Despite growing enthusiasm around artificial intelligence, many OEMs still struggle to unlock the full value of their Product Lifecycle Management (PLM) systems. These platforms house decades of engineering knowledge, yet much remains locked in unstructured formats, siloed systems, and disconnected workflows. As industries like automotive, aerospace, and medical devices evolve, modernizing PLM with AI has become a strategic imperative.
Why AI in PLM Now?
PLM has long been essential for managing product complexity, regulatory compliance, and lifecycle data across industries such as aerospace, automotive, medical devices, and high tech. As product development cycles shrink, markets grow volatile, regulations tighten, and supply chains become more complex, PLM’s role is more critical than ever. It serves as the central repository for product related data and processes, supporting capabilities tailored to each sector—from variant configuration in automotive to formula and recipe management in pharma and food industries.
Critical business drivers such as time to market, rapid change management, material traceability, homologation, and product recall handling have made PLM indispensable. To stay competitive, organizations must leverage data intelligently. AI enables embedding real-time, data-driven insights directly into engineering workflows, transforming PLM from a system of record into a system of intelligence.
AI-enabled PLM accelerates time to market, improves first-time-right rates through contextual design and compliance guidance, and lowers the cost of quality by enabling early issue detection. It also strengthens collaboration between design and manufacturing teams by providing insights at the point of decision. Most importantly, AI helps institutionalize knowledge, making expertise accessible across teams and enabling scalable, consistent decision making.
Key Data Limitations and AI Implementation Challenges
AI’s effectiveness in PLM depends heavily on the quality and structure of engineering data. This requires data to be clean, tagged, and connected across bills of materials, CAD models, compliance records, and test results. However, organizations often have fragmentedfragmented, and inconsistent data spread across different BOM structures, missing metadata, outdated guidelines, disconnected systems, and untagged test outcomes. These issues undermine AI’s ability to generate accurate and meaningful insights.
Additionally, the loss of tribal knowledge as experienced engineers retire creates knowledge gaps that AI cannot bridge without properly digitized and structured knowledge bases. The absence of real time, contextual insights integrated within engineering workflows further limits timely, data driven decision making. Without addressing these foundational challenges both data quality and knowledge management AI initiatives in PLM increases the risk of delivering limited value and may fail to reach their full potential.
The Cost of Inaction
Gartner predicts that by 2026, 50 percent of PLM vendor solutions will incorporate generative AI capabilities, up from just 5 percent in 2023. This rapid shift underscores the growing role of AI in product development and highlights the urgent need for OEMs to prepare their PLM environments.
Organizations that delay modernization risk repeated design errors, manual rework, slower time to market, and rising quality costs from late-stage failures and recalls. Competitors adopting AI-driven workflows will gain a technological edge and thereby market share. Outdated PLM systems will also struggle to scale innovation across global platforms and manage growing regulatory complexity.
A Framework for AI Enabled PLM Transformation
To fully realize the potential of generative AI in engineering, OEMs must adopt a structured transformation framework built on four foundational pillars.
- The first is data readiness, which involves ensuring that data across bills of materials, CAD systems, and test environments is clean, well tagged, and interconnected enabling AI to deliver meaningful and reliable insights.
- The second is AI integration, which requires embedding AI capabilities directly within PLM and CAD tools so that context aware recommendations are surfaced in real time, seamlessly supporting the engineer’s workflow.
- Third is knowledge institutionalization, where organizations must digitize and structure key assets such as design rules, test outcomes, and compliance data converting tribal knowledge into a reusable, enterprise-wide knowledge base.
- Finally, change management is essential to success, involving the upskilling of teams and the realignment of business processes to embrace AI augmented decision making, ensuring adoption and long-term impact.
Real World Use Cases and Proven Impact
Leading OEMs are already transforming product development by integrating AI into PLM, unlocking value through targeted use cases that enhance both engineering decisions and operational execution.
1. AI Guided Design Suggestions
By ingesting structured design guidelines and associating them with part metadata and CAD models, AI can deliver real time, context aware suggestions directly within engineering tools. As components are selected, the system surfaces relevant standards, design rules, and best practices, reducing dependency on manual searches or tribal knowledge. This improves guideline compliance, reduces design errors, and accelerates iteration cycles. Even junior engineers can produce high quality, standards compliant work, boosting overall engineering productivity and reducing rework.
2. Impact Analysis of engineering changes across the organization
AI enables proactive visibility into how design changes affect downstream functions including engineering, manufacturing, planning, and service. For example, introducing a new fastener in product structure prompts automated checks across engineering, manufacturing, production, and service BOMs. The AI evaluates tooling compatibility, supplier lead times, assembly constraints, and serviceability impacts. This comprehensive impact analysis prevents late stage surprises, reduces tool redundancies, and strengthens alignment between design intent and production capabilities, ultimately improving tact time and build schedule reliability.
3. Design Benchmarking Using Past Projects
Generative AI can extract, compare, and benchmark design elements from historical projects to inform early design decisions. When developing a new component such as a windshield or bracket, AI retrieves data like dimensions, weight, material choices, compliance criteria, and prior test outcomes. Engineers gain instant access to cross platform benchmarks and production insights, allowing them to avoid past mistakes, improve manufacturability, and optimize cost and performance. This drives better first-time right rates and reduces design iterations.
4. AI Assisted Performance Testing and Regulatory Compliance
AI systems trained on historical test data, defect logs, and root cause analyses can detect patterns and predict risks during the design phase. If a part previously failed fatigue testing or exhibited corrosion in certain environments, AI flags these risks early and recommends proven countermeasures such as material changes or enhanced surface treatments. It also advises on required regulatory tests for specific markets and shares insights from past certification efforts. This reduces the risk of late-stage design failures, regulatory delays, and product recalls, accelerating time to compliance and improving product reliability.
Conclusion: From Data to Decisions
The true promise of generative AI in PLM is not just automation but transformation. It is about turning decades of fragmented design data into a dynamic, intelligent decision support system that empowers engineers and accelerates innovation. Achieving this vision however requires more than advanced technology. It depends on clean, connected data, integrated systems, and a clearly defined business strategy.
Collectively, AI enabled PLM elevates the system from mere data management to a powerful decision enabling platform. OEMs adopting this approach report faster development cycles, higher first-time right rates, reduced quality costs, and more consistent design practices. Most importantly, AI helps institutionalize engineering knowledge, ensuring that valuable insights are preserved and decisions remain scalable across programs, teams, and global markets.
Organizations that take action now by preparing their data, embedding AI into core engineering processes, and capturing institutional knowledge will not only avoid the common pitfalls of stalled AI initiatives. They will lead the next era of intelligent product development. The future of PLM is not just digital; it is intelligent by design.
