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

Massimo Fascinari; Vincent English

doi:10.71204/qeqqvj92

Authors

Massimo Fascinari Telematic University UNINETTUNO (TUU) Author https://orcid.org/0009-0008-6587-5664
Vincent English Telematic University UNINETTUNO (TUU) Author

DOI:

https://doi.org/10.71204/qeqqvj92

Keywords:

Strategic Technology Management, Artificial Intelligence, Human-AI Collaboration, Resource-Based View, Technology Roadmapping

Abstract

This paper investigates the effective integration of artificial intelligence (AI) into Strategic Technology Management (STM) practices to enhance the strategic alignment and effectiveness of technology investments. The study aims to understand how AI fundamentally transforms STM under conditions of uncertainty and what organizational prerequisites are necessary for successful adoption. A mixed-methods approach was employed, combining quantitative analysis of survey data (n=230) with qualitative insights derived from expert interviews (n=14). This methodology addressed three critical research questions: the success factors AI introduces for STM roadmap formulation, the resources and capabilities required for AI-enhanced STM, and the optimal design principles for human-AI interaction in complex STM tasks. The findings demonstrate that AI transforms STM by enabling data-driven strategic alignment and continuous adaptation, with success depending upon cultivating proprietary data ecosystems, specialized human talent, and robust governance capabilities. The research synthesizes these elements into the AI-based Strategic Technology Management (AIbSTM) conceptual framework, structured across strategic alignment, resource-based view, and human-AI interaction layers. The research concludes that the most viable integration trajectory is human-centric augmentation, where AI serves as a collaborative partner to human judgment rather than an autonomous replacement. This work extends the Resource-Based View to AI contexts and offers a prescriptive framework for practitioners navigating AI integration in strategic technology management.

Author Biography

Massimo Fascinari, Telematic University UNINETTUNO (TUU)

Distinguished technology leader and academic with over 30 years of IT industry experience. I specialize in building, implementing, and strategizing advanced technology to digitally transform customers across diverse global industries. My expertise bridges the gap between complex technical architectures—including Generative AI, Data Mesh, and Cloud—and high-level business strategy. Having recently completed doctoral research on the intersection of AI and strategic management, I am focused on translating practitioner insights into academic excellence.

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