CAD Files Are Not AI-Ready, But Your Product Lifecycle Depends on Them

80% of manufacturers are increasingly adopting artificial intelligence (AI) to accelerate innovation, streamline operations, meet regulatory demands, reduce costs, shorten production cycles, and achieve a range of measurable outcomes.

One critical roadblock continues to stall progress: the inability of AI systems to access and interpret data trapped inside unstructured documents, like handwritten reports, spreadsheets, MBOMs, SOPs, etc. Today, we’re zeroing in on AI’s inability to read, interpret, and extract data from CAD files.

CAD documents are essential to product development, engineering, and quality assurance processes... BUT they are among the least AI-ready content types in the modern digital enterprise. These files contain high-value information (technical schematics, specifications, and annotations) that are typically locked in proprietary formats and require specialized software to open or interpret.

To increase adoption of AI across the product lifecycle, organizations must begin transforming these static assets into structured, usable data. Some do it through (mostly) manual effort, such as engineers converting CADs to shareable PDFs. But this is a costly, time-consuming, and error-prone approach.

Here is a scalable, automated way to transform volumes of engineering CAD content into AI-ready assets up to 95% faster, while maintaining 100% pixel-perfect accuracy and full compliance.

Learn more about:
Closing the EBOM-MBOM Gap with Dassault and Adlib >
How AI-Driven CAD File Automation Saves Engineers Time and Money >
The Critical Role of Adlib in Dassault Solutions like 3DX, Catia, and Solidworks >

The AI-CAD Disconnect

AI systems thrive on structured and contextualized information. However, CAD files (generated in CATIA, SolidWorks, AutoCAD, or other platforms) are not inherently structured in a way AI can consume. Their contents are layered, visual, and often embedded with design data that lacks machine-readable metadata.

For AI tools to deliver insights, like predictive maintenance forecasts, supplier risk analysis, or automated compliance validation, they must be trained on clean, well-labeled datasets. Unfortunately, CAD files rarely meet this standard. When these files remain isolated from enterprise systems or locked in incompatible formats, it limits an organization’s ability to leverage AI effectively.

“In my opinion, we don’t have an AI problem. We have a document readiness problem. Until we make the data locked in CAD files and specs accessible, AI is just guessing. Real transformation starts by giving our systems something meaningful to work with.” - Anthony Vigliotti, Chief Product Officer, Adlib

The Product Lifecycle Runs on Documentation

From initial concept to manufacturing, quality assurance, and regulatory submission, essentially every stage in product lifecycle relies on documentation. In regulated industries such as aerospace, life sciences, and energy, documents like SOPs, batch records, design drawings, and change orders are mission-critical.

When these documents exist in unstructured or semi-structured formats, they slow down key workflows:

• Regulatory submissions may be rejected due to formatting issues or missing data.
• Cross-functional collaboration is hindered when documents are inaccessible or inconsistent.
• AI initiatives stall when content cannot be extracted, indexed, or interpreted.

Despite investments in robust Product Lifecycle Management (PLM) platforms such as Dassault Systèmes’ ENOVIA, many organizations still face this challenge. While ENOVIA excels in managing structured records, it was not designed to extract or convert the contents of CAD files and other unstructured documents.

Making CAD Files AI-Ready with Document Transformation

Many leading, forward-thinking organizations are incorporating document transformation solutions alongside their PLM systems. One such example is the integration of Adlib Software with ENOVIA.

Adlib enables organizations to transform more than 300 document types (including CAD drawings, PDF specifications, and scanned quality records) into structured, AI-usable formats. This includes:

• Rendering high-fidelity CAD drawings into searchable, lightweight PDFs with embedded metadata
• Automating document classification, stamping, watermarking, and version control
• Extracting structured data from legacy formats and handwritten notes
• Assembling complex documentation packages for regulatory or supplier review

Together, ENOVIA and Adlib create an end-to-end pipeline where unstructured engineering content can be converted into digital assets that are accessible, traceable, and actionable across teams and systems.

Learn more:
The Critical Role of Adlib in Dassault Solutions like 3DX, Catia, and Solidworks >

Use Cases Across the Product Lifecycle

1. Regulatory Submissions

Regulatory authorities such as the FDA or EMA require precise formatting and metadata standards. Adlib automates the generation of compliant, submission-ready documents, eliminating delays due to formatting errors and reducing resubmission rates by up to 60%.

2. Supplier Collaboration

Procurement and engineering teams can use Adlib to standardize supplier-submitted CAD files and specifications into consistent formats. This streamlines intake and evaluation processes, especially when managing hundreds of files across global suppliers.

3. Shop Floor Access and Maintenance

Technicians on the manufacturing floor often lack access to design software. Adlib renders CAD files into accessible PDFs that include embedded data such as version history and approval status, ensuring accurate execution without the need for native applications.

4. AI Enablement and Predictive Analytics

Organizations leveraging AI for predictive failure detection, digital twins, or automated QA benefit from Adlib’s ability to convert historical design files into clean, structured data inputs. This significantly improves AI model training and insight accuracy.

5. Documentation for Compliance and Quality Assurance

Adlib supports traceability by embedding audit trails and metadata directly into files. This enables teams to meet ISO, FDA, and SOX documentation standards while reducing manual work and preparation time.

The Big Shift: From Static PLM to AI-Ready Workflows

We’re in a moment of transformation. PLM isn’t just about managing data anymore, it’s about activating it. You can’t build a digital twin if half your documents are unreadable. You can’t automate workflows if your PDFs aren’t structured.

And you can’t deploy AI across your product lifecycle if your inputs are fragmented. AI-readiness starts before the model. It starts at the document level.