In modern society, digital transformation and the increasing use of artificial intelligence (AI) pose significant challenges for organisations across Europe and beyond. In particular, traditional retail environments must navigate complex changes as AI systems are integrated into customer service and operational workflows. This research explores the complexities of AI adoption in retail through the lens of organisational learning theories, focusing on the empirical case of Atlas, an AI system introduced in shoe retail stores to improve customer service.

The study uses three theoretical frameworks - Theory of Practice Architectures (TPA),  Levels of Learning and Relations (LL/FPSB), and the Organizational Learning Triangle (OLT) - to explore how different perspectives influence the interpretation of digital transformation processes. Each framework offers different insights: TPA explores the interconnectedness of saying, doing, and relating within organisational practices; LL/FPSB provides a multifaceted analysis of learning and structural change; and OLT systematically categorises organisational learning processes in relation to knowledge management, culture, and environment.

By applying these theories to the Atlas case, this research critically evaluates the strengths and limitations of each framework in capturing the complexity of AI-driven change. The study contributes to organisational learning research by emphasising theoretical plurality, contextual sensitivity and methodological reflection - three critical aspects for understanding and guiding AI integration in European and international retail environments.

The central research question is
How do different theoretical frameworks of organisational learning influence the systematic analysis of empirical cases?

This research not only advances discussions on AI adoption in traditional retail, but also highlights the role of meta-theoretical assumptions in shaping how researchers interpret digital transformation. The findings aim to inform both academia and practice, providing valuable perspectives for organisations seeking to effectively navigate AI-driven change.

This research follows a comparative theoretical approach, applying several organisational learning frameworks to a single empirical case. The methodology is divided into three key phases: 1. Case preparation and abstraction - The Atlas case was systematically prepared and abstracted to identify its essential characteristics. This step ensured that the case remained generalisable and suitable for theoretical analysis, while retaining its real-world complexity. 2. Cross-theoretical analysis - A cross-national consortium of researchers from Sweden, Germany and Norway analysed the Atlas case using TPA, LL/FPSB and OLT. Each framework was applied independently, allowing the researchers to observe how different theoretical lenses shape the interpretation of AI integration challenges. 3. Comparative discussion and synthesis - A structured discussion among consortium members identified the strengths and limitations of each framework. This phase highlighted where theories converged and diverged in explaining AI-related learning processes, organisational adaptation and employee experiences. The study uses qualitative analysis based on theoretical reasoning and case study interpretation. By systematically comparing theoretical applications, the research assesses the extent to which different frameworks capture essential dynamics of AI-driven organisational learning.

This study aims to demonstrate how meta-theoretical assumptions influence the analysis of organisational learning in the context of digital transformation. By applying TPA, LL/FPSB and OLT to the Atlas case, the research will illustrate how each framework influences what researchers can observe, interpret and conclude about AI integration. Key findings will include: 1.Theoretical strengths and limitations - While TPA provides granular insights into everyday practices and enabling constraints, LL/FPSB offers a broader structural perspective, and OLT systematically categorises learning dynamics. The comparative analysis will show the extent to which each theory addresses practical, systemic and cultural aspects of AI adoption. Importance of Theoretical Plurality - The study will highlight the need for context sensitive, 2. 2. sensitive, multi-theoretical approaches to organisational learning research, particularly when analysing complex transformations such as AI adoption. By integrating different theoretical perspectives, this cross-national research contributes to the advancement of organisational learning, digital transformation studies and the broader discourse on AI in the workplace. Ultimately, the study aims to refine how organisations conceptualise, implement and evaluate AI-driven learning and change processes.

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