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

• URL: http://arxiv.org/abs/2512.08938v1
• Date: Sun, 26 Oct 2025 17:34:08 GMT
• Title: The Impact of Artificial Intelligence on Strategic Technology Management: A Mixed-Methods Analysis of Resources, Capabilities, and Human-AI Collaboration
• Authors: Massimo Fascinari, Vincent English,
• Abstract summary: The study introduces the AI-based Strategic Technology Management (AIbSTM) conceptual framework.<n>Contrary to visions of autonomous AI leadership, the research demonstrates that the most viable trajectory is human-centric augmentation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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