogue environment, far from any digital hype.

Tempted by LinkedIn posts, I noticed the summer was full of memories, with Martin Eigner sharing 40 years of PLM experience, Oleg Shilovitsky sharing 30 years of PDM Evolution, and Michael Finochario publishing posts on PLM vendors, CAD kernels, and more.

So where do I stand? While digesting all these historical experiences, I reflected on what we can learn from them and what we didn’t learn from them.

 

It started with technology.

From 1990 to 1999, I worked with mid-market companies, where data management was the most significant challenge. The introduction of MS Windows made data management more user-friendly, evolving from drawing management systems with version and status management capabilities.

Who remembers Automanager Workflow from Cyco, before SmarTeam came on the market?

For that reason, in the early days, PDM was an IT job. As the PDM system primarily dealt with engineering data, it was relatively easy to implement as an organizational change process. We transitioned from analogue to electronic in the department.

Connecting with other systems, particularly ERP, was a serious IT job and a financial challenge. Connecting with other systems, particularly ERP, was a serious IT job and a financial challenge. The rapid decline of IT components, combined with the rapid growth of global connectivity, has created new opportunities for collaboration.

As part of the Dassault/IBM/SmarTeam organization, I explained and taught these new capabilities worldwide.

In 2008, my VirtualDutchman blog and coaching journey began, evolving from explanations of technology to modern methodologies, which led to organizational change and expectation management – skills not traditionally associated with IT.

 

Then came digital transformation

With growing connectivity, smartphones and Web 2.0 technology have led to more PLM-like discussions. PLM vendors expanded their scope and developed capabilities beyond mechanical engineering.

The expansion of capabilities was also the moment when the confusion about the term PLM reached its peak: a PLM strategy or a PLM system?

At the time, they were largely considered the same in discussions and advertisements..

Meanwhile, digital transformation was occurring at the marketing and sales levels – companies invested in direct communication with their customers through the web.

Meanwhile, the internal ways of working for R&D, engineering, and manufacturing did not change significantly. Still, they were following linear processes, and despite the existence of 3D CAD, the 2D drawing remained the primary carrier of legal information between engineering, manufacturing, and suppliers.

Note: the option where the most benefits could be achieved – connected supply chains – had the lowest focus in 2017 – something that would change with COVID-19.

Fundamental digital transformation in the PLM domain occurred gradually. ARAS came with its overlay approach (the platform), connecting various disciplines and enterprise systems. In contrast, Dassault Systèmes introduced its 3DEXPERIENCE platform, utilizing its own software brands as platform components.

The Aras overlay approach

Most PLM vendors rapidly countered Aras’ overlay approach with their low-code offerings based on Mendix, ThingWorx or Netvibes, to enable data flows beyond the traditional PDM scope. The Coordinated Digital Thread was born.

The good news is that PLM has now clearly become a strategy based on a federated system infrastructure. The single PLM system no longer exists, although many of us still use the term’ PLM system’ to refer to the main component of a PLM infrastructure – the System of Record.

Moving to a federated PLM infrastructure is already a challenge for companies, not because of the available technology, but first of all because of the legacy data and, closely related to that, legacy processes and people skills.

Legacy is creating the inertia, not technology!

 

Next came the cloud – SaaS

With the availability of cloud solutions that support real-time interactions between stakeholders, either within an enterprise or in a value chain, a new paradigm has emerged: the connected enterprise.

A connected enterprise no longer needs interfaces to transfer data from one system to another.

Instead, with apps and dashboards, combined data from different online sources is presented in a single, user-friendly working environment – A combination of the Systems of Record with the new environments – the Systems of Engagement.

The technology used to create dashboards and apps is based on modern data-driven technologies and principles (ontologies, graph databases, and the semantic web). The Connected Digital Thread was born.

However, legacy systems play an essential role again, as some systems of engagement can be implemented in a complementary manner to the systems of record, allowing companies to work within an integrated technology model.

People will work in a particular mode, either coordinated or connected, but organizations can operate in both modes simultaneously. A story I have been sharing a lot – it is not about migrations but about an evolutionary approach towards an integrated technology model.

At this point, it becomes essential that business objectives drive the implementation of a PLM infrastructure. Of course, you hear me say we should start from the business; however, the big difference now is that a company should coordinate the technologies, systems, and tools it acquires to avoid isolated islands of information.

Follow Yousef Hooshmand‘s 5 + 1 business transformation steps.

An open SaaS infrastructure enables a company to let data flow almost in real-time. There is a lot of discussion related to data quality and governance, and if you have missed it, please read these three articles I created together with Rob Feronne, the product Digital PLuMber:

There are some great insights in this dialogue and the associated LinkedIn comments.

Despite the increasing availability of technology, it is the legacy of people, processes, and culture that is hindering progress.

Rob Feronne had a shocking lightbulb moment 😲 in our discussion about the future of PLM, where the participants – see below –  answered a question related to the importance of technology in our PLM domain – shocking also for me.

My thumb was up because modern technology matters! The question inspired Oleg Shilovitsky to write a whole blog post on this topic. If you’re truly shocked, read his post, where I agree with the content; the question is too simple to answer with a thumbs up/down.

As technology has become more accessible than before, you no longer need an IT department to establish a PLM infrastructure. And then indeed, the people and process side needs and deserves much more attention..

 

And now there is AI

If you haven’t read anything about AI recently, you must be living in an isolated location. Regardless of the business discussions you are following, it is all about the potential of AI.

Although AI is not a new concept, the fact that various AI capabilities have now reached the end-user level is what drives the hype. Currently, I believe we are at the peak of the hype.

Last week, I participated in an interesting discussion in the series: The Future of PLM moderated by Michael Finochario, this time talking with the analysts. Click on the link to see Michael’s excellent summary and access to the recording of the event.

It was an interesting discussion for a little more than an hour, and the majority of our discussion was about the potential impact of AI on businesses. First, the impact AI can have on the traditional work of an analyst and next, the effects on the PLM domain.

I believe we agreed that AI at this moment is mainly providing higher user efficiency and performance, very much aligned with the interesting research I have been reading in the MIT NANDA report with the title The GenAI Divide: STATE OF AI IN BUSINESS 2025

The report’s interesting findings included high adoption of tools but low transformation. Despite significant investment in Generative AI (GenAI), most organizations are not achieving meaningful business transformation. ​

  • 95% of organizations report zero return on GenAI investments. ​
  • Only 5% of integrated AI pilots generate millions in value. ​
  • 80% of organizations have explored or piloted tools like ChatGPT, but these primarily enhance individual productivity.
  • 60% of organizations evaluated enterprise-grade systems, but only 20% reached the pilot stage, and just 5% reached production. ​
  • Key barriers include brittle workflows, a lack of contextual learning, and operational misalignment. ​

Therefore, the question is – Is current AI the next bubble?

In 2014, I wrote about the lack of digital transformation in the PLM domain, and two images (below) from a report by The Economist could be used again. The report can be found here: The Onrushing Wave.

Click on the image to read the 2013 predictions.

I realized that my current job, as a recreational therapist and firefighter at the time, was not at risk, and that some of the predictions from 10 years ago had become a reality. Who is still bothered by telemarketers or retail salespersons?

However, many of the AI symptoms mentioned in the MIT NANDA report are similar to the hype surrounding digital transformation.

The only reservation I have now – will it take a decade before we understand and demonstrate the value of AI, or are we accelerating?

In this context, the upcoming PLM Roadmap/PDT Europe conference on 5 – 6 November will be interesting, as here we will discuss reality.

For a few of you interested in more, there is the day before the conference, a (free) workshop where we will discuss with some thought leaders and experts from various companies how the future of PLM could look like – based on standards, AI tools and more. Click on the image below the conclusion.

 

Conclusion

The summertime was a nice moment to reflect, inspired by others in my network. What is clear is that there is a shift from technology towards people and change. The rapid expansion of AI tools, along with connected technologies, has created an overwhelming array of possibilities. Now it is time for business leadership to understand them and utilize them for significant business improvement, where the fear is that substantial change will always be slowed down by organizational inertia.