Synthesizing Behavioral Decision Theory and Artificial Intelligence for Enterprise Transformation

Modern enterprise leadership operates within unprecedented volatility, complexity, and structural uncertainty. While traditional Business Intelligence (BI) systems capture historical transactional metrics, they fail to mitigate executive cognitive biases or predict dynamic market shifts. Conversely, autonomous Artificial Intelligence (AI) algorithms often operate as opaque "black boxes," lacking human contextual reasoning, executive intuition, and enterprise risk alignment. This paper presents the conceptualization, empirical validation, and strategic architecture of Strategic Decision Intelligence (SDI) an integrative framework that synthesizes Behavioral Decision Theory (BDT), Enterprise Risk Management (ERM), and Advanced AI/Machine Learning predictive analytics. Using a robust analytical framework and a synthetic simulation dataset of 10,000 strategic enterprise decisions (N = 10,000) reflective of Fortune 1000 operational profiles, we evaluate the comparative performance of traditional BI, standalone AI, and hybrid SDI decision architectures. The findings demonstrate that SDI reduces strategic decision latency by 64.2%, increases predictive decision precision by 38.6%, and decreases systemic enterprise risk exposure by 47.3% compared to baseline environments. Furthermore, this study extends foundational research in enterprise risk management (Alam, 2024; Alam et al., 2024) and big data analytics in large corporations (Alam & Shabbir, 2024) by demonstrating how dynamic AI debiasing and cloud security protocols optimize executive decision quality.