Aurelio Ravarini

The rapid rise of Generative AI (GenAI) chatbots such as ChatGPT and Claude presents organizations with a strategic dilemma: adopt powerful external solutions or invest in developing secure, internal alternatives. This study conceptualizes the "GenAI adoption dilemma" and examines it through the lens of internal data ecosystems. Based on qualitative case study research, including 46 interviews with senior professionals in a multinational consultancy firm, we identify three strategic pathways: internal, external, and hybrid chatbot adoption. We analyze them across five criteria: cost, risk, time to market, data ecosystem integration, and competitiveness. We develop a framework to guide decision-makers in navigating this emerging challenge. Our findings primarily contribute to GenAI chatbot adoption literature by framing it as a systemic organizational decision rather than a purely technological, they extend data ecosystem theory by empirically linking GenAI chatbots adoption to internal infrastructure, governance, stakeholders, and data practices, and provide insights for IT adoption and outsourcing literature.

The adoption of Generative AI chatbots in organizations is transforming how employees perform cognitive work, enhancing efficiency and supporting augmentation. Perceived social presence can make these systems feel more human-like and trustworthy; however, it may also lead employees to overestimate their capabilities and delegate cognitive effort to them. This study investigates how trust, shaped by perceived social presence, contributes to higher-order cognitive offloading—the tendency to shift critical thinking to Generative AI chatbot tools. To explore this phenomenon, we propose to conduct an inductive qualitative case study supported by thematic analysis of interviews. By collecting insights from employees and managers, the study will examine how Generative AI chatbots are used in the workplace and the triggers and consequences involved. The goal is to develop an empirically grounded framework explaining the mechanisms linking perceived social presence, trust, and cognitive offloading in organizations adopting Generative AI chatbots, offering contributions to IS research and guidance for practitioners.

As AI reshapes manufacturing, understanding how workers adapt and develop new competencies is critical. This study offers a grounded, step-by-step model of task crafting in a digitally evolving manufacturing company, highlighting how employees respond to misalignments by redesigning tasks, tools and routines. Based on interviews and observations, the findings reveal a recursive process driven by experiential knowledge, collaboration, and local innovation. Generative AI chatbots (e.g., ChatGPT) augment this process by supporting ideation, automation and informal learning, without displacing human agency. Generative AI supports micro-reskilling and hybrid competencies as a digital companion. This study contributes to job crafting literature by framing competence development as emergent and situated, and to Generative AI-in-manufacturing debates by showing how human-AI collaboration unfolds in practice. It offers implications for workforce transformation strategies aligned with Industry 5.0, where AI and human expertise co-evolve in dynamic, socially embedded ways.

The rise of user-driven digital practices has long challenged organizational IT governance, with Shadow IT emerging as a widely studied phenomenon reflecting the unauthorized use of technologies within firms. However, the rapid advancement of Artificial Intelligence (AI) - and more recently Generative AI (GenAI) - has introduced new forms of shadow activity that are not yet fully captured in existing literature. This paper addresses this gap by offering a conceptual extension of the Shadow IT paradigm, arguing that Shadow AI and Shadow GenAI represent qualitatively distinct yet evolutionarily linked developments in shadow technology use. We conduct a Systematic Literature Network Analysis (SLNA) using Scopus-indexed data and bibliometric tools such as VOSviewer and Pajek to examine how the academic discourse on Shadow IT has evolved, and how emerging research is beginning to address the challenges posed by unauthorized AI and GenAI adoption. Our analysis reveals a deepening of risks: from governance and data privacy in Shadow IT, to ethical opacity and knowledge erosion in Shadow GenAI. The paper contributes to the literature by theorizing Shadow AI and GenAI as part of a continuum rooted in organizational strain and user rationalization. In doing so, we highlight the need for new governance strategies that go beyond infrastructure control to address the epistemic and cultural implications of AI-driven autonomy in the workplace.

This study explores how prompt engineering training influences student performance and adaptability when using Generative AI (GenAI) chatbots in education. Focusing on first-year engineering management students, we examine the impact of targeted instruction on learning outcomes, linking AI interaction to academic self-efficacy, goal orientation, self-monitoring, study skills, and technology engagement. Using a mixed-methods approach, results show that prompt engineering significantly enhances academic performance and critical engagement with AI tools. Findings highlight the importance of integrating AI literacy into curricula to maximize GenAI’s educational potential and prepare students for AI-driven professional environments.

This study investigates how Generative AI (GenAI) chatbots mediate sensemaking in organizations, particularly in contexts characterized by equivocality. Drawing on the enactment model by Weick and colleagues (2005), we analyze how GenAI technology interacts with workers during sensemaking. Our findings reveal that GenAI chatbots act as assistants, collaborators, and facilitators, by both introducing as well as reducing equivocality in an organizational context through iterative dialogue and contextual adaptation. The chatbots do not only transform cues into actionable insights, they also introduce novel ways of structuring and interpreting information. Furthermore, workers’ presumption of the chatbot behavior shapes their interaction with it, influencing its role in navigating equivocality during sensemaking. By extending theoretical boundaries of technology-mediated sensemaking, this research provides actionable insights into leveraging GenAI for organizational resilience and adaptability in dynamic environments.

In the era of digital transformation, understanding the interplay between Artificial Intelligence (AI) and human decision-makers is crucial. This paper examines the nuanced dynamics of AI in decision-making processes, focusing on its role as a decision-maker, an enabler, and its impact on work paradigms and organizational structures. Through a comprehensive literature review and qualitative case studies across diverse sectors, the study introduces a novel analytical framework that aligns AI's role with decision-making stages. This framework aims to identify optimal organizational structures, ranging from full human to full AI delegation, including hybrid models. By highlighting the human-centric approach and the interdependence of human and AI in decision-making, this research contributes significantly to the evolving field of organizational behavior and decision science, providing insights into effective AI integration and the transformative role of Generative AI in professional landscapes.

This article articulates the nuanced challenges of integrating Generative AI (GenAI) into educational settings, aiming to dispel overly simplistic narratives driven by unwarranted enthusiasm or unfounded apprehensions. It introduces a conceptual framework for the application of GenAI within higher education, delineating four key strategies that leverage the dual roles of educators - as both creators and designers - while positioning GenAI as either a facilitative agent (creature) or a utilitarian tool. The identified strategies - Interactive Co-Creation, Adaptive Design, Learning Scaffold, and Efficient Structuring - underscore GenAI’s potential to revolutionize teaching methodologies by enabling personalized education, enhancing content quality, and expediting course development processes. Emphasizing GenAI’s capacity to cater to diverse student needs, simplify educational content creation, and foster engaging learning environments, the model provides educators with a roadmap for integrating GenAI into their instructional practices to harness the full potential of educational technology.

In this paper, we introduce digital job crafting as an evolution of the concept of job crafting. An in-depth literature review demonstrated that in the broad research dedicated to job crafting, only limited attention has been dedicated to the effect that digital technology exerts on job crafting practices. We propose using the socio-technical approach to make these impacts explicit and, thus, re-frame the concept of job crafting in a new, integrated model. Two case studies are presented to explore the effectiveness of applying the model in different organizational contexts.

Driven by the growing availability and accessibility of data and processing power, businesses across all sectors are developing and implementing increasingly "intelligent" systems that are empowered by a wave of artificial intelligence (AI) technologies. This paper examines the state of AI adoption in small and medium-sized enterprises (SMEs) in the manufacturing sector, an area of research that has received relatively little attention. Three SMEs, whose main offices are located in Northern Italy, have been studied to understand how they deal with the issue of AI adoption and what problems they typically encounter. An articulated picture emerges where SMEs struggle between the desire to realize the promises of innovation and the ability to build the appropriate organizational setting to pursue it.