Organisational Ambidexterity in the Context of Artificial Intelligence: A Systematic Literature Review on AI’s Impact on Exploration and Exploitation

Abstract

Existing research highlights that artificial intelligence (AI) technologies impact organisational ambidexterity. Yet, the literature offers fragmented viewpoints, as the studies are scattered across various ambidexterity focus areas, industries, and geographies. This creates a significant gap in the understanding of how AI influences exploration and exploitation on an organisational level. The main research aim of this thesis was to synthesise a conceptual framework that explains how artificial intelligence shapes organisational ambidexterity through its impacts on exploration and exploitation. By following a rigorous and transparent systematic literature review process, this thesis incorporated the findings from 71 peer-reviewed articles. Data analysis adhered to a hybrid approach. First, the thematic analysis was conducted in an inductive manner, uncovering recurring themes and patterns, followed by the application of ambidexterity theory as analytical lens to group the findings into three main domains – AI-enabled exploration, AI-enabled exploitation, and AI-enabled organisational ambidexterity. As a result, 11 themes were identified. In the exploration domain, it was identified that AI enables exploratory innovation, sensing and seizing of new business opportunities, and creativity. In the exploitation domain, it was discovered that AI enables efficiency gains, improves the efficiency of business functions, enables exploitative innovation, improves decision-making, and acts as a learning assistant. In the organisational ambidexterity domain, it was uncovered that AI aids in balancing exploration and exploitation, as well as enables ambidextrous learning and innovation. As a final outcome of this thesis, a conceptual framework of AI-driven organisational ambidexterity was developed. The framework incorporates organisational and contextual conditions, AI capabilities, AI-enabled mechanisms of organisational ambidexterity, exploratory activities, exploitative activities, organisational ambidexterity and tensions, and it suggests a conceptual explanation of how these constructs interact. In essence, the framework proposes that conditions and AI capabilities could activate mechanisms that enable both exploratory and exploitative activities, which, in turn, shape organisational ambidexterity. This thesis identified four AI-enabled mechanisms of organisational ambidexterity: innovation and opportunity recognition, optimisation, learning and knowledge management, and cognitive and analytical mechanisms. As an additional layer of realism, the framework highlights inherent tensions that could occur in this process.