Sep 2026· South Asian Journal of Business Insights· 0 citations· 41 references
Abstract
While AI is often viewed as a strategic resource for improving efficiency and competitiveness, its outcomes remain uneven across organizations. Limited conceptual clarity remains regarding how artificial intelligence adoption contributes to organizational resilience, particularly in turbulent South Asian contexts. This paper argues that the missing link lies in AI readiness, conceptualized as a multidimensional capability that reflects the extent to which organizations and their employees are prepared to adopt, use, and adapt to AI technologies. Based on an integrative review, the study draws on the Resource-Based View, dynamic capabilities theory, and leadership perspectives to develop a conceptual framework that positions AI readiness as a mediating mechanism between leadership and organizational resilience. Leadership is conceptualized as an enabling force that shapes organizational conditions and supports capability development, while AI readiness facilitates the translation of technological potential into adaptive capacity. Organizational resilience is viewed as a dynamic capability involving anticipation, coping, and adaptation under disruption. The study further situates this framework within the context of South Asian economies characterized by institutional volatility, resource constraints, and uneven digital maturity. It argues that under such conditions, leadership-enabled AI readiness becomes particularly critical for sustaining organizational performance. The paper contributes to theory by integrating leadership, AI readiness, and resilience within a capability-based framework and offers practical insights for organizations seeking to navigate technological change in turbulent environments.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9
This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...
This work revisits schema linking when using the latest generation of large language models (LLMs) and finds empirically that newer models are adept at utilizing relevant schema elements during generation even in the presence of large numbers of irrelevant ones.
Karime Maamari, Fadhil Abubaker, Daniel Jaroslawicz et al.· arXiv.org· 109 citations· ⚡19
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