The integration of Generative Artificial Intelligence (GenAI) in engineering presents both opportunities and challenges, particularly in complex, safety-critical systems. This thesis explores two key aspects: (1) developing an AI-powered reverse engineering tool for Knowledge-Based Engineering (KBE) applications and (2) designing a communication framework to enhance interdisciplinary collaboration in AI-driven projects. The aerospace engineering component introduces the REProcess tool, leveraging Large Language Models (LLMs) to extract structured process representations from KBE applications, improving traceability and automation. The science communication component investigates collaboration barriers between AI experts and domain specialists, leading to the development of a structured ideation matrix for GenAI-powered automation systems. Together, these contributions demonstrate how Explainable AI principles can facilitate the adoption of GenAI in engineering workflows while bridging communication gaps. This interdisciplinary approach provides practical methodologies to enhance AI-driven engineering processes, ensuring both technical feasibility and effective human-AI collaboration.