The Impact of Artificial Intelligence on Strategic Technology Management: A Mixed-Methods Analysis of Resources, Capabilities, and Human-AI Collaboration

TL;DR

This study explores how AI transforms strategic technology management by emphasizing human-AI collaboration, resource requirements, and success factors, supported by a new conceptual framework and mixed-methods research.

Contribution

It introduces the AIbSTM framework integrating technical, human, and organizational aspects, and extends the Resource-Based View to AI-enhanced strategic management.

Findings

AI enables data-driven strategic alignment and adaptation.

Success relies on proprietary data, talent, and governance.

Human-AI collaboration is preferred over autonomous AI leadership.

Abstract

This paper investigates how artificial intelligence (AI) can be effectively integrated into Strategic Technology Management (STM) practices to enhance the strategic alignment and effectiveness of technology investments. Through a mixed-methods approach combining quantitative survey data (n=230) and qualitative expert interviews (n=14), this study addresses three critical research questions: what success factors AI innovates for STM roadmap formulation under uncertainty; what resources and capabilities organizations require for AI-enhanced STM; and how human-AI interaction should be designed for complex STM tasks. The findings reveal that AI fundamentally transforms STM through data-driven strategic alignment and continuous adaptation, while success depends on cultivating proprietary data ecosystems, specialized human talent, and robust governance capabilities. The study introduces the AI-based Strategic Technology Management (AIbSTM) conceptual framework, which synthesizes technical capabilities with human and organizational dimensions across three layers: strategic alignment, resource-based view, and human-AI interaction. Contrary to visions of autonomous AI leadership, the research demonstrates that the most viable trajectory is human-centric augmentation, where AI serves as a collaborative partner rather than a replacement for human judgment. This work contributes to theory by extending the Resource-Based View to AI contexts and addressing cognitive and socio-technical chasms in AI adoption, while offering practitioners a prescriptive framework for navigating AI integration in strategic technology management.