AI and data are rewriting the rules of manufacturing. Those who build digital consistency today will lead tomorrow’s industry.
Industry pioneer Henry Ford famously said over a century ago, “Any customer can have his car painted any color he wants, as long as it’s black.” Early Model Ts actually came in a variety of colors – grey, red, green, blue, and black. But when Ford introduced the moving assembly line, speed and efficiency took priority. Standardizing production meant fewer variants, and black became the color of progress.
Today, digital technologies enable new manufacturing models in which products no longer need to move along a traditional production line. Variety is making a comeback. Work can be carried out synchronously, on demand, and often in parallel at individual stations – enabling manufacturers to deliver the product mix the market demands with greater flexibility.
However, this flexibility comes at a price: advancing digitalization is driving a rapid increase in complexity. Beyond mechanical components, software now plays a decisive role. Consider the Tesla Model S, which has been in production since 2012. While the exterior has changed a little, the car has continuously evolved under the hood through software updates and hardware optimizations. Each iteration improves performance and functionality, even as production efficiency drives costs down.
Manufacturers are therefore faced with a dual challenge. They must perfect mechanical components – achieving tighter tolerances, more efficiency processes, and higher quality – while also orchestrating complex interactions between software AI functions and digital services. What were once separate worlds are now merging into a single, highly complex, connected system.
PLM is becoming an enabler of modern industry
Without a consistent and integrated data architecture, today’s product complexity is nearly impossible to master. This is where product lifecycle management (PLM) comes in. As a central data hub, PLM not only documents all development, production, and usage information for the physical product – it also records the digital history of software versions, training data, and AI models: seamless, traceable and versioned.
PLM not only acts as a data repository, but also structures information intelligently, linking it contextually, and making it available to people and machines. This transforms the flood of data into a strategic resource that can realize its full potential. PLM is becoming a key enabler of a data-driven, AI-powered industry –Â one defined by product diversity, individualization, and smart functionality that can still be governed with precision. Every design decision, every change, and every new software version is documented and therefore remains auditable, reproducible and scalable.
The most important element and the “backbone” of digital product development is the “digital thread” made possible by PLM. It connects all relevant information – from initial design through production and use to recycling. As a reliable, dynamic knowledge network, the digital thread ensures transparency and forms the foundation for broad adoption of next-generation AI technologies.
Clean data for the use of AI
AI can only deliver real value across the entire value chain – from design to production to operations – when the underlying data is consolidated and reliable. The Aras study, “The Future of Product Development” shows how strongly companies are embracing AI: 80% of companies are already using AI in product development, and 91% are also planning to significantly increase their investments in this area. The study surveyed 656 industry executives across the U.S., Europe, and Japan.
According to the study, the biggest drivers behind this change are clear: high cost pressure (35%), increasing product complexity (34%) and regulatory pressure (33%) are pushing organizations toward new digital approaches. At the same time, many companies still face gaps – 69% are concerned about data protection and security, 65% see skills gaps in dealing with AI and 64% are struggling with data silos.
To fully harness the potential of AI while mitigating risks, companies need comprehensive PLM deployment. This is not just an IT project, but a cultural and organizational transformation. The goal is to break down data silos, build up expertise, and establish flexible system architectures. This is where PLM shows its strategic relevance: as a central platform enabling continuous data flow, it provides the necessary framework for successful, scalable AI integration – and ultimately a lasting competitive advantage.
The future of smart production
Industrial value creation is evolving into a fully networked, data-driven ecosystem. AI-supported systems control material flows, inventories, and logistics routes in real time – detecting bottlenecks early and automatically calculating alternatives. In engineering, generative AI designs variants and concepts autonomously, transforming engineers into curators of creative AI processes. Even the environment benefits: energy consumption, COâ‚‚ footprints, and recycling potential are automatically recorded, documented, and prepared for reporting.
These systems can only deliver on their promise when the data collection, processing, and analysis are seamlessly integrated. Those who rely on end-to-end digitalization today are laying the groundwork for a resilient, innovative, and sustainable future. PLM is the key to this and enables the leap into the next evolutionary stage of manufacturing. Those who act now will secure their lead. Those who hesitate will lose out.
Jens Rollenmüller, VP of Professional Services, EMEA at Aras
Jens Rollenmüller is a recognized leader in digital transformation and data management with more than two decades of experience in product lifecycle management and IT services. As Aras’ Regional Vice President of Professional Services, EMEA, he drives large-scale PLM initiatives and supports organizations in achieving efficiency, scalability, and innovation while reducing IT complexity.
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