From GenAI hype to trusted AI: engineering’s next frontier

When the wrong definition of AI becomes a risk

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Artificial Intelligence is everywhere in the headlines. Yet in the public imagination, AI has been reduced to Generative AI (GenAI) — large language models that write paragraphs, chatbots that simulate dialogue, or diffusion models that produce images from prompts. This narrow definition has created a dangerous misconception: that the same technologies that autocomplete text can be applied to engineering design, CAD modeling, and system validation.

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This is just a category error.

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Engineering is not about producing outputs that are “plausible.” It is about producing outputs that are deterministically correct, fully traceable, and compliant with standards. In design engineering, a misplaced routing, an incorrect dimension, or an unverified tolerance is not a minor flaw; it can halt production, trigger costly recalls, or endanger lives.

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• Generative AI is probabilistic: it predicts what “looks likely” based on patterns in data.
• Engineering AI must be deterministic: it encodes rules, ensures compliance, and delivers outputs that are verifiable and audit-ready.

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This is the paradigm Dessia brings to industry: AI-Apps that generate, verify, and validate engineering workflows with transparency and compliance at scale.

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Why Generative AI falls short in engineering

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Generative AI (GenAI) has been a revolution in content creation — text, imagery, even music. But its foundations make it unsuitable for mission-critical engineering tasks:

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• Probabilistic outputs: Generative AI delivers what seems statistically plausible, not what is guaranteed correct.
• Black-box logic: It is difficult or impossible to explain why a given answer was produced.
• Compliance blind spots: Industry standards, ISO norms, and certification requirements cannot be hard-coded into these models with reliability.
• Safety-critical risks: In aerospace, automotive, or rail, a single error in design validation can translate into catastrophic financial or human costs.

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Contrast this with engineering requirements:

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• Every design element must be rule-compliant.
• Every workflow must be traceable to its data source.
• Every decision must be auditable and reproducible.

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This is why the hype around GenAI in engineering is misleading. Engineers don’t need “plausible” designs; they need valid, certified, and optimized ones.

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Generative vs. Deterministic AI in engineering

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In other words: GenAI is creativity without guarantees. Deterministic AI is engineering with trust.

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The Dessia paradigm: Four pillars of deterministic AI

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At Dessia, our AI-libraries bring determinism, transparency, and scalability to engineering workflows. Our approach is structured around four core categories:

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1. AI for verification & validation

Automating the verification of engineering data, ensuring consistency, compliance, and alignment across CAD, PLM, and BOM systems. This strengthens quality, productivity, and standardization while guaranteeing audit-ready traceability.

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2. AI for knowledge reuse

Unlocking the value of existing engineering knowledge through intelligent data reuse and similarity-driven insights. This enables scalability and efficiency, reducing redundant work and accelerating decision-making.

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3. AI for design assistance

Supporting engineers with AI-driven tools that optimize architectures, guide decision-making, and integrate constraints early in the design cycle. This empowers cost optimization, agility, and productivity throughout development.

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4. AI for generative design

Delivering deterministic generative design capabilities that create valid architectures, routings, and layouts under explicit engineering rules. Unlike probabilistic GenAI, Dessia’s generative design is traceable, compliant, and certifiable, driving first-time-right innovation.

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Conclusion: AI for engineering must mean trust, not hype

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Generative AI may dominate headlines, but engineering requires something deeper: explainability, determinism, and industrial reliability.

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Dessia’s AI-libraries embody this paradigm, they allow engineering teams to innovate faster while meeting the strictest demands of compliance and certification.

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The future of AI in industry will not be won by who creates the most plausible image or paragraph. It will be defined by who builds AI systems that engineers, regulators, and industries can trust — and at Dessia, that future is already here.

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At the same time, this deterministic foundation paves the way for new possibilities. By layering specialized LLMs with Retrieval-Augmented Generation (RAG) on top of our framework, we can further enhance usability and adoption. Chatbots will enable engineers to interact with CAD, PLM, and BOM data in natural language, while agent-based assistants will help customize AI-Apps to specific processes. In this way, the reliability of deterministic AI is complemented by the accessibility of LLM-driven agents, combining trust, compliance, and fluidity in tomorrow’s engineering workflows.

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