Reimagining leadership for the AI era: a grounded theory of adaptive, ethical and contextually situated practices in workplaces

Purpose As artificial intelligence (AI) reshapes workplace systems, traditional leadership models struggle to address the complexities of human-AI collaboration. This study explores the evolving competencies, behaviors, and identity transformations necessary for effective leadership in AI-augmented environments, particularly within the Indian context. Design/methodology/approach Employing a grounded theory methodology, 60 in-depth interviews were conducted with leaders across diverse industries in India. Data were analyzed inductively to build a contextually rich, systemic framework of leadership in the AI era. Findings The study identifies a three-pillar model of leadership transformation in AI-augmented workplaces: anticipatory-ethical leadership, socio-technical reengineering, and ontological shifts in leader identity. Leaders proactively address AI's ethical challenges, redesign organizational practices to balance technology and human values, and shift from control-based to distributed, dialogic leadership styles to foster innovation and collective adaptation. Practical implications Recommendations are offered for HR practitioners and organizational leaders to navigate AI transitions through foresight, empathy, and systemic awareness. Emphasis is placed on fostering distributed leadership, ethical AI integration, and innovation ecosystems that elevate human potential alongside technological advances. Social implications Beyond organizational practices, the AI-era leadership framework has broad implications for public policy and inter-organizational governance, especially in emerging economies. Leadership development must extend beyond firms into national education systems, ethical AI policies, and multi-stakeholder governance. Categories such as ethical tech sense making and equity tracing can guide certification programs and accountability standards, while inclusive AI socialization and role reengineering inform leadership training in universities and public service academies. At a cross-sector level, shared governance alliances can set norms for refusal criteria, recertification, and equity protections. Ultimately, AI leadership demands systemic, multi-level capacity building for inclusive, sustainable development. Originality/value The study advances leadership theory by integrating cultural context, emotional labor, and collective resilience into discussions of AI-era leadership. It highlights the systemic, relational, and ethical dimensions that are critical but often overlooked in AI transition narratives.