Scientific Management 2.0: Automation, Skill Disruption, and the New Productivity Frontier

Abstract

Scientific Management 2.0 represents a contemporary evolution of Frederick Taylor’s foundational principles, reframed within an era defined by advanced automation, algorithmic coordination, artificial intelligence, and continuous digital workflow optimization. As organizations transition from labor-centric to intelligence-augmented systems, managerial decision-making increasingly depends on real-time analytics, autonomous processes, and human–machine complementarities. This paper examines how automation reshapes productivity frontiers, disrupts traditional skill architectures, and redefines managerial control mechanisms within smart organizations operating in digital economies. We argue that Scientific Management 2.0 is not a repudiation of Taylorism, but a technologically amplified reinterpretation that embeds measurement, efficiency, and systematization into algorithmic infrastructures. However, unlike classical Taylorism, which prioritized task decomposition and manual efficiency, this new era emphasizes cognitive augmentation, reskilling dynamics, digital literacy, and human adaptability to accelerated technological change. Through an extensive review of contemporary research, we uncover how digital workflows reduce informational friction, how AI-based orchestration transforms coordination costs, and how new forms of skill polarization emerge as routine work becomes increasingly automated. The findings reaffirm that Scientific Management 2.0 expands the productivity frontier but simultaneously raises concerns regarding labor displacement, algorithmic control, and widening skill inequalities. The paper concludes by proposing managerial frameworks and future research directions to balance automation gains with inclusive workforce development.