Technology Management and Strategic Decision Making in U.S. Manufacturing Firms: Insights from Artificial Intelligence and Data Driven Systems

Abstract

Artificial intelligence (AI) and data‑driven decision systems are transforming U.S. industry by delivering real‑time insights, assisting predictive maintenance and automating complicated decisions. Yet adoption remains unequal, and the managerial consequences of AI are poorly understood. This qualitative study analyses how technology management and strategic decision‑making evolve when AI and analytics are integrated into U.S. manufacturing organisations. The research relies only on secondary data—published case studies, academic literature, industry reports and policy documents—to minimise the biases inherent with primary interviews or surveys. The study targets one overarching objective: to understand how data‑driven technologies influence decision‑making practices and organizational capabilities. Two specific objectives are (i) to identify the socio‑technical and managerial factors that enable effective use of AI and data analytics in manufacturing, and (ii) to explore how firms cultivate strategic and dynamic capabilities to sustain competitive advantage in the face of rapid technological change. The findings reveal that AI adoption often follows a staged trajectory with short‑term inefficiencies before long‑term gains; that human–machine collaboration, data governance and ethical considerations are central to successful integration; and that capability building—through training, leadership and cross‑functional collaboration—is critical for realizing the benefits of data‑driven systems. The article proposes a conceptual model of technology management that emphasizes strategic alignment, collaborative design and continual learning. Implications for practitioners and policymakers are highlighted.