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<p>As Artificial Intelligence (AI) assumes a growing presence in executive decision spaces, an empirical and theoretical gap persists concerning the boundary conditions governing how top management teams (TMTs) integrate AI into strategic decision-making (SDM). Existing research addresses AI’s impact on operational, functional, and middle-management decisions, yet leaves the unstructured terrain of Top management teams’ strategic decision-making largely unexplored. We argue that SDM at the apex of organisations is distinct not because any single attribute is unique to it, but because four attributes such as the irreversibility of strategic decisions, the socio-political collectivity of TMT processes, the proprietary character of executive AI tools, and the identity stakes of strategic leadership may co-occur and reciprocally amplify each other, producing integration dynamics qualitatively different from those theorized in the broader human-AI collaboration literature. We address two research questions: (1) Under what contextual conditions do TMT members delegate strategic decisions to AI rather than treat AI as an augmentation resource? (2) How does the structure of strategic decisions and the predictability of their situational context shape the mode and depth of human-AI collaboration in TMT practice? Guided by the Gioia methodology and drawing on 33 semi-structured interviews with C-suite executives across three multinational organisations, supplemented by structured demonstrations of in-house AI tools at each firm, we develop a grounded framework distinguishing high-confidence AI delegation of analytical work from iterative human-AI co-deliberation along two axes: problem structuredness and situational predictability. Eighteen first-order codes, six second-order themes, and two aggregate dimensions as (a) Contextualized AI Integration and Delegation Framework, and (b) the Transformative Human-AI Strategic Empowerment dimension, emerge from systematic inductive analysis. Three theoretically grounded propositions offer implications for Upper Echelons Theory, Strategic Decision-Making Theory, and Contingency Theory, providing a TMT-specific account of human-AI integration that goes beyond the generic automation–augmentation divide.</p>

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Integrating Artificial Intelligence in Strategic Decision-Making: Contexts for Delegation and Augmentation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月17日

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  • Ram Srinivas,
  • Sahana Swaroop Chetan

摘要

As Artificial Intelligence (AI) assumes a growing presence in executive decision spaces, an empirical and theoretical gap persists concerning the boundary conditions governing how top management teams (TMTs) integrate AI into strategic decision-making (SDM). Existing research addresses AI’s impact on operational, functional, and middle-management decisions, yet leaves the unstructured terrain of Top management teams’ strategic decision-making largely unexplored. We argue that SDM at the apex of organisations is distinct not because any single attribute is unique to it, but because four attributes such as the irreversibility of strategic decisions, the socio-political collectivity of TMT processes, the proprietary character of executive AI tools, and the identity stakes of strategic leadership may co-occur and reciprocally amplify each other, producing integration dynamics qualitatively different from those theorized in the broader human-AI collaboration literature. We address two research questions: (1) Under what contextual conditions do TMT members delegate strategic decisions to AI rather than treat AI as an augmentation resource? (2) How does the structure of strategic decisions and the predictability of their situational context shape the mode and depth of human-AI collaboration in TMT practice? Guided by the Gioia methodology and drawing on 33 semi-structured interviews with C-suite executives across three multinational organisations, supplemented by structured demonstrations of in-house AI tools at each firm, we develop a grounded framework distinguishing high-confidence AI delegation of analytical work from iterative human-AI co-deliberation along two axes: problem structuredness and situational predictability. Eighteen first-order codes, six second-order themes, and two aggregate dimensions as (a) Contextualized AI Integration and Delegation Framework, and (b) the Transformative Human-AI Strategic Empowerment dimension, emerge from systematic inductive analysis. Three theoretically grounded propositions offer implications for Upper Echelons Theory, Strategic Decision-Making Theory, and Contingency Theory, providing a TMT-specific account of human-AI integration that goes beyond the generic automation–augmentation divide.