I recently read an article that used a simple observation to explain a larger economic change – Why AC is cheap, but AC repair is a luxury. You can buy an air conditioner for less money than it costs to hire someone to fix a small mechanical issue in your home. It is cheaper to buy a flatscreen TV to cover a hole in your drywall, than to invite a person who can patch the hole.
Manufacturing became extremely productive, so AC, TVs, etc units became inexpensive. Home repair did not follow the same productivity curve and became relatively more expensive. This relationship between productivity and cost is described by Jevons paradox and the Baumol effect.
This same relationship is now unfolding in the PLM industry. Especially in consulting and blogging. Intelligence became cheap. Judgment became expensive.
The New Reality in PLM Content and Consulting
You can see this shift developing in PLM discussions and consulting materials. The internet is flooded with cleanly generated summaries, recommendations, system comparisons and transformation plans. My LinkedIn is full of nice PLM diagrams, smart observations, and frameworks explaining how to digitally transform everything.
These documents appear polished and coherent. Much of it is created automatically by AI systems or by custom models that consultants train on their own content. GPT PLM advisors are offering decades of experience packed into the LLM model that will build a PLM strategy for your companies.
Bloggers who used to share their insights now question whether blogging is still useful. Some say that large language models can already explain every PLM concept. Consultants who once spent weeks producing analysis and frameworks can now generate them in minutes. This creates a new landscape. Insight creation is no longer a scarce activity.
The traditional value of PLM consultants and PLM educators was tied to their ability to synthesize information. They helped customers understand processes, data models and decisions. That activity depended on time, experience and manual work. It is now abundant and inexpensive. The foundation of the profession is shifting.
What Happens When Intelligence Becomes Cheap
The collapse of intelligence costs introduces a new problem. When AI can produce large volumes of intelligent output, the real bottleneck moves to something else.
My favorite quote from the article – Agatha Christie once wrote that she never thought she’d be wealthy enough to own a car, or poor enough to not have servants. Whereas, after a century of productivity gains, the average American middle-class household can comfortably manage a new car lease every two years, but needs to split the cost of a single nanny with their neighbors.
This bottleneck is judgment. It is the drywall repair in the original analogy. The product is cheap. The skilled labor is not.
In PLM, judgment sits in the ability to recognize constraints. It influences which insights matter. It understands the timing of decisions. It observes unspoken organizational dynamics. It predicts second order effects. It often lives in the experience of how systems behave when used by real engineering and manufacturing teams.
An AI tool can generate a long list of recommendations for metadata, data governance, part numbering, BOM management and API integrations. Only a small fraction of those recommendations are suitable for a specific company. Identifying the relevant ones is not something a model can do. It requires human understanding of people, processes, culture, technology maturity and political context.
As insight generation accelerates, the gap between ideas and execution becomes larger. The organization has not traveled the thinking journey. The alignment has not formed. The context has not been explored. The pace of insight creation does not match the pace of change adoption. This gap is where judgment is needed most.
The Decline of the Old PLM Consulting Model
The consulting model most people grew up with depended on slow intelligence pipelines. A team interviewed employees, reviewed documents, analyzed data and built presentations. The process created natural filters. It forced prioritization. It created shared alignment. It helped people understand how conclusions were formed.
AI removes this entire pipeline. The insight generation no longer has a cost. Without that cost, insights lose their built-in prioritization. The quantity of output increases, but the clarity does not. Organizations receive more options than they can meaningfully evaluate. The burden of judgment grows.
Consultants who continue to rely on producing documents will find themselves in a more difficult position. A document is no longer a proof of expertise. It is just one of many possible outputs. Where those consultancies should move? I predict they need to move to guidance, sequencing, hands-on decision support and navigation through the messy parts of implementation.
How PLM Blogging Is Evolving
The same transformation is affecting PLM blogging. The idea of PLM blogging explaining complex concepts is dead. ChatGPT can generate more explanations you can read in five minutes. But it doesn’t mean the idea that blogging is no longer needed is correct. I think it is a misunderstanding of what made PLM content valuable. AI can describe PLM concepts. It can generate lists of best practices. It can analyze vendor messaging. It can write definitions.
This does not replace insight and interpretation. I call it “comments” in my original tagline of Beyond PLM. We need less information (remember, it was a time when information was not available too). But for the last decade, the most important PLM blogging has always been about perspective. A blogger offered judgment on the meaning of industry events. They connected ideas, highlighted contradictions and explained practical consequences. They shared experiences from customer projects and exposed technical or organizational patterns that matter. This is what was needed and not a list of presentations and slides from the event.
A summary is not the same as interpretation. A generated explanation is not the same as experience. Without active voices in PLM, the space fills with content that explains the surface but never reaches the depth. Human interpretation is the differentiator. It describes why things happen, how decisions play out and what implications follow. AI is not a substitute for that.
The next phase of PLM blogging will likely be more transparent about reasoning and tradeoffs. It will focus on interpretation over explanation. It will emphasize lived experience and observed outcomes. The industry needs this type of writing. It reduces noise and increases clarity.
Why Practical Common Sense and Judgment Is the New Scarcity in PLM
Judgment in PLM operates at several levels. It is the ability to recognize the real bottleneck in a company. It is the ability to decide which part of the system needs attention first. It is the sense of when an organization is ready for a broader PLM adoption step. It is the awareness of how decisions in one domain will affect another.
It is also the ability to filter. When AI generates dozens of options, someone must decide which ones are sensible. Someone must understand which ones are politically viable. Someone must sense which ones fit current priorities and which ones should be postponed. Someone must recognize which recommendations will create friction in engineering or cause unexpected behavior in downstream systems.
These decisions come from years of observing how PLM technologies behave in real environments. They also come from understanding people. PLM systems are socio-technical systems. Their success depends on behavior, incentives and maturity. AI does not see these aspects. Only people do.
Judgment becomes more valuable as the volume of intelligence increases. It is the scarce resource that directs the results toward outcomes.
The New Work of PLM Consultants and Advisors
PLM consultants who want to stay relevant will transition from intelligence generators to intelligence navigators. Their strength will lie in the ability to interpret insights, guide decisions and support implementation. The most successful consultants will spend less time writing static documents and more time working alongside organizations to achieve outcomes.
Implementation will become the primary value. This includes planning realistic steps, helping teams adopt new processes, guiding integration decisions, resolving conflicts, adjusting the roadmap and ensuring that the system works in real operation. These activities rely on human presence and practical experience.
Internal PLM architects and experts face a similar transformation. They can generate content with AI tools, but their influence will come from building systems that last. They will create clear governance structures, reusable playbooks, modular templates and durable workflows. They will help their organizations adopt PLM in a sustainable way.
The people who rely only on generating insights will face increasing competition from machines. The people who demonstrate judgment will become more valuable.
Why Insight Without Alignment Does Not Work
AI accelerates the production of insights, but alignment inside organizations does not automatically accelerate with it. When a team sees a set of recommendations created in a day, they may not internalize them. They did not participate in the analysis. They did not debate the options. They did not experience the tension of the decision-making process.
A fast insight pipeline does not shorten the adoption process. It sometimes slows it down because the organization has to process the conclusions without the context that traditionally came from a long consulting engagement. This is where human guidance matters. Judgment helps assess how much communication is required. It identifies who needs to be involved. It determines the level of change management that will support the adoption.
The biggest risk is an expanding gap between knowing what should be done and actually doing it. AI widens this gap because it makes it much easier to generate correct-sounding ideas without building organizational readiness. Judgment is required to close this gap.
The Role of PLM Voices in the New Environment
The PLM industry benefits from voices that interpret events and provide grounded perspectives. These voices help others make sense of a rapidly changing environment. PLM bloggers and advisors should continue sharing their views. Their content will stand out because it contains judgment, not just information.
People in the PLM community want clarity about trends, vendor strategies, new technologies, architecture approaches and practical lessons from real deployments. AI can describe mechanics, but it cannot evaluate meaning. Human interpretation remains essential.
Sharing judgment publicly also builds a collective understanding in the industry. It provides reference points, challenges assumptions and strengthens the dialogue around PLM. This is valuable at a time when the volume of generated content grows faster than the industry’s ability to interpret it.
What is my conclusion?
The PLM industry is moving into a new phase in PLM blogging, consulting, and implementations. In this phase, the most important contributions come from human judgment. Intelligence is abundant. AI accelerates the production of content and recommendations. The work that remains is selecting what matters and guiding organizations so they can achieve meaningful outcomes.
This shift does not diminish the role of consultants or bloggers. It clarifies it. Their work becomes more focused on interpretation, prioritization, communication and execution. These activities define the new shape of PLM expertise.
The future of PLM is shaped not by how much intelligence someone can generate, but by how effectively they can use intelligence to help organizations progress. The work becomes more grounded and more human. It becomes centered around decisions and outcomes rather than documents.
Intelligence is cheap. Common sense and judgment is not. That is where the PLM industry is heading and where the most valuable contributions will continue to emerge.
Just my thoughts…
Best, Oleg
Disclaimer: I’m the co-founder and CEO of OpenBOM, a digital-thread platform providing cloud-native collaborative and integration services between engineering tools including PDM, PLM, and ERP capabilities. Interested in OpenBOM AI Beta? Check with me about what is the future of Agentic Engineering Workflows.
With extensive experience in federated CAD-PDM and PLM architecture, I advocate for agile, open product models and cloud technologies in manufacturing. My opinion can be unintentionally biased.
