The Empowering Mechanisms and Challenges of Generative AI for Business Management Innovation: An Integrative Conceptual Review

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

Innovation Management in the Age of Generative AI: Strategic Challenges and Opportunities for Business Leaders

Generative artificial intelligence (GenAI) is a transformative shift in how organizations can navigate innovation processes and stay competitive in dynamic markets. This paper explores the synergy between innovation management theory and the capabilities of generative AI, offering a holistic perspective of its strategic challenges and opportunities to business leaders. Based on the concepts of dynamic capabilities theory, open innovation and recent empirical studies on the adoption of GenAI, we propose an integrated theoretical framework that conceptualizes GenAI as an enabler and disruptor of the existing innovation processes. Three specific areas of strategic challenges emerge from our analysis: organizational and cultural barriers, such as adaptation and resistance to change by the workforce; technical and governance issues, such as data quality and risk of AI hallucination; and ethical and regulatory issues, such as IP and algorithmic bias. At the same time, we pose three key strategic questions: how can innovation cycles be accelerated with automated ideation and prototyping? How can innovation be made accessible for everyone, by democratizing the process? And how can new business models emerge with content generated by AI? Finally, the paper offers practical managerial suggestions and a research agenda for scholars. The present work is a theoretically informed, but practically relevant, study at the intersection of artificial intelligence and strategic management, providing invaluable guidance for the innovation landscape in the era of GenAI.

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.

Generative Artificial Intelligence in Business Decision-Making: Emerging Frameworks, Applications, and Future Challenges

It is concluded that no single framework adequately explains GenAI's enterprise decision-making effects across all analytical levels, and that individual-, organizational-, and task-level frameworks must be combined rather than treated as competing explanations, identifying multi-level theoretical integration as the central future research prospect.

The polyhedric role of artificial intelligence in value creation processes of SMEs: A literature review

This study offers a literature review on how artificial intelligence (AI) creates value in SMEs by distinguishing three roles along the value chain, enabler, enhancer and transformer and integrating the Dynamic Capabilities perspective with an analysis of managerial mechanisms and ethical/regulatory tensions. A systematic literature review was applied to a dataset of 84 articles drawn from the Web of Science database with an extensive string of keywords related to AI and SMEs. The findings provide a critical assessment of (1) theoretical underpinnings, (2) methodological aspects and (3) empirical evidence on AI and SMEs, by highlighting the different roles of AI (enabler, enhancer and transformer) within the different stages of the SMEs value chain, as well as the dark side (bias, opacities, lock-ins) associated with AI. This study is the first to make systematic efforts to take stock of existing conceptualizations and empirical findings concerning the different roles that AI can play in supporting SME processes, as an enabler of digital infrastructures and data ecosystems, an enhancer of managerial decision-making and operational efficiency or a transformer of business models and value creation mechanisms. In doing so, it offers a coherent overview of the state of the art and helps to identify cumulative insights or gaps for future research.

ARTIFICIAL INTELLIGENCE-AUGMENTED OPEN INNOVATION AND KNOWLEDGE MANAGEMENT: AN EXTENSION OF THE INTEGRATED OIKM FRAMEWORK FOR TELECOMMUNICATION ORGANIZATIONS

This paper extends the author's Integrated Open Innovation and Knowledge Management (OIKM) framework, originally developed for a telecommunications operator, to account for generative and agentic artificial intelligence (AI), which the original model predates. Literature published between 2024 and 2026 on generative AI's role in knowledge management, absorptive capacity and intellectual capital is reviewed and used to formulate five theoretical propositions that recast AI as a boundary condition strengthening or conditioning the OIKM framework's existing mediating and moderating relationships, and, in one case, contributing a new direct effect on the knowledge management process itself. A mixed-methods design combining qualitative case analysis with survey-based PLS-SEM is proposed for future empirical validation. The paper offers an AI-Augmented OIKM (AI-OIKM) model comprising five propositions: AI-mediated open innovation, generative-AI-mediated knowledge flow, AI-enabled absorptive capacity, and AI/digital intellectual capital, each extending an original construct without altering its identity, plus a fifth proposition on generative AI's direct effect on the knowledge management process. As a conceptual paper, the propositions are not yet empirically tested; the proposed mixed-methods design offers a research agenda for future validation across telecommunication contexts. The paper builds a theory-driven connection between the knowledge-based view of the firm and the emerging literature on organizational AI capability, extending a previously validated framework while preserving comparability with the original study. The paper offers telecommunications executives a conceptual basis for integrating AI into innovation and knowledge processes as a core operating capability rather than a bolt-on efficiency tool.