AI decisions in critical systems like defense and med-tech require safety, traceability, and explainability to convince regulators and assign accountability.
The classic V-model for product validation struggles with today's complex systems, leading to late discovery of integration issues and exploding costs.
AI's probabilistic nature conflicts with deterministic validation methods, creating a gap where models behave unpredictably in production despite passing tests.
To manage AI's dynamic behavior, its components like data, models, and pipelines must be treated as version-controlled product artifacts.
Product Lifecycle Management (PLM) provides the essential digital thread to connect, trace, and govern AI components throughout the entire product lifecycle.
Digital twins provide a virtual environment to continuously validate AI behavior by simulating thousands of real-world scenarios, including rare failures and edge cases.
AI assists rather than replaces human experts, whose decisions and feedback are captured within the lifecycle to ensure accountability and responsible operation.
A closed-loop architecture ensures AI models are continuously trained, tested in a digital twin, monitored in production, and improved based on feedback.
A four-layer blueprint combines a PLM governance layer, a digital twin validation layer, an AI runtime layer, and human oversight for a complete system.
The core principles for success are to treat AI as a product component, use digital twins for testing, close the feedback loop, and ensure end-to-end traceability.
Jobs that call for the skills explored in this talk.