Cristina Alaimo, ESSEC Business School

Lauren Waardenburg, ESSEC Business School

Jonny Holmström, Umeå University

Lior Zalmanson, Tel Aviv University

Reza M. Baygi, VU Amsterdam

We are living through a profound epistemic shift in the history of computation. Contemporary AI systems learn from, operate through, and increasingly generate data at unprecedented scale. Unlike symbolic approaches associated with GOFAI (Good Old-Fashioned AI), advances in machine learning and deep learning have repositioned data—not rules—as the primary substrate of computational intelligence. This transformation compels us to rethink the foundations of AI scholarship within the IS discipline and to develop new theoretical vocabularies to understand the data-AI nexus as a complex socio-technical phenomenon.

Data are sociotechnical artefacts shaped by institutional practices, historical trajectories, cognitive frames, and semiotic conventions. As AI systems are built on data, learn from data, and reshape data through processes of classification, prediction, and generation, data are simultaneously increasingly crafted to meet the requirements of AI systems. Training datasets are curated, cleaned, labelled, augmented, and optimized with particular model architectures in mind. In this recursive dynamic, the boundary between data and AI becomes blurred, demanding renewed conceptual attention to how organisations' social fabric alters in the wake of AI.

Experts and organisations primarily encounter and shape AI through data practices: generating inputs, curating datasets, fine-tuning models, evaluating outputs, and conducting quality control. To understand AI in organisational contexts, we must therefore unpack the socio-cognitive and technical processes of data production, cleaning, transformation, and governance; that is, the data work embedded in everyday organisational routines. These processes extend beyond individual tasks to encompass institutional histories of data: organisational memory, sedimented classification systems, collective sensemaking, and enduring cognitive frames. Such historically layered infrastructures shape both how AI systems are designed and how their outputs are interpreted.

AI is no longer trained only on data understood as structured database entries. Today, AI systems rely on an expanding range of materials, digital and analogue alike, that are transformed into training input. For the first time, AI also generates data. Synthetic data—text, images, code, and other artefacts generated by machines—are becoming a key input for model training, fine-tuning, and evaluation. The growing use of synthetic data for training purposes may be motivated by performance considerations, but also by issues of privacy, scarcity, access, and governance. When synthetic data, disconnected from social practice, recursively feed new models, the challenge becomes institutional, as human actors defer to AI even where scepticism and independent judgment are needed for legitimizing AI-generated material.

We invite submissions that advance our understanding of the data-AI nexus by foregrounding a dialogue between existing research traditions on data and AI, which have often remained disconnected. We particularly welcome theoretically ambitious and empirically rich studies that move beyond purely technical accounts of AI to explore its socio-technical, organisational, and epistemic dimensions. We encourage submissions that interrogate how data shape AI systems, how AI systems reshape the data they consume, and what happens when data becomes increasingly detached from practices of representation and meaning making.

We aim to catalyse how IS scholars conceptualise and study the interdependencies between data, AI, and social practice. We seek contributions that treat the data-AI nexus not as a technical backdrop but as a core analytical problem that reshapes how knowledge is produced, validated, and institutionalized in digital societies. We welcome empirical and conceptual contributions embracing a broad range of paradigms, methodological approaches, and levels of analysis, valuing diversity in theories, methods, and genres.

Potential topics

• Data and AI co-constitution: exploring where and when intelligence in AI emerges in computational architectures, datasets, or their entanglement, and how choices about data selection, curation, labelling, and historical accumulation shape what AI systems can know and do
• AI as a data-maker: conceptualizing AI as an active producer of data that structures, generates, and transforms the data it relies on, including the rise of AI agents and recursive dynamics where synthetic data becomes input for other AI systems
• Data decoupled from practice: examining consequences of data production detached from institutionalized knowledge practices or situated practices of meaning-making, and implications for governance and decision-making
• The social life of the data-AI nexus: tracing institutional, historical, and cognitive trajectories of data as it is produced, curated, transformed, and mobilized, including how organizational memory and AI agents reshape institutional dynamics
• Theoretical and methodological advances: reconceptualizing constructs such as intelligence, data quality, ground truth, training, and synthetic data as relational, temporal, and emergent phenomena
• Cross-level insights: studying how assumptions about data and intelligence are embedded in AI systems and reshape organizational practices and societal outcomes
• Data work in organizations: examining curation, labelling, cleaning, evaluation, model development practices, and integration of generative AI into workflows
• Data, AI, and grand challenges: addressing climate change, public health, inequality, digital governance through responsible AI in contexts with incomplete, biased, or synthetic data

Timeline

• May 28, 2026: Call launch at the Theorizing Data & AI Conference
• January 15, 2027: Initial paper submission deadline
• April 15, 2027: First round authors notification
• May 1, 2027: Workshop for first round authors during Theorizing Data & AI Conference
• July 31, 2027: Invited revisions deadline
• October 31, 2027: Second round authors notification
• January 15, 2028: Final revision deadline

Associate editors

• Aleksi Aaltonen, Ida Asadi Someh
• Ioanna Constantiou, Domenico di Prisco
• Mayur Joshi, Ekaterina Jussupow
• Jannis Kallinikos, Tomislav Karačić
• Stan Karanasios, Alexander Kempton
• Angelos Kostis, Harris Kyriakou
• Christine Legner, Kalle Lyytinen
• Eric Monteiro, Jeff Parsons
• Mike Power, Jan Recker
• Paavo Ritala, Marta Stelmaszak Rosa
• Lauri Wesssel,