Artificial Intelligence Strategic Lifecycle: A Literature Review-Based Framework

Artificial intelligence and business model: a bibliometric analysis of strategic and entrepreneurial trends

Orchestrating AI-based circular business models through platform ecosystems: Review, framework, and future directions.

The findings show that AI-CBM transitions unfold through recursive cycles of experimentation, validation, and recalibration, as developments in one dimension expose misalignments in data maturity, governance arrangements, and ecosystem coordination.

Artificial intelligence and the reconfiguration of competency management systems in organizations

Artificial intelligence (AI) is reshaping work and human resource management, yet existing reviews largely treat competencies as secondary outcomes of AI adoption and offer limited theory-driven integration of how competency management itself is transforming. This study addresses that gap by systematically examining how competency management has evolved in AI-enabled contexts, how dominant theories explain AI-driven competency change, and where those theories require extension. Using a PRISMA 2020-guided systematic review of 187 Scopus-indexed journal articles, this study combines bibliometric mapping (keyword co-occurrence, temporal overlay, and bibliographic coupling) with directed qualitative content analysis to link research fronts with underlying theoretical mechanisms. The findings show that AI-related competency change extends beyond technical skills toward hybrid and portfolio-based configurations that integrate technical understanding, managerial judgment, learning agility, governance capabilities, and psychological readiness. The analysis demonstrates that no single framework sufficiently explains these shifts. Human Capital Theory, the Resource-Based View, and Dynamic Capabilities each illuminate partial mechanisms, while complementary perspectives from HRD, socio-technical systems, organizational economics, and psychology are needed to account for task contingency, human-AI complementarity, structural redesign, and employee readiness. The study contributes a theory synthesis that re-conceptualizes competency management as a dynamic, multi-level, and socio-technical system. It offers implications for designing adaptive competency architectures, aligning HRD interventions with AI-enabled work systems, and embedding governance capabilities within workforce development strategies.

Does AI strengthen business leadership decision making: literature review

AI does not replace the role of executive leaders; instead, it serves as a cognitive aid that frees up a leader's capacity from routine operational tasks, yet it still requires the contextual intuition and ethical governance of human leaders.

Artificial Intelligence-Aided Strategic Information System Tools for Competitive Position in the Market: A Systematic Review

This systematic review examines the role of Artificial Intelligence (AI)-aided Strategic Information System (SIS) tools in enhancing organizational performance and competitive position in modern business environments. Organizations increasingly adopt AI-enabled technologies such as Business Intelligence (BI), Decision Support Systems (DSS), Customer Relationship Management (CRM) systems, predictive analytics, machine learning, and Generative AI to improve strategic decision-making, operational efficiency, innovation capability, and market responsiveness. However, existing studies are largely fragmented across industries, organizational contexts, and individual AI applications, with limited systematic evidence synthesizing how AI-aided Strategic Information System tools collectively influence organizational performance and sustainable competitive advantage. This knowledge gap limits a comprehensive understanding of their strategic value in modern business environments. Following PRISMA guidelines, a systematic search was conducted across Scopus, ScienceDirect, and Google Scholar for studies published between 2017 and 2026. After rigorous screening and eligibility assessment, 22 studies were included in the final synthesis. The extracted data were analyzed thematically to identify patterns, opportunities, and challenges associated with AI-enabled Strategic Information Systems. The findings indicate that AI-aided SIS tools significantly enhance organizational competitiveness by enabling data-driven decision-making, improving customer intelligence, optimizing supply chain performance, and strengthening strategic agility. AI-powered CRM and predictive analytics systems were found to improve marketing effectiveness and customer engagement, while BI and DSS tools enhance managerial decision speed and accuracy. Additionally, Generative AI and large language models are emerging as transformative tools for market intelligence and strategic insight generation. Despite these benefits, challenges such as data privacy concerns, algorithmic bias, ethical risks, workforce skill gaps, organizational resistance, and high implementation costs persist. The review concludes that successful AI adoption depends on organizational readiness, leadership commitment, technological infrastructure, and governance frameworks. Overall, AI-enabled Strategic Information Systems are reshaping competitive dynamics by enabling adaptive, intelligent, and sustainable organizational decision-making.

An AI-Based Personalized Learning Framework for Corporate Employee Development: An Integrative Literature Synthesis

Background: Accelerating digital transformation, encompassing widespread business process automation and the adoption of artificial intelligence, has widened the competency gap between current workforce capabilities and future organizational demands. This condition positions the corporate Learning and Development (L&D) function as a strategic pillar for sustaining competitive advantage and simultaneously heightens the urgency of integrating AI into corporate learning systems. Purpose: Aims. This study synthesized, through an integrative approach, empirical and conceptual literature on AI-based personalized learning frameworks for corporate employee development to produce a coherent conceptual framework. Method: A Systematic Literature Review (SLR) design was employed, utilizing qualitative meta-synthesis guided by the PRISMA 2020 protocol. Research questions were formulated using the SPIDER framework. Systematic searches were conducted across five major academic databases, Scopus, Web of Science, ERIC, IEEE Xplore, and Google Scholar, covering publications from 2020 to 2025. Results: From 1,847 initially identified articles, 1,203 unique records remained after deduplication. Title and abstract screening yielded 312 articles, and full-text screening produced a final synthesis corpus of 47 articles. Findings confirm that AI-driven personalized learning systems have a significant capacity to address workforce competency gaps arising from digital transformation. Conclusion: This study produced a comprehensive AI-based personalized learning framework by integrating perspectives from educational technology, human resource management, and artificial intelligence. Implementation. Organizations are advised to adopt this framework as a strategic response to the imperatives of reskilling and upskilling in the digital transformation era.