AI LEADERSHIP CAPABILITY AND HUMAN-AI COLLABORATION: AN ORGANISATIONAL DEVELOPMENT PERSPECTIVE IN THE INDIAN IT INDUSTRY

Authors

  • Jolly Sen Gupta IBMC, Mangalayatan University, Aligarh.
  • Ambrish Sharma Mangalayatan University, Aligarh.

DOI:

https://doi.org/10.70917/ijcisim-2026-5549

Keywords:

Artificial Intelligence, Organizational Culture, Psychological Readiness, Value Creation

Abstract

Artificial intelligence is reshaping leadership, teamwork, and organizational structures in India’s IT sector, particularly in Mumbai and Pune. This study examines how AI infrastructure maturity, employee psychological readiness, and organizational culture influence AI transformation success and value creation, with organizational size as a moderating factor. Using a quantitative approach, 110 employees from six major IT firms completed an 11-item, five-point Likert-scale survey. Data were analyzed using descriptive statistics, reliability tests, correlations, regression, structural equation modeling, ANOVA, and mediation analysis. AI infrastructure, organizational culture, and psychological readiness significantly predicted value creation (coefficients 0.44, 0.37, and 0.28), while organizational size was insignificant (0.11, p = .09). AI infrastructure was strongly associated with both organizational culture and value creation; psychological readiness supported value creation directly and indirectly. Findings highlight that technological maturity, workforce preparedness, ethical leadership, cultural agility, training, and accountable governance are critical for sustainable AI adoption. The study recommends prioritizing infrastructure and people capabilities over organizational expansion to maximize AI-driven value.

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Published

2026-09-07

How to Cite

Jolly Sen Gupta, & Ambrish Sharma. (2026). AI LEADERSHIP CAPABILITY AND HUMAN-AI COLLABORATION: AN ORGANISATIONAL DEVELOPMENT PERSPECTIVE IN THE INDIAN IT INDUSTRY. International Journal of Computer Information Systems and Industrial Management Applications, 18(23s), 23–30. https://doi.org/10.70917/ijcisim-2026-5549

Issue

Section

Original Articles