Aug 2026· International Journal of Education and Humanities· 0 citations· 39 references
TL;DR
A comprehensive research framework for AI capabilities is integrated and constructs, clarifies the field’s consensus and research gaps, and not only enriches the theoretical research landscape in the field of AI capabilities but also provides practical guidance for organizations of various types to systematically build AI capabilities and achieve the transformation of technological value.
Abstract
Against the backdrop of the deepening development of the digital economy and the ongoing integration of the digital and physical worlds, artificial intelligence has evolved from a single-function technical tool into a core strategic capability that supports organizations in building long-term competitive advantages. As a core construct that explains differences in the value transformation of AI technology, AI capability has become a hot topic of research in the fields of strategic management and information systems. However, existing research is scattered across diverse disciplinary perspectives and application scenarios, and has yet to form a unified research framework or theoretical system. Based on 33 core publications in the field of AI capabilities, this paper conducts a systematic review following the logical framework of “conceptual evolution-theoretical foundations-application scenarios-research outlook.” The study traces the evolutionary path of AI capabilities, from their origins in IT capability research to the development of a general three-dimensional construct, and further expansion into specialized technological forms and specific application scenarios; it synthesizes a theoretical framework centered on the resource-based view and dynamic capabilities theory, complemented by multiple theoretical perspectives; and summarizes the application progress and heterogeneity in value realization of AI capabilities across eight major scenarios, including green innovation, supply chain management, public governance, and business model innovation; Finally, it identifies limitations in existing research regarding research design, theoretical perspectives, scenario coverage, and risk governance, and proposes future research directions. This paper integrates and constructs a comprehensive research framework for AI capabilities, clarifies the field’s consensus and research gaps, and not only enriches the theoretical research landscape in the field of AI capabilities but also provides practical guidance for organizations of various types to systematically build AI capabilities and achieve the transformation of technological value.
The study concludes that the integration of AI and strategic emerging industries is essentially a synergistic evolutionary process driven by the triple logics of technology, industry, and ecology, and needs to be advanced systematically from multiple dimensions such as top-level design, technology inclusiveness, and go...
Cai-Feng Zhang· Academic Journal of Business...· 0 citations
The research aims to uncover the depth of AI integration across different sectors and assess its potential impact on the country's economic development prospects, as well as identify the current baseline of AI application.
Շողեր Պյոտրի Պողոսյան· Սոցիալ-տնտեսական զարգացման ա...· 0 citations
The development of the Innovative Ecosystem (IE) presents a new paradigm for economic integration, collaborative advancement, and shared achievements. The rise of Artificial Intelligence (AI) has significantly accelerated the global processes of digitization, informatization, and intelligence. Exploring how AI can leve...
This study aims to identify the key challenges and development prospects of AI implementation to enhance business efficiency and ensure sustainable development, providing strategic guidance for organizations to balance technological innovation with responsibility and risk management.
Giedrius Čyras, Vita Marytė Janušauskienė· International Scientific Con...· 1 citation
This conceptual study extends the Digital Resource View by theorizing AI as a configured, multi-layered digital resource architecture composed of AI infrastructure, AI capability, AI-driven decision systems, and AI-orchestrated digital platforms, and identifies boundary conditions for platform dependence, institutional...
AI Agents are driving the transformation of sci-tech intelligence analysis from "human-in-the-loop" to "human-on-the-loop," enabling intelligent and pipeline-based intelligence production processes.
Hangqi Yang· World Journal of Information...· 0 citations
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