DEVELOPMENT OF A METHODOLOGICAL FRAMEWORK FOR INTEGRATING INTELLIGENT TECHNOLOGIES IN ENGINEERING INNOVATION MANAGEMENT

Abstract

This study develops and empirically validates a methodological framework for integrating intelligent technologies into engineering innovation management. In response to increasing technological complexity and data-driven transformation, organizations require structured approaches to effectively leverage artificial intelligence (AI), machine learning (ML), and advanced analytics within innovation processes. The proposed framework adopts a multi-layer architecture encompassing strategic alignment, technological infrastructure, data and analytics capabilities, and organizational readiness. A mixed-method research design is applied, combining conceptual modeling with empirical analysis based on survey data collected from engineering professionals. Statistical techniques, including regression analysis and correlation modeling, are employed to examine the relationships between technology adoption, organizational enablers, and innovation performance. The results indicate a significant positive impact of intelligent technology integration on innovation outcomes, with organizational and data-related factors acting as critical mediators. The study contributes to the literature by providing a structured and empirically grounded framework that bridges the gap between theoretical models and practical implementation. It offers actionable insights for organizations aiming to enhance innovation efficiency, reduce uncertainty, and achieve sustainable competitive advantage through intelligent technologies.