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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

3:31 min

Navigating evolving FINRA and SEC regulations for AI

Kumesh Aroomoogan Kumesh Aroomoogan +2 · World Congress 2026 North America

6:51 min

Defining proven AI in high-stakes environments

Bharat Melag Bharat Melag +4 · World Congress 2026 North America

2:41 min

Navigating regulatory uncertainty and deliberate adoption

Bharat Melag Bharat Melag +4 · World Congress 2026 North America

3:08 min

Managing the impact of AI on software trust

Serge Baumberger Serge Baumberger · Europe 2026 Virtual

44 sec

Introduction to AI safety and responsible architecture

Ashok Prakash Ashok Prakash · World Congress 2026 North America

1:56 min

Navigating AI adoption in highly regulated environments

Leigh-Ann Russell Leigh-Ann Russell · World Congress 2026 North America

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