World Congress 2026 Europe - Virtual Stage • Jul 2, 2026 • Session details

AI in Regulated Industry - Validating AI-Enabled Products with PLM and Digital Twins

Murli Mohan Srinivas

In regulated industries, unexplained AI is just a confident guess. Discover how integrating PLM and digital twins makes probabilistic models safe, traceable, and fully certifiable.

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#1 about 2 min

Explainability requirements for AI in regulated industries

Using AI in high-stakes fields requires convincing regulators that decisions are safe, traceable, and explainable.

#2 about 4 min

Validating probabilistic AI behaviors in high-stakes environments

Unpredictable and adaptive AI models require rigorous end-to-end traceability across defense and medical applications.

#3 about 3 min

Overcoming validation gaps in traditional engineering models

Complex product lifecycles require moving validation from a late-stage activity to a continuous closed-loop process.

#4 about 4 min

Shifting to smart validation for probabilistic AI models

Because AI systems are probabilistic and prone to edge-case failures, static testing must be replaced by continuous simulation.

#5 about 4 min

Treating AI models as regulated product artifacts

Embedding AI data and pipelines into a closed-loop product lifecycle management system ensures auditable and governable behavior.

#6 about 3 min

Simulating edge cases using digital twin technology

Digital twins allow teams to test rare failures and edge cases continuously before models hit production.

#7 about 2 min

Maintaining human oversight for responsible AI automation

Capturing traceable human decisions within the validation loop guarantees that automated processes remain supervised and compliant.

#8 about 4 min

Implementing an architecture blueprint for continuous validation

A four-layer architecture combining governance, digital twins, runtime monitoring, and human control enables an auditable AI system.

#9 about 3 min

Securing innovation through traceability and continuous validation

Success with AI in regulated environments depends on treating models as components and closing the validation loop.

Matching moments

3:08 min

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Serge Baumberger · Europe 2026 Virtual

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3:32 min

Transitioning toward a trustworthy AI development life cycle

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3:20 min

Bridging constraints between product management and software engineering

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Alexandre Guenoun +3 · World Congress 2026 Europe

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Addressing data sovereignty and compliance blind spots within AI

Sebastian Kister

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2:17 min

Overcoming integration challenges in regulated industry AI deployments

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