It is concluded that value creation through artificial intelligence depends on the alignment of technological, organizational, and human capabilities, as well as on governance strategies that support responsible, ethical, and sustainable implementation within organizations.
Artificial intelligence (AI) has emerged as an important strategic resource in the contemporary business environment, influencing organizational competitiveness, innovation, and decision-making across multiple industries. This systematic literature review examines how the existing literature positions AI in the development of business strategies across the manufacturing, financial, retail, e-commerce, education, agriculture, and technology sectors. Following the PRISMA 2020 guidelines, studies published between 2015 and 2025 were identified through Scopus, Web of Science, ScienceDirect, and Google Scholar, screened using predefined inclusion and exclusion criteria, and evaluated based on publication year, document type, language, research themes, empirical contribution, theoretical value, and journal impact factor. Of the records retrieved, 20 articles met the eligibility criteria and were included in the final review. The findings indicate that most studies relied on secondary data sources, including patent databases, bibliometric records, company datasets, and published literature, whereas relatively few employed primary data collected through surveys, interviews, or organizational respondents. The review also found that much of the literature adopts a global or non-region-specific perspective, with limited empirical evidence from specific regional contexts. Across the reviewed studies, machine learning, AI-based computational modeling, text mining, natural language processing, and bibliometric analysis were the most frequently used methodological approaches. The literature also consistently identifies implementation challenges, ethical considerations, governance issues, and organizational readiness as critical factors influencing AI adoption. Overall, the reviewed literature positions AI as a strategic resource that can enhance business performance and competitiveness when supported by appropriate governance, organizational capabilities, and context-specific implementation.
L. M. De La Fuente, F. Yambao· Journal of Interdisciplinary...· 0 citations
Purpose – This systematic review examines how artificial intelligence (AI) can support strategic decision-making in private universities, the organizational conditions shaping its value, and the governance and implementation risks that constrain responsible use.Methodology – Searches were conducted in the Web of Science Core Collection, Scopus, and Google Scholar between March and May 2026, with the final update on May 31, 2026. After duplicate removal, screening, full-text assessment, and evidence appraisal, 46 substantive sources published between 1955 and 2025 were included in this review. Four additional methodological references supported the review reporting and synthesis. Because the evidence base was heterogeneous, narrative thematic synthesis was applied while distinguishing direct private university evidence from evidence transferred from general higher education, organizational decision research, and AI governance.Findings – The synthesis identifies four interconnected roles of AI: environmental intelligence, decision augmentation, strategic execution, and governance infrastructure. AI can strengthen institutional sensing, the comparison of strategic alternatives, implementation coordination, and decision traceability. However, direct empirical evidence specific to private universities is limited. Strategic value depends on data quality, organizational learning, analytical capability, decision ownership, auditability, governance capacity, strategic fit, and alignment with the institutional mission. Therefore, AI is best understood as a human-led decision-support capability rather than a substitute for institutional judgment.Research limitations – The heterogeneous corpus prevents statistical estimation of a common institutional effect, while the review is restricted to English-language sources from three search platforms. Therefore, the four-part architecture should be treated as an evidence-organizing framework rather than a validated causal model.Originality – This review integrates higher education, organizational decision-making, strategic management, and AI governance evidence into an institution-level capability architecture for responsible AI-supported strategic decision-making.
Yun-Dong Wu, Wei-Jian Kong· Artificial Intelligence in E...· 0 citations
This study aims to analyze the role of Artificial Intelligence (AI) in driving digital business transformation through a systematic literature review approach. Digital business transformation has become increasingly essential for organizations seeking to maintain competitiveness in the era of Industry 4.0 and the emerging Industry 5.0 paradigm. As a core component of technological advancement, AI provides advanced capabilities in data processing, process automation, predictive analysis, and the generation of valuable business insights. Through an analysis of 25 selected scientific publications from reputable academic databases, this study identifies four main dimensions of AI’s role in digital business transformation: (1) intelligent business process automation, (2) data-driven personalization of customer experiences, (3) supply chain and operational optimization, and (4) AI-based strategic decision-making. The findings indicate that organizations successfully integrating AI into their core business strategies experience improvements in operational efficiency, enhanced customer satisfaction, and increased business performance. However, challenges such as digital talent shortages, implementation costs, data privacy concerns, ethical considerations, and resistance to organizational change remain significant barriers. This study contributes to a comprehensive understanding of how AI is reshaping the contemporary digital business landscape and provides practical insights for business leaders in developing effective digital transformation strategies.
Ilham Karunia Akbar, Rafly Verdyansyah Pratama, Ahmad Rifa’i et al.· Devotion : Journal of Resear...· 0 citations
The empirical and conceptual research on artificial intelligence (AI) adoption in small and medium-sized enterprises (SMEs) that was published between 2019 and 2025 is compiled in this systematic literature review. This evaluation incorporates information from three main research domains: AI applications in business operations, adoption enablers and barriers, and organizational performance results. It is based on fifty peer-reviewed articles that were found through searches of Scopus, Web of Science, and IEEE Xplore. Results show that in order to improve customer service, marketing automation, and operational efficiency, SMEs primarily use machine learning, natural language processing, and chatbot technologies. While financial limitations, a lack of skills, and insufficient digital infrastructure continue to be obstacles, top management support, technical preparedness, and staff capability emerge as crucial organizational facilitators. Although adoption rates are still concentrated among larger organizations, AI adoption is correlated with quantifiable increases in operational efficiency, cost reduction, customer retention, and innovation performance. There are clear regional differences, with rising nations in Latin America and Asia spearheading adoption despite more limited resources. Significant research gaps about long-term sustainability, contextual differences among developing economies, ethical governance frameworks, and the efficacy of support interventions are identified by the review. To promote inclusive, responsible AI integration in SMEs, our findings highlight the necessity of customized implementation strategies, focused capacity-building initiatives, encouraging legislative frameworks, and multi-stakeholder cooperation. For practitioners, governments, and researchers looking to close the gap in AI adoption and uncover revolutionary potential for small businesses worldwide, this paper offers practical ideas.
E. S. Hamid, Bhenu Artha· Archives of Business Researc...· 1 citation
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 study provides a systematic review of the academic sources to explore the role of the combination of AI, automation, and data-driven strategies in supporting the metamorphosis of conventional businesses and enhancing their operational efficiency, organizational agility, and long-term competitive advantage.
Gurpreet Kaur· The American Journal of Mana...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
