This 2023 Siemens AI and PLM announcement ripples into 2024.
Though made back in October 2023, a Siemens Digital Industries Software release suggests the prevalence of AI in PLM and highlights its place on 2024 corporate radar.

The release read, “Siemens Digital Industries Software and CEA-List, a technological research institute focused on smart digital systems research, announced […] research to further extend and enhance digital twin capabilities with AI and explore greater integration of embedded software on both virtual and hybrid platforms.”
For this partnership, CEA-List brings expertise in digital technologies, including smart factories, sustainability, AI, machine learning, blockchain, cybersecurity and code verification. CEA-List is a French government organization which provides concrete solutions to four main areas: energy transition, digital transition, healthcare technologies, and defense and security. This expertise complements Siemens Digital Industries Software’s Xcelerator portfolio, which includes electronic design automation (EDA), multi-physics simulation, PLM and other engineering software solutions.
The aim of the partnership is to further explore the use of digital twin technologies for autonomous driving, smart robotics and healthcare domains. But the AI trend it represents can be attributed to the technology’s transformative capabilities in data analysis, decision-making and automation.
AI clearly contributes to strategic goals and competitive advantages. In contrast, while PLM is integral to product innovation, introduction and portfolio lifecycle management, it might be viewed as a specialized discipline primarily rooted within engineering and manufacturing circles.
This article will bring this all into focus by elaborating on the intricate tapestry of AI and PLM implied by the partnership and 2024 trends. It will dig into the understanding of AI and PLM’s roles in implementing intelligent enterprise twins and how both technologies can work in conjunction to create intelligent digital twins.
Understanding the Duality of PLM and AI
PLM serves as the backbone in crafting certain types of digital surrogates, providing a systematic approach to managing the entire product lifecycle. It spans ideation, design, testing, manufacturing, maintenance and decommissioning — ensuring a holistic view and seamless collaboration across departments. PLM platforms such as Siemens’ Teamcenter and others integrate data and processes, fostering collaboration and optimizing workflows.
In the context of the intelligent enterprise twin, PLM becomes the orchestrator of data and processes. It ensures that every facet of a product’s journey is meticulously recorded and managed. From the initial design phase to the product’s retirement, PLM platforms act as the custodian of information, enabling stakeholders to make informed decisions and streamline operations. The scope of PLM amalgamates series of cross-functional change impact assessments and associated decisions.
AI adds an intelligent layer to the PLM data backbone. It enhances PLM’s capabilities in predicting, analyzing and adapting to dynamic scenarios. In the context of the digital twin, AI empowers the system to learn and evolve based on real-time data inputs. For instance, predictive maintenance algorithms can anticipate equipment failures, optimizing maintenance schedules and minimizing downtime.
Combining AI and PLM to Elevate the Value of Digital Twins
AI-driven analytics further elevate the value of digital twins by providing actionable insights. Through machine learning algorithms, the system can identify patterns, anomalies and potential optimizations. This contributes to more informed decision-making processes. In the intelligent enterprise twin, AI acts as the cognitive engine, infusing the digital replica with the ability to think, learn and respond intelligently.
In the context of this discussion AI, PLM and digital twins can be seen as:
- AI: the cognitive capability within the PLM and digital twin framework. It adds a layer of intelligence, enabling the system to learn, adapt and optimize processes autonomously. AI is the ‘brain’ that interprets the vast amount of data within PLM and ERP systems, extracting meaningful insights and driving continuous improvement.
- PLM: the overarching framework that governs the entire product lifecycle. It encapsulates process, data and collaboration tools to ensure a cohesive and streamlined journey from product conception to retirement. It involves the integration of people, processes, business systems and information to optimize product development and lifecycle processes.
- Digital twins: the virtual replicas of physical objects or processes. It often relates to product information as it traverses through different phases of a lifecycle. There are multiple types of digital twins, but they all encompass data, processes and relationships by seamlessly connecting system stages.
While PLM encompasses various technologies, it primarily focuses on the structured management of product-related data and processes. In the context of digital twins, AI can be applied to enhance the intelligence and autonomy of these virtual replicas.
For example, AI algorithms can analyze data from digital twins to identify patterns, predict behavior and optimize performance. While digital twins and PLM are closely related, digital twins are often a component within PLM systems. AI, on the other hand, can be integrated into both digital twins and PLM systems to enhance analytical capabilities.
Building Intelligent Digital Twins in Industry
While digital twins are critical components of PLM, AI enriches it by injecting predictive and adaptive capabilities. Virtual replicas are dynamic by design. They are continuously updated with real-time data or iterative change to ensure they properly monitor, assess and simulate their physical counterparts. Digital twins facilitate a deeper understanding of the actual performance and behavior of physical entities, enabling better decision-making and predictive capabilities. Simply put, the smarter the model, the more accurate the measured inputs and more real-time the data input and output, the more effective the prediction and actionable decision.
As digital twins mature, they provide a real-time, dynamic representation of physical entities. Meanwhile, PLM manages the lifecycle of products and AI contributes intelligence to optimize various processes.
These concepts can complement each other in creating smart, efficient and predictive systems in various industries. AI-based digital twins can bring a new era of virtual simulation with examples like:
- AI-built purpose-built computer algorithms to yield better operational efficiency, from data input to interpretation and simulation.
- Smarter decisions based on AI-based root-cause analysis and scenario planning for better, faster problem solving.
- AI-developed mechanical, electronics, hardware and software integration, with enhanced compatibility, quality assurance and end-of-life predictability.
- AI-driven useful life optimization to estimate replacement or overhaul requirements of an asset used in operations.
- AI-compatible scenario planning, impact assessment and product/PLM data change management through part, component and BOM maturity control.
Or as Jean-Marie Brunet, vice president and general manager of the Hardware Assisted Verification Division at Siemens Digital Industries Software, put it in that October release, “With the strong increase in electronics and software content of products and systems, there is a clear need for multi-domain, multi-fidelity system simulation solutions to relieve multiple design and verification challenges. We share a vision with CEA of an even more comprehensive Digital Twin and believe we can implement this vision through the power of the Siemens Xcelerator portfolio because it covers everything from Electronic Design Automation software and hardware tools to system, sensors and multi-physics simulation software.”