Blog Manufacturing AI

5 Predictions for AI in PLM

author
CLEVR
Last Update
July 18, 2025
Published
July 11, 2025

Manufacturers today need to innovate faster, manage more complex products, and operate sustainably. In this challenging environment, the collaboration of Artificial Intelligence (AI) and Product Lifecycle Management (PLM) is truly changing how things get done. 

This combination provides a practical solution that's helping companies handle market demands right now. This article looks at five key ways AI is reshaping PLM and what that means for leaders in manufacturing.

Short on Time? Here's a Brief Overview

  • AI will help predict design problems early and even generate new design options, speeding up innovation and improving product features.
  • AI will automate many routine repetitive tasks, letting engineers focus on complex problem-solving and creative work.
  • Data from products in use will constantly feed back into AI-driven digital twins, allowing for continuous improvement across the entire product lifecycle.
  • Manufacturers should check if their current PLM systems are AI-ready, try out small AI projects first, and build teams with the right mix of skills.

5 Predictions for AI in PLM

AI is a powerful tool for manufacturing companies, opening up new ways to improve processes, spark innovation, and get ahead of the competition.

Prediction 1: AI will enable predictive design and development

AI is set to change product design from simply reacting to problems to actively preventing them. A big part of this is catching potential failures early. AI systems can look at tons of old data, simulation results, and how products perform in the real world to spot design flaws, material issues, or manufacturing headaches before they become real problems. This means lower development costs and faster delivery of products.

AI also helps make smarter decisions right from the start, going beyond simply predicting failures. By looking at market trends, customer feedback, and current product performance data, AI can help your engineers choose better initial designs and fine-tune product features to what the market actually wants. 

Prediction 2: Intelligent automation will reshape engineering workflows

AI is going to change engineering workflows by automating repetitive tasks. Think about all the routine, rule-based jobs that eat up engineers' time: data entry, updating Bills of Materials (BOMs), handling engineering change orders (ECOs), creating standard reports, and putting together compliance documents. AI is perfect for taking over these kinds of tasks. In fact, studies show that 77% of workers say automating repetitive tasks saves them about 3.6 hours a week.

AI will also act as a "co-pilot" or smart assistant for engineers. AI tools can offer design ideas in real time, quickly search through huge technical databases, help optimize designs based on different factors, or point out potential problems as designs take shape.

Prediction 3: Closed-loop manufacturing will become the norm

Get ready for closed-loop manufacturing to become standard, thanks to AI. Closed-loop manufacturing is where data from a product's later life stages continuously flows back to improve the early stages, especially in design and engineering. It makes Product Lifecycle Management a living, breathing system. 

Real-time feedback is key here. IoT sensors in real, physical products, along with data from Manufacturing Execution Systems (MES) on the factory floor, provide a steady stream of performance and operational data.

Digital twins are a big part of this, too. These are live virtual copies of physical things and processes. They're constantly updated with real-time data, and AI systems study this information within the digital twin to spot patterns, predict when maintenance is needed, and find ways for improvement. 

McKinsey states that digital twin tech can increase revenue by about 10% and get products to market up to 50% faster. Airbus, for example, already uses digital twins for its aircraft programs to test scenarios and improve processes. 

AI takes all this feedback and suggests design changes for future product developments, improving manufacturing processes, and boosting product reliability. This constant improvement also helps with supply chain management by giving a clearer picture of how parts are performing and what's needed. 

Tools like Siemens Teamcenter are essential for handling the data for these digital twins and pulling in information from Manufacturing Operation Management (MOM) systems that coordinate and optimize factory floor processes.

Prediction 4: AI will transform compliance and quality management

AI systems can check product design details, material selections, and supplier information against huge databases of rules (i.e., safety, environmental concerns, and specific industry needs) and internal quality goals. This means potential compliance problems can be spotted and flagged right at the start—during design—which saves a lot of money and trouble later on.

AI will also take over parts of compliance checking, such as helping to create compliance reports, ensuring all paperwork is complete, and confirming that manufacturing steps meet the required standards. This helps companies make more informed decisions about quality and compliance. Plus, when AI is built into PLM systems, it can create solid, easy-to-search audit trails.

AI-powered vision systems and data analysis are already playing a crucial role in quality checks. For instance, BMW's GenAI4Q project uses AI to create custom inspection checklists for every car, using specs and sensor data to find defects.

Prediction 5: Human-AI collaboration will redefine roles

When AI joins PLM, the main effect is boosting what people can do and changing their roles, rather than replacing them. The focus will move from doing manual tasks to thinking strategically and innovating. Engineers and PLM managers will increasingly count on AI to handle data work, routine analysis, and automated tasks. This will let them spend more time on big-picture thinking, tough problem-solving, and overseeing the AI systems. 

A good example of this teamwork is CLEVRAssist, a solution developed with automation technology leader Festo, which uses AI to help people learn and interact in industrial environments.

What Manufacturers Should Do Now

So, how can manufacturers get started with AI in PLM? Taking a few practical steps now can build a solid base for bringing in AI and getting the most out of it.

First off, check if your PLM setup is ready for AI. This means taking a good look at the data you have in your PLM, ERP, MES, and other systems. Is it of good quality? Is it complete? Can you get to it easily? Make sure your systems can talk to each other, using APIs or cloud platforms, so data can flow smoothly.

Next, begin with small projects that can make a big difference without much risk. Don't try to change everything at once. Instead, test AI on specific things. For instance, try predictive maintenance for one type of machine, use generative design for a less critical part, or automate a PLM workflow that's causing headaches. 

And third, put together teams with people from data, IT, and engineering. To make AI in PLM work, you need engineers who know the products, data scientists who can build and run AI models, and IT staff who can handle the tech side and ensure everything connects. 

PLM Is the Intelligence Hub of Future Manufacturing

The incorporation of AI is changing Product Lifecycle Management from a simple data storage place into a smart, active center that helps make decisions throughout the entire product lifecycle. This is a necessary step for manufacturers to innovate quickly, work more efficiently, and build stronger businesses. Companies that start using AI in their PLM plans now will be in the best spot to lead the way in manufacturing's future.

Research Methodology

The ideas in this article come from looking closely at industry reports from well-known sources like Gartner, McKinsey, and Deloitte. We also studied how AI and Product Lifecycle Management are being used in real manufacturing companies, including specific case studies and expert opinions.

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FAQ

Can't find the answer to your question? Just get in touch

1

What's the typical ROI timeline for AI investments in PLM systems?

Most manufacturers see initial returns within 12 to 18 months. Early gains typically come from workflow automation and quality improvements, while returns from predictive capabilities and closed-loop manufacturing usually arise in years two and three.

1

How can small- to medium-sized manufacturers (SMEs) benefit from AI in PLM?

AI tools and cloud services are easier to access these days. SMEs can gain a lot by using AI for specific tasks, like automating certain workflows or using AI to predict trends, all without needing a huge budget.

1

What are the main challenges when implementing AI in PLM?

The usual roadblocks are getting good quality data, ensuring AI tools work smoothly with existing PLM and company systems, and helping your team learn new skills related to data and AI.

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