Jul 2026· Research technology management· Vol 69, pp. 13 - 27· 0 citations· 67 references
TL;DR
A three-dimensional framework for managing GenAI in organizations is developed based on a longitudinal and collaborative study based on five multi-industry workshops conducted over one year, offering actionable insights for managers navigating the integration of GenAI technologies.
Abstract
Abstract OVERVIEW: This article explores how “Taylorism,” the historical management approach created by Frederik Winslow Taylor, can be adapted to tackle contemporary management challenges posed by generative artificial intelligence (GenAI). Drawing on a longitudinal and collaborative study based on five multi-industry workshops conducted over one year, we developed a three-dimensional framework for managing GenAI in organizations. This framework offers a three-part action plan for practitioners: (1) establish dedicated GenAI labs to orchestrate experimentation and learning; (2) explore GenAI in a structured, capability-driven manner; and (3) redefine organizational roles and workflows to support generativity. Grounded in historical parallels with Taylor’s scientific management principles, our findings empirically extend existing GenAI management frameworks by revealing how organizations operationalize them. We discuss implications for practice and suggest avenues for future research at the intersection of GenAI, technology management, and organizational innovation. We present actionable insights for managers navigating the integration of GenAI technologies, emphasizing collective actions and cross-functional collaboration. PRACTITIONER TAKEAWAYS Build before you scale: Establish a dedicated GenAI laboratory to coordinate experimentation before scaling use cases across business units. Start with capabilities, not problems: Explore GenAI use cases by mapping the technology’s capabilities to your organizational activities. Slow down to go fast: Invest in reflective functions such as evaluation and governance roles. These may initially seem burdensome, but they are essential to preventing GenAI initiatives from becoming the next “AI winter.”
Research on algorithmic management is expanding rapidly but remains highly fragmented, lacking a systematized understanding of how cross-disciplinary debates address its real-world implementation barriers. To address this gap, this study conducts a comprehensive bibliometric analysis of 340 peer-reviewed journal articles (2015–2025), explicitly adopting a blended review approach that integrates phenomenon- and field-focused strands. Moving beyond descriptive science mapping, we elevate thematic trajectories into causal narratives, demonstrating how the academic discourse has fundamentally matured from isolated technical inquiry into a systemic socio-ethical critique driven by macro-forces such as generative AI and modern labor regulations. By unpacking the field’s most influential literature, we conceptualize the status quo across four core socio-technical tensions: coordination efficiency versus psychological well-being, algorithmic control versus worker resistance, algorithmic opacity versus structural governance, and the transition from platform labor to mainstream corporate human resource management. Based on this synthesis, we propose an actionable, multi-stakeholder future research agenda and outline practical governance interventions, offering an essential roadmap for researchers, practitioners, and policymakers navigating the algorithmic workplace.
Arne Jeppe, Tim Brée, Erik Karger et al.· Management Review Quarterly· 0 citations
The use of artificial intelligence (AI) in qualitative management research has so far been framed predominantly as assistance: a researcher interacting with generative AI during the design or analysis of a case study. Yet methodological discussions still offer limited understanding of how qualitatively different forms of AI involvement reconfigure the production of theoretical insight in case-based research. The existing differentiation remains stage-bound and tool-centred; what is missing is a systematic coupling of AI roles to forms of theorising and to specific validity threats. Drawing on a review of 139 documents, the authors develop a four-role conceptual framework, AI as Discoverer, Selector, Co-Constructor and Generator. Each role is examined through affordance theory, theorising from cases and sociomaterial perspectives in order to analyse how different forms of AI involvement influence theorising, interpretation and engagement with empirical material. The study's contribution lies less in naming four roles than in coupling them to forms of theorising and to role-specific validity threats. AI involvement cannot be treated as a uniform methodological condition: different roles raise different implications for contextual understanding, methodological rigor, researcher oversight and the empirical status of research material. We advance six propositions linking each AI role to conditions of legitimacy, characteristic validity threats and disclosure requirements, and we illustrate the framework through a corporate-governance research scenario. The paper closes with role-sensitive guidance for authors, reviewers and editors of governance and R&D case research.
Rosario Faraci, Rosaria Ferlito· CORPORATE GOVERNANCE AND RES...· 0 citations
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.
Rhythm Mittal, Kunal Saxena· Journal of Emerging Technolo...· 0 citations
A strategic approach to use of GenAI, including the support of top management, incorporation of the principles of a “learning organization,” consultation of key stakeholders and appropriate choice of initial content development, will enhance the likelihood of success.
R. Hamilton, W. Sodeman· Development and Learning in...· 0 citations
The first comprehensive synthesis linking AI technical capabilities with anticipatory governance theory is provided, offering a theoretically grounded, actionable pathway for practitioners navigating the digital transformation of project delivery, shifting the focus from controlling machines to cultivating symbiotic ecosystems.
Ken A. Charles· International Research Journ...· 0 citations
Artificial intelligence (AI) is changing how educational institutions interact with donors, alumni and stakeholders. While early applications focused on automating routine tasks, the emergence of generative AI (GenAI) and large language models (LLMs) has introduced a new paradigm: AI as a strategic thought partner. This paper explores how GenAI can enhance leadership, creativity and decision making within university advancement, drawing on personal experience during a large-scale university merger and the applied use of multiple LLMs — including Microsoft Copilot, Claude and ChatGPT — as strategic tools. It presents principles of effective prompt design and use of ‘personas’ that enable advancement professionals to collaborate productively with AI and proposes a values-led advancement AI governance framework to support ethical and responsible use. The discussion highlights both opportunities and risks — emphasising the importance of human review, data privacy and the need for advancement-specific guidelines. The paper concludes that GenAI, when implemented with clear guardrails and institutional integrity, has the potential to elevate advancement from an operational function to a strategic, mission-aligned partner in shaping the future of higher education. This paper is also included in The Business & Management Collection, which can be accessed at https://hstalks.com/business/.
Liz Hawkins· Journal of Education Advance...· 0 citations
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