The Influence of Organizational Capabilities on AI Implementation for Sustainable Business Transformation

Sammanfattning

Background: Organizations increasingly adopt artificial intelligence (AI) to improve operations and competitiveness while facing growing pressure to operate sustainably. However, despite AI's potential to support sustainable practices, outcomes vary significantly across organizations, suggesting that organizational capabilities play a critical role in determining implementation success. Purpose: This study examines the influence of organizational capabilities on AI implementation for sustainable business transformation. Specifically, it investigates how organizations sense opportunities, seize strategic initiatives, and reconfigure internal structures to integrate AI for sustainability, and what differences emerge in sustainability outcomes between firms with strong versus weak organizational capabilities. Method: A qualitative single-case study design was employed, utilizing eight semi-structured interviews with participants across multiple organizational levels (executive, managerial, operational, and cross-functional representatives) in a leading Swedish multinational company recognized for sustainability commitment. Data was analyzed using thematic analysis with integrated deductive and inductive coding approaches, grounded in Dynamic Capabilities Theory and Organizational Ambidexterity Theory. Results and analysis: The findings show that organizational capabilities play a central role in shaping AI-driven sustainability outcomes. Organizational ambidexterity is especially important, requiring deliberate structures to balance innovation with operational efficiency. Effectively identifying opportunities relies on multiple sensing channels that combine formal processes with insights from frontline employees. Resource constraints also strongly influence organizational behavior by shaping how priorities are set. In addition, formal governance mechanisms, transparent learning processes, and clear leadership commitment are essential for achieving successful transformation. Conclusions: AI implementation for sustainability is fundamentally an organizational capability challenge rather than a purely technological one. Organizations demonstrating strong sensing, seizing, and reconfiguring capabilities paired with deliberate ambidexterity mechanisms and transparent learning cultures to achieve more effective sustainability outcomes. The study extends Dynamic Capabilities Theory by demonstrating how these capabilities operate in practice within complex AI-sustainability contexts and supports Organizational Ambidexterity Theory by showing that structural solutions, not merely cultural approaches, are critical to balancing competing organizational demands. Recommendations for future research: Future research should test these findings on larger groups of organizations to confirm them, study how capabilities develop over time, and compare how different industries handle AI-driven sustainability. It should also look at human factors like employee engagement, resistance, and skills, as well as how companies ensure ethical and responsible AI use, giving a fuller picture of how AI can support sustainable transformation.