Mayank Vadaliya, an Application Support Engineer at Tesla, a doctoral researcher in Information Technology at the University of the Cumberlands, and a Full Member of Sigma Xi, The Scientific Research Honor Society, investigates systemic breakdowns in modern manufacturing and automotive sectors that rely on highly integrated, opaque software networks. His research focuses on the intersection of machine learning, artificial intelligence, and physical autonomous systems under real-world constraints. Industry trends show growing reliance on automated diagnostic tools, yet complex software environments still demand rigorous, structured human oversight for safety. Vadaliya applies established incident response frameworks to modern artificial intelligence pipelines, prioritizing verifiable data constraints over rapid deployment cycles to reduce catastrophic operational failures. He advocates for structured inquiry over immediate symptom remediation, noting, "It's a symptom in one layer of the software stack while the real coupling lives upstream or sideways in another dependency." He recommends frameworks like the Five Whys, which "forces the room to keep asking 'what would have to be true for this to happen?' until you reach something falsifiable." Establishing verifiable thresholds between handoffs prevents localized anomalies from cascading into total system outages during critical production windows. Applying these structured principles to complex datasets enables organizations to distinguish between a minor statistical fluctuation and a foundational software defect.