The Beyond PLM article revisits a period before CAD platforms, PLM software, and digital workflows transformed engineering into a data-centric discipline. Rather than relying on interconnected databases and automated systems, engineering organizations once operated through what the author describes as a “human memory system,” where knowledge, coordination, and decision-making depended heavily on personal experience, physical proximity, and informal communication.
The article argues that older engineering environments functioned through a combination of paper drawings, face-to-face collaboration, and institutional memory. Engineers, machinists, manufacturing teams, and suppliers often shared understanding through conversations, handwritten notes, and years of accumulated practical experience. Drawings alone rarely captured the full reality of a product. Much of the critical context existed inside people’s minds, including why certain design decisions were made, what compromises were accepted, and which manufacturing constraints shaped the final product.
According to the article, early engineering systems succeeded not because documentation was complete, but because humans continuously filled the gaps. Experienced workers connected fragmented information across departments and interpreted ambiguous details using tacit knowledge developed over time. In many ways, organizations themselves acted as living memory networks where expertise circulated socially rather than digitally.
The article contrasts that environment with modern engineering software systems, which excel at storing files, revisions, bills of materials, and transactional records but often fail to preserve the reasoning behind decisions. Contemporary PLM and PDM systems manage structured data effectively, yet much of the product’s deeper history remains scattered across emails, meetings, chats, and undocumented human interactions.
A central theme of the article is that artificial intelligence will not eliminate the need for human context. Instead, future engineering platforms may need to evolve into “product memory systems” capable of capturing both structured technical data and the broader narrative surrounding design decisions. The article suggests that AI agents can process information, but they still depend on human judgment, interpretation, and contextual understanding to make engineering knowledge meaningful.
Ultimately, the article frames engineering not merely as a technical process but as a deeply human activity shaped by memory, collaboration, storytelling, and accumulated experience. Even as AI and automation reshape product development, the author argues that preserving human context may become more important than ever.