AI for Engineers
The AI landscape within PLM is rapidly evolving, shifting from CAD optimization, and manufacturing planning to enhancing user interfaces and democratizing software functionalities. This shift aims to simplify complex tasks into plain language, significantly reducing the learning curve for engineers. Innovations by Siemens and Microsoft, Dassault Systèmes, and PTC exemplify this trend, focusing on human-machine collaboration, data-driven decision-making, and predictive modeling to boost productivity and innovation across various industries.
Artificial intelligence is coming big time in the PLM world. A couple of years ago the focus was on CAD topology optimization, optimization of planning within manufacturing and advanced rendering.
Now last couple of months simultaneously with development, the focus has shifted towards User Interface and democratization of the advanced functionalities in PLM software by helping the interaction with applications by explaining the complex scenarios and result expectations in plain language. This development will certainly be used to lead to enhancement of productivity by giving the tools to engineers to effectively evaluate their hypothesis with close to zero coming to speed time, today spent on attending the demanding courses on how to use the software.
Below you will find a couple of examples of this trend among the leading PLM vendors and my thoughts on it:
The partnership between Siemens and Microsoft stands for a significant move in integrating AI within industrial sectors. It focuses on enhancing human-machine collaboration, improving productivity, and helping innovation across various industries, including manufacturing, infrastructure, transportation, and healthcare. Their collaboration has led to the development of the Siemens Industrial Copilot, which optimizes automation code generation, and the Teamcenter app for Microsoft Teams, which aims to ease virtual collaboration among teams across business functions.
Another of the leading vendors within PLM, Dassault Systèmes has also heavily invested in AI, focusing on using data science and artificial intelligence to enhance business processes and decision-making. Their Information Intelligence solutions on the 3DEXPERIENCE platform offer tools for augmented virtual twin experiences with data science, aiming to transform how organizations design, develop, and manage products and systems. Dassault Systèmes has developed a data science solution that helped Renault Group optimize vehicle costs by simulating the impacts of raw material price increases and part cost variations. This shows their capability to integrate AI into strategic business functions to drive digital transformation.
Furthermore, Dassault Systèmes is exploring generative AI's potential to augment human creativity and improve digital twin simulations and experiences. By automating tasks like data analysis, customer service, and content creation, generative AI is positioned as a tool to enhance efficiency and unlock new creative potential.
PTC has also been active in AI initiatives, focusing on integrating AI into its Product Lifecycle Management (PLM) and Internet of Things (IoT) solutions. PTC's ThingWorx platform incorporates AI to provide advanced analytics, enabling businesses to gain actionable insights from IoT data. Moreover, PTC's acquisition of Onshape, a cloud-based CAD system, integrates AI into design and manufacturing processes, although specific AI initiatives by PTC were not detailed yet.
There is more to come. The AI initiatives by those just highlight a broader industry trend towards AI features for enhancing engineers’ toolboxes. Many other, more specialized players are also developing human-machine collaboration for industrial purposes and driving innovation across various industry sectors. Each of these companies is approaching AI with unique strategies that will certainly complement the established ways of working and eventually become more visible in the PLM market.
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How will this affect the workforce?
The inevitable question is how this rapid evolution of AI impacts engineer’s daily life from the human perspective. There is a certain built-in resistance to quick changes in the engineering world, as the existing organizational structure and ways of working are acquired during an extended period of trial and error. I am guessing, as always, there will certainly be both positive and negative surprises.
On the positive side as I mentioned earlier, simplifying complex tasks and user interfaces will allow engineers to quickly master tools, shifting focus from learning how to use software tools to innovating. Automation of routine tasks and optimization of processes will probably free up time for creative and critical thinking.
Automation may affect job security for some lower-end roles dependent on tasks that AI can perform.
On the other hand, there might be some negative consequences. Automation may affect job security for some lower-end roles dependent on tasks that AI can perform. Another worrisome thought is that reliance on AI for predictive modeling and data-driven decisions might reduce the emphasis on intuitive and creative aspects of engineering design.
As I spoke earlier today with one of my colleagues, the biggest successes through time in the engineering and science world come from the luxury of having the possibility to make mistakes.
The greatest scientific discovery was the discovery of ignorance.
To quote a recent post from Yuval Noah Harari on X: “The greatest scientific discovery was the discovery of ignorance. Once humans realized how little they knew about the world, they suddenly had a very good reason to seek new knowledge, which opened up the scientific road to progress..”
The extensive use of AI might result in many bad ideas never seeing the light of day and, thereby, not being refined into something good.
In today’s geo-politically uncertain world use of AI in decision-making introduces ethical considerations and potential biases, which could affect fairness and outcomes in product development that would serve the whole of humanity.
The future will tell us the answer, but the dilemma remains: while AI's integration into PLM announces a new era of efficiency and innovation for engineers, it also brings challenges that need a careful balance between leveraging technology and maintaining our value of still being better than the machines in being humans. 😊
Dennis Akkermans Interesting read
Industry Leader for Transportation & Mobility and Life Science
8moSean Sullivan can you sshare some more examples on AI powered PLM and design? What's your view, what are the strengths and potential negative consequences?
Industry Leader for Transportation & Mobility and Life Science
8moVery interesting article on AI application on PLM and for design. I'm sure there might be some negative consequences but not sure if automation should be considered as one of them. I think it's great to have AI and technology ease the work privously done by humans and I don't think that makes us redundant. On the contrary, it opens up other tasks that (for now at least) require a human. And as for bias and exclusive design, don't you think that we (humans) do this? So my question: can we discuss over lunch the AI application on design and what consequences come with that? 🤔😅