Data migration is one of the most important steps in any 3DEXPERIENCE implementation. Organizations often have decades of legacy data, such as drawings, specifications, bills of materials (BOMs), and other engineering documents, stored in various formats. Many times, this information is available only in read-only formats like PDFs, scanned documents, or proprietary systems. This makes it very hard to extract and reuse directly.

Traditionally, converting legacy data into the machine-readable format required by 3DEXPERIENCE has been a manual, time-consuming process that often leads to errors. Large teams of engineers and data specialists spend countless hours cleaning, formatting, and validating data before it is ready for migration.

AI as a Game-Changer in Data Transformation

With the rise of AI-powered tools like ChatGPT, Copilot, and others, the way we approach data transformation has changed a lot. Instead of relying mainly on manual data entry and scripting, AI can:

• Interpret unstructured data, such as PDFs, scanned documents, and legacy text files.
• Reformat content into structured formats like Excel, CSV, or XML that meet 3DEXPERIENCE requirements.
• Minimize errors by applying consistency checks and pattern recognition.
• Cut down on transformation time, leading to faster migration cycles.

Real-World Example: Converting Paint Specification Data

At BWC, we recently used AI tools to convert Paint Specification Data stored in PDFs into Excel sheets for smooth migration to the 3DEXPERIENCE cloud.

• AI read and interpreted the PDF content.
• Data was reformatted into the structure required by 3DEXPERIENCE.
• Manual work was limited to validation and handling exceptions.

The result?

• 80% reduction in data conversion time compared to traditional manual methods.
• Significant reduction in errors and rework.
• Faster go-live on the 3DEXPERIENCE platform.

Addressing Data Privacy and Limitations

While the potential is immense, data privacy remains a key concern. Not every dataset can or should be processed using public AI tools due to confidentiality and compliance issues. Organizations will need to:

• Build domain-specific AI conversion models in secure environments.
• Define data governance policies to ensure IP protection.
• Balance AI automation with human oversight for critical datasets.

Conclusion: Smarter Implementations with AI

The smart use of AI in data transformation for 3DEXPERIENCE migrations can greatly reduce overall project timelines and execution costs. By automating repetitive and error-prone tasks, organizations can speed up their digital transformation, achieve faster returns on investment, and allow their teams to focus on more valuable work. 

At BWC, we believe the future of PLM data migration lies in the collaboration between AI and human expertise—leading to faster, more accurate, and cost-effective implementations.