08 September 2026 5:30 PM PT
Artificial Intelligence (AI) and Machine Learning (ML) present a transformative opportunity to enhance how systems are designed, deployed, and managed across their lifecycle. Rather than replacing the human systems engineer, modern AI/ML tools serve as force multipliers for decision-making, pattern recognition, and predictive analysis.
In this presentation, we explore practical, field-tested integration strategies for deploying Large Language Models (LLMs) such as ChatGPT directly into systems engineering workflows. From accelerating automated requirements verification and refining trade studies to enhancing Model-Based Systems Engineering (MBSE), this session demonstrates how generative AI can augment traditional frameworks and become another tool in the systems engineer’s toolkit. Drawing on real-world application patterns and practical case studies, we outline structured approaches for identifying emergent architectural risks, detecting requirement ambiguities, and mitigating lifecycle bottlenecks long before they manifest in hardware or software integration.
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