DEVELOPING AN INDIAN KNOWLEDGE SYSTEMS FRAMEWORK FOR AI-MEDIATED WORKPLACES:

Abstract

The rapid diffusion of Artificial Intelligence (AI) into organizational work is transforming work design, decision-making, coordination, performance evaluation, and employee–technology relationships. Human-Centered AI (HCAI) and AI ethics research has appropriately emphasized reliability, safety, transparency, explainability, accountability, fairness, and human control, while organizational scholarship has examined automation, augmentation, hybrid intelligence, algorithmic management, motivation, meaningful work, and thriving. Yet these perspectives give comparatively limited attention to the employee as an ethically self-developed and conscious person. This conceptual paper develops an Indian Knowledge Systems (IKS) framework for AI-mediated workplaces centered on Nishkāma Seva, Self-Making, and Consciousness. The paper conceptualizes Nishkāma Seva as duty-oriented and selfless action without egoistic attachment to outcomes, drawing on the Bhagavad Gītā, Yoga traditions, Upaniṣadic thought, Indian Psychology, and Indian humanistic management; Self-Making as intentional self-regulation and self-transformation; and Consciousness as reflective awareness and ethical discernment. These constructs are combined with Self-Determination Theory, meaningful-work research, workplace thriving, mindfulness, human-centered artificial intelligence, and ethical AI governance. The framework suggests that AI-mediated work influences employee flourishing via psychological conditions (e.g., autonomy, competence, relatedness, meaning, vitality, and learning) and that the IKS constructs provide developmental mechanisms through which employees can sustain agency, ethical resilience, meaningfulness, and growth. The paper develops propositions that relate AI-mediated work design to consciousness, self-making, Nishkāma Seva, and flourishing. It suggests ethical AI governance, organizational culture, leadership, and algorithmic control as contextual conditions. The framework provides a culturally situated extension of human-centered AI and a foundation for future construct development and empirical testing.