As the longstanding backbone of manufacturing innovation, product lifecycle management (PLM) makes up the cohesive processes and technologies that manage a product’s entire journey from conception to disposal.
However, as products become increasingly complex and customer demands for customization grow, traditional PLM systems reveal significant limitations. Legacy PLM platforms were never designed to handle today’s level of complexity, often creating data silos and bottlenecks in engineering that hamper innovation and time-to-market.
Enter artificial intelligence. As AI adoption accelerates across enterprise systems, with over 80 percent of global companies now implementing AI to some degree, PLM has emerged as a prime candidate for transformation. The convergence of AI and PLM addresses fundamental challenges in product development, from managing increasing complexity to enabling cross-functional collaboration.
Improved Decision Making
“While AI has been used in manufacturing for decades, the advent of generative AI (GenAI) has catapulted the industry forward in improving human productivity and operational efficiency,” states Josh Epstein, Chief Marketing Officer at Aras, an American developer of innovative PLM software.
Recent research shows PLM systems enhanced with AI can dramatically improve decision-making accuracy, reduce development cycles, and create more sustainable product lifecycles— capabilities that are becoming essential rather than optional in today’s competitive landscape.
What AI Brings to the PLM Table
Artificial intelligence is addressing critical PLM challenges through five transformative capabilities, backed by recent industry data and implementations:
Predictive Analytics: AI analyzes historical product data and IoT streams to forecast supply chain risks and component failures. McKinsey estimates AI could unlock $4.4 trillion in productivity growth across enterprise systems, with PLM optimization being a key driver.
Natural Language Processing (NLP): NLP enables conversational queries like “Show torque specs for EV drivetrains” in PLM databases. Engineering.com highlights how NLP eliminates keyword dependency, letting teams extract specifications from unstructured documents using plain language.
Machine Learning for Design Optimization: Generative AI balances aerodynamics, materials, and manufacturing constraints to propose optimized designs. This integration has been shown to reduce prototyping cycles by 40 percent in the automotive and aerospace sectors through multi-disciplinary coordination.
AI-Driven BOM Management: Automated bill-of-materials adaptation substitutes obsolete parts using real-time supplier data. Studies have shown that this automation accelerates BOM cost calculations, effectively reducing procurement delays and improving efficiency.
Decision Intelligence Systems: AI flags design conflicts by synthesizing CAD, ERP, and quality data. Consumer goods manufacturers have leveraged decision intelligence for dynamic stock optimization, with one case example seeing over 70 percent of actions being executed autonomously.
AI is fundamentally reshaping PLM by introducing capabilities that turn static data repositories into dynamic decision engines. These advancements explain why 82 percent of manufacturers now prioritize AI-ready PLM systems in their 2025 IT budgets, according to Rootstock.
What to Look For When Selecting a PLM Partner
Traditional PLM platforms, designed for static workflows, often struggle with today’s demand for agility and cross-functional collaboration. Legacy systems frequently lock organizations into proprietary ecosystems, which in turn, complicates AI integration and adaptability. When evaluating modern PLM partners, prioritize platforms that balance AI innovation with architectural flexibility. Here are a few key considerations:
Open Architecture: Avoid closed systems that restrict third-party integrations. Prioritize platforms with modular architectures that unify data from CAD, ERP, and IoT systems without proprietary constraints. This ensures seamless AI integration while preventing vendor lock-in.
Interoperable AI Toolkits: Seek partners supporting hybrid AI workflows, such as natural language processing for regulatory checks or predictive analytics for supply chain optimization. “Prioritizing interoperability — the ability of systems, devices, and applications to work together within and across organizational boundaries — is vital for achieving seamless data exchange, collaboration, and innovation across the product lifecycle,” Epstein adds.
Vendor-Neutral Data Infrastructure: Ensure data lakes and knowledge graphs are decoupled from specific AI vendors. Open frameworks allow continuous model training on historical product data while maintaining intellectual property control.
Strategic Alignment: Leading manufacturers prioritize modular AI capabilities over monolithic solutions. This approach ensures PLM systems can adapt to emerging technologies like generative design or digital twins without costly overhauls. Industry reports highlight growing adoption of platforms emphasizing interoperability, data sovereignty, and compliance-ready AI tools.
Benefits of AI in PLM
AI is redefining how organizations innovate, collaborate, and compete. By injecting intelligence into every lifecycle phase, AI delivers measurable advantages that cascade across departments:
- Automated change management slashes approval cycles by instantly analyzing how a single design tweak impacts cost, compliance, and production timelines.
- Smart recommendations surface data-driven product updates, like substituting materials to meet sustainability goals without compromising performance.
- Real-time compliance monitoring flags regulatory risks during design phases, preventing costly recalls or certification delays.
- Predictive maintenance insights extend product lifespans by identifying failure patterns in field data and triggering proactive upgrades.
- Streamlined cross-functional collaboration syncs engineering, procurement, and manufacturing teams on a single truth source, eliminating version control chaos.
- Future-proofed product strategies use lifecycle intelligence to predict market shifts, ensuring portfolios evolve ahead of consumer demands.
- Self-optimizing workflows learn from historical project data to allocate resources, prioritize tasks, and flag bottlenecks before they stall progress.
With agentic AI, background agents “operate autonomously to make new inferences and suggest optimizations without requiring explicit instructions from humans,” says Epstein. AI transforms PLM from a reactive process into a strategic growth engine, from concept to retirement.
Market Trends and What’s Ahead
The industrial AI landscape is poised for explosive growth, with the global market projected to expand at a 28.5 percent CAGR through 2035, reaching $380 billion, driven by demand for operational efficiency and sustainability. In PLM specifically, 91 percent of manufacturers plan to increase AI investments over the next two years, prioritizing tools that tackle production costs (cited by 35 percent of firms) and product complexity (34 percent).
Near-term adoption will focus on generative design optimization and AI-augmented compliance, as legacy systems struggle with escalating regulatory requirements. By 2030, expect widespread integration of predictive analytics for supply chain resilience and NLP-driven knowledge management to address workforce transitions.
Longer-term, PLM platforms will evolve into autonomous decision engines. Aras’ 2025 survey reveals 59 percent of companies view AI as critical to their PLM strategy within two years, with open architectures enabling hybrid AI workflows. Challenges remain: interoperability between legacy systems and AI toolkits will dictate adoption speed. However, AI will not just enhance PLM— it will redefine how products are conceived, built, and sustained.
The Era of Intelligent PLM
The convergence of AI and product lifecycle management marks a fundamental shift from static documentation to dynamic, self-optimizing systems. Future PLM platforms will prioritize predictive intelligence over reactive workflows, using generative design to accelerate innovation and digital threads to contextualize data across silos.
Early adopters already see AI-driven tools reducing time-to-market while slashing compliance delays.
