For Siemens, the challenge lies in more than simply acquiring AI—it’s about operationalizing it.
Siemens’ acquisition of Altair Engineering, a leader in Artificial Intelligence (AI), simulation, and high-performance computing (HPC), reflects a bold ambition to strengthen its AI-driven industrial software portfolio. As Tony Hemmelgarn, President and CEO at Siemens Digital Industries Software, said: “this will augment our existing capabilities with industry-leading mechanical and electromagnetic capabilities and round out a full-suite, physics-based, simulation portfolio as part of Siemens Xcelerator.”
With a foundation already set in AI and generative AI capabilities, Siemens is taking a strategic leap to deepen its offerings in areas such as Product Lifecycle Management (PLM) and Digital Twins.
Yet, the acquisition raises critical questions: Is Siemens advancing its strategic edge by embedding next-level AI and knowledge graph technologies, or is it scrambling to keep up in a landscape that is moving faster than ever?
Elevating AI-driven PLM and digital twins
Siemens’ integration of Altair’s powerful AI, simulation and high-performance computing tools into its PLM tech suite, particularly within Teamcenter and Simcenter, offers a potential transformation in how digital twins and simulations are used across engineering and manufacturing. Altair’s deep expertise in physics-based simulations, including mechanical and electromagnetic modeling, could allow Siemens to develop more sophisticated digital twins that not only represent physical products but also predict behaviors and outcomes with high fidelity.
With Altair’s technology, Siemens can push digital twin capabilities beyond basic visualization and monitoring, creating a system that incorporates real-time data, predictive analytics and adaptive simulations. This would enable manufacturers to make informed, AI-driven decisions at every stage of the product lifecycle, from design and development to production and maintenance.
However, despite Siemens’ existing portfolio, which includes substantial AI and generative AI tools, the acquisition raises a critical question—how effectively can Siemens embed these capabilities as a core, transformative feature within its PLM platform? Without a clear path to seamlessly integrate AI across its offerings, Altair’s capabilities risk being relegated to auxiliary add-on features, potentially limiting their business impact. For Siemens, this move is more than just adding tools; it’s about embedding intelligence deeply within the end-to-end PLM framework, making AI a central component of its digital transformation strategy.
Enhancing digital twins with HPC
Siemens is marketing itself as a leader in digital twin technology, primarily through its Xcelerator platform, which integrates real-time operational data to improve asset management, production efficiency and product quality. Altair’s HPC capabilities could significantly enhance Siemens’ digital twin offerings by allowing more complex, detailed, and faster simulations—an essential component of predictive maintenance and optimization for manufacturers.
The integration of HPC into Siemens’ digital twin ecosystem could be transformative, enabling simulation models that accommodate an unprecedented scale of data and complexity. For instance, manufacturers could simulate entire production lines or supply chain networks, gaining insights that help them optimize operations, reduce energy consumption, minimize downtime and predict implications from product changes. This is particularly relevant as industries move toward more sustainable and resilient operations.
However, leveraging Altair’s HPC across Siemens’ existing infrastructure poses some challenges. HPC solutions typically require specialized infrastructure, substantial processing power and technical expertise. Siemens will need to carefully consider how to bring HPC capabilities into mainstream use within its portfolio, including positioning within its maturing SaaS offering. The risk here is that without a robust integration plan Altair’s HPC tools may remain isolated and less affordable, providing limited impact and reducing the transformative potential of this acquisition.
Knowledge graph technology: connecting data with digital thread
Altair’s recent acquisition of Cambridge Semantics, a developer of knowledge graph and data fabric technologies, brings new dimensions to the integration of enterprise data across complex manufacturing ecosystems.
Knowledge graphs provide a framework for Siemens to unify and contextualize vast amounts of data from disparate systems—an essential step for effective AI-driven insights and accurate digital twin models. With knowledge graphs, Siemens could break down data silos, connecting information from PLM, digital twins, and other systems into a cohesive whole, creating a seamless digital thread across the lifecycle.
Incorporating Cambridge Semantics’ knowledge graph technology into Siemens’ portfolio could lead to a new era of “data-rich” digital twins, where structured and unstructured data come together to provide a more comprehensive, actionable view of products, assets and operations. By grounding generative AI models in real-world data, knowledge graphs could improve response quality and deliver contextual insights, allowing engineers and operators to make better, faster decisions.
Yet, the question remains: can Siemens adapt this advanced data integration technology effectively in an industrial setting? Cambridge Semantics’ data fabric has been proven in sectors like defense, life sciences, and government. Adapting it for manufacturing will require Siemens to navigate industry-specific complexities. Without careful implementation, the risk is that knowledge graph technology will be underutilized—merely another tool rather than a strategic game-changer in Siemens’ PLM and digital twin offerings.
Strategic opportunity or catch-up?
The acquisition of Altair could empower Siemens to lead in AI-driven PLM, high-fidelity simulations and data-enriched digital twins. But the road ahead demands more than technological additions; it requires Siemens to deeply integrate these capabilities within its core platforms and ensure they serve as transformative, essential components rather than optional add-ons.
For Siemens, the challenge lies in more than simply acquiring AI—it’s about operationalizing it. By embedding Altair’s and Cambridge Semantics’ technologies as central pillars in its software ecosystem, Siemens has the opportunity to redefine industrial intelligence in manufacturing. Can Siemens realize this vision to become a true leader in AI-driven industrial software, or will it struggle to fully leverage these assets, ending up as a late entrant in a rapidly advancing field?
