Engineering failures and poor system-level decisions continue to cost industries billions annually, often driven by fragmented data environments, disconnected tools, and limited integration across complex systems. As organizations adopt digital engineering practices, the inability to effectively integrate artificial intelligence within these environments remains a critical gap, leading to inconsistent decision-making, reduced traceability, and inefficiencies across the system lifecycle. Current approaches frequently apply AI as a standalone analytical capability, failing to connect insights with engineering processes and limiting its practical impact. This study addresses this challenge by proposing an industry-oriented framework that integrates AI within digital engineering to support system-level decision-making and lifecycle coherence. The framework aligns AI-driven analytics with model-based systems engineering principles, enabling structured integration across design, validation, and operational phases. Key elements include enhanced traceability, improved interoperability, and decision support mechanisms that link data, models, and engineering workflows. The contribution of this research lies in providing a practical and scalable approach for embedding AI into digital engineering environments. The proposed framework supports improved decision consistency, strengthens system integration, and enhances the effectiveness of engineering processes. The findings offer actionable insights for industry practitioners seeking to transition toward more integrated, intelligent, and data-driven engineering systems.