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AI and Natural Language Processing in Qualitative and Organizational Research

Henri Schildt / Arnaud Cudennec | Skimle and Aalto University | Bayes Business School, City St George's, University of London

AI and Natural Language Processing in Qualitative and Organizational Research
FECHA Y LUGAR

12/02/2026 14:00

Sala Descubre/Online Teams. Edificio 8E. Acceso J - 4ª Planta | Universitat Politècnica de València. 46022

ABSTRACT

Organized by Focal Area 5, INGENIO (CSIC–UPV)

14:00–15:25 | Talk Henri Schildt, Founder & CEO of Skimle | Professor of Strategy, Aalto University

Title: Qualitative Research with Artificial Intelligence: From Automating Research to Augmenting Collaboration

Henri Schildt will discuss how AI can augment qualitative research by supporting collaboration, transparency, and analytical rigor, drawing on the design and use of Skimle. He is Professor of Strategy at Aalto University and Founder & CEO of Skimle. His work bridges strategy research, qualitative methods, and artificial intelligence. He focuses on designing AI tools that enhance—rather than replace—scholarly judgment.

 

15:25–15:35 | Break

 

15:35–17:00| Talk Arnaud Cudennec, Lecturer (Assistant Professor) in Strategy, Bayes Business School, City St George's, University of London

Title: Natural Language Processing (NLP) in Organization Studies: Recent Approaches and Lessons from Applied Work

This workshop introduces recent uses of NLP in organization studies, with a focus on word embeddings and transformer-based approaches, discussing insights and methodological challenges from applied research.

Arnaud Cudennec is a Lecturer in Strategy at Bayes Business School. His research examines categorization and evaluation in innovative fields using sociological theory, experiments, large-scale data, and NLP. His work has been published in leading journals such as Academy of Management Journal and Poetics.

 

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Reunión de Microsoft Teams

Unirse: https://teams.microsoft.com/meet/33067707972937?p=Qg7jnv7KXjTW78Jb4Y

Id. de reunión: 330 677 079 729 37

Código de acceso: HJ9yM38p