Implementing agentic AI in project management: an evidence-based framework and maturity model from systematic analysis

Knowledge Documentation Framework for AI Initiatives: Development and Validation Across Organizational AI Contexts

The study contributed a validated lifecycle-integrated KD framework for AI initiatives; a taxonomy of ten systematically identified gaps in current AI KD practices; and a methodological demonstration of mixed-method CVI validation for framework development in information systems research.

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.

Artificial Intelligence Strategic Lifecycle: A Literature Review-Based Framework

By integrating five theoretical perspectives, the review develops a model of the AI strategic lifecycle, offering both a consolidated foundation for future research and a forwardlooking agenda for managers seeking to leverage AI as a strategic asset.

Artificial Intelligence for Strategic Workforce Planning: A Systematic Review of Organizational Adaptation Mechanisms

Artificial Intelligence’s Transformative Impact on Management, Strategy, and Workplace Dynamics: A Systematic Literature Review

The integration of artificial intelligence (AI) into organizations is reshaping management practice, competitive strategy, and the experience of work, yet scholarship remains fragmented across disciplines and is predominantly Western-centric. This systematic literature review synthesizes interdisciplinary evidence on how AI transforms leadership and management functions, drives business model innovation, and alters workplace dynamics, with particular attention to Southeast Asian and Philippine contexts. Peer-reviewed, English-language journal articles published between 2016 and 2025 addressing the organizational, strategic, or workforce implications of AI were included; purely technical studies and non-peer-reviewed materials were excluded from the corpus, with institutional reports used only for contextualization. Scopus, Web of Science, ABI/INFORM, Business Source Complete, and the ACM Digital Library were searched. Screening followed the PRISMA 2020 guidelines: 1,245 records were identified, and 128 studies met all inclusion criteria and were synthesized thematically. First, AI integration facilitates an augmented leadership model in which effective managers combine human judgment with algorithmic capability. Second, AI operates as a strategic capability that enables adaptive, data-centric business models. Third, workplace dynamics exhibit a bifurcation effect whereby AI augments some roles while displacing or deskilling others. The review contributes an integrative tripartite framework connecting leadership, strategy, and workforce transformation, and identifies priorities for research and policy in emerging economies. No protocol was registered.

Incorporating Artificial Intelligence in Small Businesses: A Practitioner-Oriented Literature Review (2020–2025)

Artificial intelligence (AI) adoption among small businesses has accelerated since 2020, but success has not been consistent among users. A few constraints recur: limited skills, weak data readiness, and thin governance capacity. This practitioner-oriented narrative review draws together peer-reviewed, policy, and practitioner evidence published between 2020 and 2025 to examine how small firms are adopting AI, where the value lies, and which barriers persist. The evidence base comprises 20 sources—academic studies, institutional and policy reports, and practitioner-oriented material—organized thematically and supplemented by anonymized illustrative observations from the author’s professional experience (the Method section provides the selection criteria and source profile). Small firms most often use AI for customer engagement, marketing content creation, and administrative automation, and they report productivity gains when tools are paired with clear workflows and human oversight. Beyond that, outcomes vary, reflecting differences in readiness and complementary assets. Limited skills and weak data readiness are the barriers reported most consistently. This review distinguishes AI from related technologies, maps adoption across functional areas, and consolidates the evidence into an incremental adoption framework: thin-slice use cases, minimum viable governance, hybrid skill development, and sequenced investment. The contribution is translational; it connects what the research reports to what a resource-constrained firm can act on.