Guest Editors
Alsones Balestrin, Fundação Dom Cabral, Brazil
Alberto Luiz Albertin, Fundação Getulio Vargas - EAESP, Brazil
Roberto Bernardes, Centro Universitário FEI, Brazil
Cristian Foguesatto, Universidade Federal do Rio Grande do Sul, Brazil
Luis Hernan Contreras Pinochet, Universidade de São Paulo (FEAUSP), Brazil
Timeline
Special Issue launch: June 29, 2026
Full paper submission deadline: January 22, 2027
First review decision: May 31, 2027
Second round revisions due by: September 18, 2027
Final decision: November 6, 2027
BACKGROUND AND IMPORTANCE OF THE THEME
Artificial Intelligence (AI), particularly Machine Learning (ML), Large Language Models (LLMs), and generative AI, has emerged as one of the most transformative forces in contemporary business and management (Fountaine et al., 2019). AI refers to computer systems that perceive their environment, learn from past behavior, and take action to maximize the likelihood of attaining their goals, fundamentally revolutionizing data-driven decision-making across industries (Davenport et al., 2020; Doshi et al., 2025). The recent emergence of generative AI systems such as ChatGPT, Gemini, and other LLMs has intensified debates across boardrooms and academic forums about how these technologies align with digital strategies and reshape competitive landscapes (Brown et al., 2024; Kaplan & Haenlein, 2019).
Beyond generative AI, the emergence of agentic AI, systems capable of autonomously planning multi-step actions, using external tools, coordinating with other AI agents, and pursuing complex goals with minimal human intervention, represents a new strategic frontier with profound organizational implications (Baird & Maruping, 2021; Wang et al., 2024). Agentic AI systems, including autonomous AI agents and multi-agent frameworks, are increasingly deployed to automate decision chains, orchestrate workflows, and interact with digital environments at scale, fundamentally challenging traditional notions of organizational agency, authority, and accountability (Xi et al., 2023). This evolution requires organizations to reconsider governance architectures, human oversight mechanisms, and strategic competencies as AI transitions from a reactive tool of assistance to a proactive, semi-autonomous actor embedded in core business processes (Dwivedi et al., 2025; Hughes et al., 2025; Kshetri et al., 2024; Mariani & Dwivedi, 2024).
Knowledge Management (KM) has become a critical determinant of organizational success in the digital era, focusing on the strategic use of learning mechanisms, knowledge assets, and flows to drive innovation and sustainable value creation. AI plays a pivotal role in knowledge-based value creation, enabling firms to improve innovation, optimize decision-making, and strengthen competitive advantage. Research indicates that firms combining Intellectual Capital (IC) and KM strategies, especially in conjunction with AI capabilities, are better equipped for digital transformation and sustainable competitive performance (Haefner et al., 2021).
AI's widespread application has brought about a paradigm shift in strategic management and decision-making (Krakowski et al., 2023; Raisch & Krakowski, 2021). AI's capacity to analyze large datasets and predict trends is revolutionizing strategic planning, steering companies toward more data-driven decisions. Concurrently, AI is driving transformation in leadership and managerial approaches, with decision-making processes becoming increasingly data-dependent, necessitating leaders to enhance their comprehension of AI's potential and broader implications (Iansiti & Lakhani, 2020; Sarwar et al., 2023).
However, AI also raises critical ethical, legal, and societal concerns—particularly in high-stakes settings such as healthcare, education, crisis response, and social services (Budhwar et al., 2023; Kellogg et al., 2020). The absence of a humanistic perspective can result in interactions that feel mechanistic and unresponsive to the complexities of individual circumstances, potentially propagating or amplifying existing societal biases (Martin, 2019). Moreover, in an era where algorithmic systems spread misinformation at scale, organizations manipulate knowledge to serve strategic interests, and political or sociocultural polarization undermines consensus on fundamental facts, the concept of "truth" is no longer neutral or self-evident (Knight & Tsoukas, 2019).
EVOLUTION AND IMPACT ON BUSINESS AND ORGANIZATIONS
Ethics, Governance, and Responsibility in AI Systems
The deployment of AI in organizational contexts raises profound ethical, governance, and social responsibility challenges (Martin, 2019). As AI systems increasingly mediate critical organizational decisions, fundamental questions emerge about managing conflicting truths within AI-augmented environments, particularly when generative systems produce plausible but potentially inaccurate information (Knight & Tsoukas, 2019). The absence of humanistic perspectives can result in mechanistic interactions that propagate societal biases and reshape organizational power structures, labor relations, and inequalities (Kellogg et al., 2020).
Organizations must implement "human-in-the-loop" decision-making approaches combining algorithmic efficiency with human judgment and ethical reasoning (Raisch & Fomina, 2025). This demands governance mechanisms ensuring transparency and accountability, regular algorithmic audits to identify bias, and robust processes for validating AI outputs. Ultimately, fostering organizational cultures that value ethical reflection alongside operational efficiency becomes essential for responsible AI deployment.
Organizational Dynamics and Work Transformation
AI's influence extends beyond technological integration, impacting every aspect of business operations and organizational frameworks (Leonardi & Treem, 2020). AI presents opportunities for streamlining efficiency, innovating products and services, and securing competitive advantage through automation of routine tasks and enhanced decision-making accuracy through predictive analytics (Brynjolfsson et al., 2018). In customer service, AI-driven chatbots and virtual assistants are notable implementations (Huang & Rust, 2018), while in human resources, AI plays crucial roles in talent acquisition and management processes (Raisch & Krakowski, 2021).
AI impacts how firms organize labor and tasks, potentially resulting in different organizational structures at macro and micro levels (Bechky & Davis, 2025). Machine learning algorithms have been found to produce outcomes that can be systematically biased regarding gender, ethnicity, and other factors (Crăiuț & Iancu, 2022; Nadeem et al., 2022). "Good governance" of AI may demand scrutiny of hidden prejudices through frequent testing of algorithms and careful examination of current applications (Kaplan & Haenlein, 2019; Valle-Cruz et al., 2024).
Human-AI Collaboration and the Future of Work
The growing presence of AI technologies is disrupting social relationships and interactions in the workplace (Kim et al., 2024). While AI has increased efficiency, personalization, and cost reduction in many settings, it has also raised concerns about how human-to-human interactions are increasingly shaped, mediated, and sometimes replaced by these technologies (Einola & Khoreva, 2023). The challenge for organizations is to foster effective human-AI collaboration and address potential adverse effects of AI-driven decisions on organizational learning and individual career trajectories (De Stefano, 2016; Raisch & Fomina, 2025).
Questions of humanness—those qualities, attributes, and practices that characterize what it means to be human, including human dignity—are being systematically challenged as work designs and workflows are redefined by AI integration. From a sociological perspective, the expression or enhancement of different facets of humanness is shaped by social structures, cultural norms, and interpersonal dynamics, all of which are being transformed by AI (Orlikowski & Scott, 2008).
Strategic and Competitive Implications
AI plays a significant role in shaping how firms compete, enabling new business models and avenues for value creation that exploit the ever-increasing availability of data (Agrawal et al., 2019). The adoption of AI technologies—including machine learning, robotics, image recognition algorithms, and natural language processing—augments decision-making processes and fosters the creation of innovative products and services (Hirschberg & Manning, 2015). However, as data becomes more widespread and less unique, achieving competitive advantage becomes more difficult, raising questions about what complementary assets are needed for firms to create and capture value from AI (Fountaine et al., 2019).
In the international business context, AI is reshaping trade theories and policy implications, with firms possessing extensive datasets generating more accurate predictions and gaining competitive edges in global markets (Olan et al., 2022). The strategic trade policy landscape is being redefined by economies of scale, knowledge externalities, and privacy regulations, which can constrain firms' innovative capabilities by limiting data accessibility (Teece, 2022).
Entrepreneurship and Innovation
AI technologies are reshaping the landscape of entrepreneurship, providing novel tools for entrepreneurs to innovate, automate, and optimize various aspects of their ventures from idea generation to scaling operations (Chalmers et al., 2020). AI can automate routine tasks, enhancing efficiency and freeing resources for innovation initiatives. The entrepreneurial landscape is changing due to AI's ability to predict trends and automate decision-making processes, requiring entrepreneurs to adapt their business models and strategies to harness AI's full potential.
Knowledge Management and Intellectual Capital
Effective KM practices enhance the acquisition, codification, and dissemination of knowledge, ensuring that Intellectual Capital is transformed into actionable business intelligence. AI-driven tools such as natural language processing and intelligent knowledge networks facilitate knowledge discovery, retrieval, and automation, thereby enhancing organizational knowledge utilization. However, AI has revolutionized knowledge governance and organizational learning by enabling predictive analytics and automated knowledge workflows, while also posing challenges related to knowledge security, ethics, and the digital divide (Kaplan & Haenlein, 2019; Arias-Pérez & Vélez-Jaramillo, 2022).
Agentic AI, Multi-Agent Systems, and Organizational Autonomy
The evolution from generative to agentic AI marks a qualitative shift in the role of artificial intelligence within organizations. Unlike generative AI systems that respond to human prompts, agentic AI systems autonomously set subgoals, plan sequences of actions, use digital tools, access external APIs, and coordinate with other AI agents to complete complex, long-horizon tasks (Wang et al., 2024). This transition introduces novel organizational dynamics: AI agents may function as quasi-autonomous actors within workflows, executing and delegating tasks in ways that progressively blur the boundary between human and machine agency (Baird & Maruping, 2021).
Multi-agent architectures enable organizations to deploy specialized AI agents that collaborate to solve problems exceeding the capacity of any single system, enabling unprecedented scalability in knowledge work automation. However, this autonomy amplifies governance challenges, raising critical questions about error propagation, unintended consequences, accountability gaps, and the appropriate scope of human oversight in agent-driven processes (Xi et al., 2023). As agentic AI systems are entrusted with progressively more consequential decisions—from supply chain orchestration to customer engagement and strategic analysis—organizations must develop new institutional competencies, oversight frameworks, and ethical guidelines to ensure autonomous AI behavior remains aligned with organizational values and societal expectations (Shrestha et al., 2019). The implications for leadership, trust, and institutional design are substantial, as firms navigate the tension between the efficiency gains offered by agentic autonomy and the accountability demands of responsible AI governance (Park et al., 2023).
CENTRAL RESEARCH QUESTIONS
This special issue invites rigorous empirical and theoretical contributions examining the multifaceted implications of AI for business and management. We welcome diverse methodological approaches—quantitative, qualitative, design science, analytical modeling, and mixed methods—that address AI's role across individual, organizational, and societal levels. Research questions include, but are not limited to:
Ethics, Governance and Responsible AI
How can organizations ensure transparency, explainability, and accountability in AI systems?
What frameworks can ensure AI advances human dignity and well-being?
How can organizations use AI to allow humans to thrive rather than being displaced?
What ethical considerations arise from AI deployment, and how can these be effectively managed?
How can AI be leveraged to advance social and environmental sustainability in organizations?
AI-Driven Innovation and Knowledge Management
How does AI contribute to organizational learning and innovation processes? • What role does AI play in enhancing product and service innovation within organizations?
How does AI enable organizations to capitalize on open innovation and collaborative ecosystems?
How can AI drive disruptive innovation in established industries?
How does AI impact knowledge sharing, knowledge hiding, and intellectual capital utilization?
Organizational Design and Transformation
How does AI enable new ways of organizing labor and tasks within organizations? • How does AI transform organizational hierarchy, authority allocation, and power relationships?
What governance mechanisms are needed to ensure AI incorporates ethical considerations while balancing efficiency?
How do organizations address the challenges of AI transformation?
What capabilities, structures, and cultural conditions determine organizational readiness for AI adoption?
Human-AI Collaboration and Workforce Implications
How can artificial and human intelligence be combined to create complementarity? • Under what conditions can AI enhance human decision-making in organizational contexts? • What is the role of AI in strategic human resource management, talent acquisition, and retention? • How does AI impact employee well-being, job satisfaction, and organizational culture? • How might the relationship between humans and AI evolve, and what are the long-term implications for organizations and society?
AI and Strategic Management
How does AI redefine competitive advantage, and what implications does this have for strategic planning and execution?
What is the role of AI in shaping business ecosystems?
How do strategies of AI-adopting and non-adopting firms differ? • How does AI enable new business models and influence positioning and differentiation?
How are AI-enabled digital platforms redefining firm boundaries, value creation, and competitive dynamics within business ecosystems?
What does AI sovereignty mean for organizations, nations, and regions, and why is it strategically significant?
Entrepreneurship in the Age of AI
How does AI empower entrepreneurs in identifying and capitalizing on new market opportunities?
What are entrepreneurial strategies based on AI, and how can AI identify business opportunities for new ventures?
What are the challenges and opportunities for entrepreneurs in building AI-driven business models?
Agentic AI and Autonomous Systems
How should organizations define accountability and liability frameworks when agentic AI systems autonomously execute decisions with material organizational and societal consequences?
What governance mechanisms and human-in-the-loop architectures are needed to maintain effective oversight of agentic AI systems operating within complex organizational workflows?
How do multi-agent AI architectures reshape organizational hierarchies, decision rights, and task delegation when AI agents can autonomously coordinate across departments and organizational boundaries?
What new organizational roles, structures, and dynamic capabilities emerge as firms transition from using generative AI to deploying agentic AI systems at scale?
How does collaboration with agentic AI systems affect employee identity, trust, psychological safety, and perceptions of meaningful work and human dignity?
What are the strategic implications of agentic AI for competitive advantage when AI agents can autonomously learn, adapt, and potentially replicate or counter rival strategies in real time?
How can entrepreneurs leverage agentic AI and multi-agent systems to create scalable ventures with minimal human capital, and what new regulatory or ethical barriers shape their adoption?
SCOPE AND CONTRIBUTION
We seek contributions that advance theoretical understanding, provide empirical evidence, or develop practical frameworks related to AI in business and management. Papers may extend existing theories to account for AI-specific phenomena, propose new conceptual models, or offer design science solutions to emerging challenges. We particularly encourage:
Studies critically examining both opportunities and risks of AI adoption • Research integrating multiple perspectives—strategic, organizational, behavioral, ethical, technological
Investigations considering contextual factors—industry, geography, organizational size, institutional environment
Works that investigate initiatives addressing one or more of the 17 United Nations Sustainable Development Goals (SDGs)
Contributions bridging academic rigor with practical relevance.
Given the focus of RAE in promoting research that matters, we encourage manuscripts that go beyond theoretical advancement to demonstrate clear social implications. Research on AI and organizational transformation has unique potential to contribute directly to the Sustainable Development Goals. We expect contributions to not only deepen our understanding of organizational practices and dynamics, but also to offer insights on how the knowledge generated can inform policies, strategies, and interventions aimed at fostering more equitable, sustainable, and human-centered AI implementations. By aligning scholarly work with these global challenges, the Special Issue seeks to highlight how management and organizational studies can serve as catalysts for meaningful societal change. We encourage authors to dedicate a section to the social implications of their research.
This special issue aims to provide a comprehensive examination of how AI is reshaping business and management, offering insights that advance scholarly understanding while informing organizational practice and policy development.
ETHICS AND ARTIFICIAL INTELLIGENCE (AI) USE
Authors are expected to adhere to RAE's (Revista de Administração de Empresas) ethical guidelines when preparing and submitting their manuscripts. Submissions must also comply with the journal's policy on the responsible use of AI tools. Any use of AI must be clearly disclosed and used transparently. Authors must ensure that such use does not replace their own intellectual contributions nor compromise the originality or novelty of their work.
ABOUT THE JOURNAL
RAE was launched in 1961 and is one of the most prestigious journals published in Latin America. The journal is currently ranked as level 1 in AJG ABS and indexed by several indexers and databases such as Elsevier's Scopus, SciELO, IBSS, HAPI, Spell, and JCR/Clarivate (WoS). Accepted articles are published in English and also in either Portuguese or Spanish to reach a wide global audience. RAE is an open-access journal and does not charge publication fees.
SUBMISSION OF PAPERS
Papers submitted must not have been published, accepted for publication, or presently be under consideration for publication elsewhere. To be eligible for review, the manuscript must be according to the RAE's guidelines (https:// bit. ly/rae-articles-format). The submission must be made through the ScholarOne system at http://mc04.manuscriptcentral.com/rae-scielo. For more information, write to: raeredacao@fgv.br
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