How to Adopt AI into Your Legacy PLM System: A Practical Guide for Product Manufacturers
In this article, let’s look at a step-by-step framework to successfully integrate AI into your legacy PLM System.
How to Adopt AI into Your Legacy PLM System: A Practical Guide for Product Manufacturers
Artificial Intelligence (AI) is rapidly transforming the world of manufacturing, and Product Lifecycle Management (PLM) is no exception.
Traditionally viewed as static, data-heavy systems, PLMs are now being reimagined as dynamic, intelligent environments that support decision-making, automate tasks, and surface insights.
But for many manufacturers still relying on legacy PLM systems, adopting AI can seem like a distant, complex goal.
The good news? You don’t need a complete overhaul to start benefiting from AI.
With a systematic, phased approach, even older PLM systems can evolve to support intelligent capabilities.
This article walks you through a step-by-step framework to successfully integrate AI into your legacy PLM — and transform it from a digital archive into a true innovation partner.
Step 1: Define a Clear Vision and Business Use Cases
Before diving into technology, take a step back and define why you want to adopt AI in your PLM system.
Avoid vague goals like “we want to be more AI-driven.” Instead, align your vision with specific, high-impact business outcomes.
Key Questions:
- What current PLM pain points do we want to solve?
- Where is human effort being wasted in data-heavy tasks?
- What decisions could be better informed with predictive or generative AI?
Common Use Cases:
- Auto-generating engineering change order (ECO) summaries
- Suggesting reusable parts or approved materials during design
- Flagging compliance risks in BOMs
- Interpreting test results and recommending next steps
- Automating routine documentation tasks
Tip: Focus on use cases that are narrow, repetitive, and data-driven. These are ideal entry points for AI.
Step 2: Audit and Clean Your PLM Data
AI’s value depends on data quality and accessibility. Legacy PLM systems often suffer from unstructured data, duplicate records, outdated formats, and inconsistent naming conventions.
Action Steps:
- Conduct a data audit: identify gaps, inconsistencies, and high-priority data sets.
- Clean and normalize data structures (naming conventions, metadata, units).
- Archive or tag obsolete data that should be excluded from AI models.
- Ensure proper data governance policies are in place — especially for sensitive product IP.
Tip: AI can’t read between the lines. Clean data is the foundation for meaningful AI insight.
Step 3: Modernize PLM Access with APIs and Middleware
Legacy PLM systems were often built as monolithic platforms with limited interoperability. To integrate AI — especially LLMs or external agents — your PLM must become accessible via APIs or MCPs or middleware platforms.
Action Steps:
- Work with your PLM vendor to expose key data objects via RESTful APIs.
- Consider using middleware tools or enterprise service buses (ESBs) to act as translators between AI services and the PLM.
- If APIs aren’t natively available, use RPA (Robotic Process Automation) to extract data for AI tools in a controlled, non-invasive way.
- Refer to our old article HERE on this.
Tip: Think of this as “decoupling the data” — allowing AI to see and interact with product information without changing the core PLM system.
Step 4: Choose the Right AI Tools and Partners
AI isn’t a monolith. There are different tools for different purposes — from generative AI (like GPT) to machine learning (ML) models for predictive analytics, to domain-specific copilots for engineering and manufacturing.
Key Criteria for Selection:
- Can the tool integrate with your PLM (via APIs or connectors)?
- Does it support on-prem or hybrid deployments (for data-sensitive industries)?
- Does the vendor understand manufacturing and engineering domain needs?
- Can the tool respect access control and compliance requirements?
AI Categories to Consider:
- LLMs (for text generation, summarization, documentation)
- ML models (for predictive maintenance, demand forecasting)
- Natural Language Interfaces (AI chatbots for querying PLM data)
- Autonomous Agents (for multi-step task automation within workflows)
Tip: Start with a narrow AI pilot using a proven vendor — scale only once results are validated.
Step 5: Pilot with a Targeted Use Case
AI adoption doesn’t mean deploying bots across the entire enterprise on day one. Start small and measure impact.
A well-scoped pilot project will help you understand technical feasibility, user experience, and potential ROI.
Ideal Characteristics of a Pilot:
- Involves a small team (design, quality, or procurement)
- Uses a clean, well-structured subset of PLM data
- Has measurable metrics (e.g., time saved per task, error reduction)
- Can be deployed without changing core PLM logic
Example Pilots:
- Auto-tagging ECOs and suggesting reviewers based on historical patterns
- Drafting design rationale or test reports from activity logs
- Searching BOMs using natural language queries (“Show me parts with lead time > 4 weeks”)
Tip: Communicate that the goal of the pilot is augmentation, not automation. AI is there to help users, not replace them.
Step 6: Train and Enable Your Teams
Even the best AI tools will fail if users don’t trust or understand them.
As AI begins to support workflows inside your PLM system, invest in change management and training.
Action Steps:
- Run internal workshops to explain how AI assistants work and how they protect IP
- Demonstrate successful pilot results to build trust
- Collect feedback loops to refine AI behavior and UI integration
- Identify “AI Champions” within departments who can lead adoption from within
Tip: Position AI as a co-pilot — an assistant that frees up experts to focus on value-added work.
Step 7: Establish Security, Compliance, and Governance
PLM systems manage highly sensitive data — from CAD models to supplier pricing. Connecting AI systems to PLM data must be done with robust security controls and clear governance.
Action Areas:
- Define access roles for AI tools (what data can be seen and by whom)
- Implement audit logs and traceability for AI-generated actions
- Ensure AI vendors meet your industry’s regulatory and cybersecurity standards (ISO 27001, GDPR, ITAR, etc.)
- Avoid uncontrolled use of public cloud LLMs with proprietary data
Tip: If possible, use on-prem LLM deployments or private AI clouds to keep IP fully protected.
Step 8: Scale with a Long-Term AI Roadmap
Once the pilot succeeds and teams begin adopting AI, it’s time to scale with a roadmap that matches your digital transformation goals.
Considerations:
- Identify which business units or workflows will benefit next
- Prioritize use cases with measurable ROI or strategic value
- Standardize AI interfaces across departments to reduce fragmentation
- Embed AI into governance processes for design reviews, supplier onboarding, and quality control
Tip: Don’t aim for AI perfection. Focus on iterative improvement and organizational learning.
Final Thoughts: PLM + AI = A Smarter, Faster Future
AI is not a bolt-on feature; it’s a mindset shift in how we use digital tools to make better products.
For manufacturers using legacy PLM systems, the path to AI doesn’t require a complete platform change. It starts with clean data, accessible APIs, and a clear focus on value.
By following a thoughtful, phased approach, you can:
- Make PLM more usable and intelligent
- Reduce engineering time and human error
- Empower teams with real-time insights
- Future-proof your digital infrastructure
The AI era is here — and it’s ready to breathe new life into your PLM system. Start small, think big, and let AI help you build better products, faster.
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