The hype surrounding Artificial Intelligence in engineering is massive. Almost every software vendor now offers smart AI features. When applied correctly, AI-powered solutions deliver maximum leverage right where engineers and designers waste valuable time every day, whether on tedious routine work or background tasks. Examples include automated test case generation, understanding complex documents, or quickly assessing minor design changes.
But behind the scenes, the industry’s reality paints a sobering picture. According to a CIMdata study, while about 80% of software vendors already offer AI features in their portfolios, the actual adoption rate among industrial customers is only 8% to 33%. Nearly nine out of ten industrial companies use AI in less than a quarter of their projects, leaving most initiatives stuck in pilot mode. So why is adoption stalling?
The challenge: the invisible data wall
The biggest hurdle isn’t the algorithm itself, but what comes next: AI integration. For industrial customers, seamless connection to existing systems is a key selection criterion. Yet software and service providers systematically underestimate this barrier. In practice, service providers encounter unexpected issues with legacy systems 2.4 times more often than customers anticipate. When new AI tools are bolted onto existing systems in isolation, they fail when faced with the reality of complex engineering processes. As our Chief Product Officer Frank Patz-Brockmann puts it: “The real pain point isn’t buying the software but getting it activated.”
Integration over API patchwork
CONTACT Software is tackling this problem head-on with Fourier AI. We need to view AI system integration as a fundamental architectural decision, not an afterthought or a quick DIY project. Instead of connecting an isolated external AI via standard interfaces, Fourier AI is deeply embedded as a fully integrated intelligence layer within our proven CONTACT Elements platform.
In customer discussions, we sometimes encounter an urge to throw established data structures overboard and replace them with a “data lake” or even a “data swamp” in the hope that the AI will somehow piece the information together on its own. However, this is entirely the wrong approach, because AI thrives on structured data.
That’s why with Fourier AI, we rely on a clear, bottom-up layered architecture:
- A Single Source of Truth (PLM) as the foundation: At the very base is still the PLM system with its highly structured data. This foundation is essential and serves as the reliable starting point for any intelligence.
- Context building: Simply storing data somewhere isn’t enough. A precise context must be constructed for the AI. For example, if a user submits a prompt like “Give me all data on this Engineering Change,” this context layer needs to know exactly which data belongs to it, who created it, and how everything is connected.
- The model layer: Sitting above that is the model layer. In addition to leading external models, we offer our own specialized models, such as for 3D similarity search. The system automatically selects the right model for specific platform tasks while giving companies the freedom to choose which models they want to access for their custom use cases.
- AI orchestration & governance: To evaluate and continuously improve answers, generated execution traces are captured and logged. The system filters data so the model only returns information the specific user is authorized to see via integrated permission controls.
- User interaction (the PLM chatbot): At the top layer is the user, who interacts directly by typing a query into the PLM chatbot, for example.
Conclusion: Foundation first, value follows
This deep architectural approach initially requires more diligence when structuring data and preparing systems. But that’s precisely where the key advantage lies: because the foundation is cleanly built from day one, the AI layer automatically adapts whenever a customer expands their data model in CONTACT Elements – without writing a single line of additional code. This is how we bridge the gap between mere tech gimmicks and genuine, productive value in day-to-day engineering.
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