Strategic Management of Emerging Technologies: A Computational Perspective

Abstract

The strategic management of emerging technologies has become a pivotal concern for modern enterprises operating in an increasingly complex and dynamic environment. As digital transformation accelerates across industries, organizations must develop agile and forward-looking strategies to harness the potential of new technologies such as artificial intelligence, blockchain, quantum computing, and the Internet of Things. This paper explores the strategic management of emerging technologies through a computational perspective, integrating analytical models, data-driven decision-making, and simulation-based frameworks to support effective technology planning, adoption, and integration.

The research begins by examining the nature of emerging technologies, focusing on their disruptive potential, lifecycle stages, and the uncertainties they introduce into strategic decision-making. Traditional strategic models often fall short in capturing the non-linear, fast-evolving nature of technological change. Therefore, this paper advocates for the incorporation of computational approaches—such as system dynamics, agent-based modeling, machine learning, and decision support systems—into the strategic management process.

A computational perspective allows organizations to model complex interactions among market forces, technological trends, and organizational capabilities. By simulating different scenarios and outcomes, decision-makers can evaluate risks, forecast adoption trajectories, and identify optimal resource allocation strategies. The study proposes a hybrid framework that combines foresight analysis, innovation diffusion theory, and real-time data analytics to guide strategic choices.

Empirical evidence is drawn from case studies across high-tech sectors where computational tools have enhanced strategic agility and technological foresight. These include AI-driven portfolio analysis in R&D management, predictive modeling for technology adoption in healthcare, and digital twin simulations in manufacturing strategy. The results reveal that computational models not only improve the accuracy and speed of strategic decision-making but also foster a proactive innovation culture.

The paper concludes by highlighting key enablers for successfully implementing computational approaches in strategic management, such as data infrastructure, cross-functional collaboration, and leadership commitment. Challenges related to model complexity, interpretability, and organizational resistance are also discussed. Ultimately, this research underscores the transformative potential of computational thinking in managing emerging technologies and provides a roadmap for future-ready organizations seeking to navigate technological turbulence with resilience and strategic clarity.