Artificial Intelligence Adoption and Operational Performance in Indian Manufacturing Organisations: A Systematic Literature Review

Artificial Intelligence (AI) is becoming an increasingly visible part of modern manufacturing. Organizations are using AI for predictive maintenance, quality inspection, production planning, demand forecasting, process optimization, robotics, automation, and decision support. These applications can improve efficiency, control costs, strengthen product quality, and help managers make decisions more quickly. At the same time, the effects of AI cannot be understood only through organizational results. The technology also changes how employees carry out their work, the skills they need, and the ways in which they interact with digital systems. This paper presents a systematic literature review of studies on AI adoption and operational performance in manufacturing, with particular attention to the Indian context. The review covers AI adoption, manufacturing performance, employee productivity and skills, AI readiness, training, acceptance, human-AI collaboration, technostress, job insecurity, trust, and governance. A corpus of 20 studies was examined and thematically organized into five broad areas: AI readiness and infrastructure; firm productivity and operations; employee experience and skills; strategic and cultural factors; and generative AI in organizations. Overall, the reviewed literature provides fairly strong evidence on the factors that support AI adoption and on its association with organizational and operational performance. AI is commonly associated with gains in productivity, process efficiency, quality, cost management, predictive capability, and decision-making. Evidence at the individual employee level, however, is much thinner. The review also points to a lack of longitudinal studies and India-specific research focused specifically on manufacturing. Employee AI readiness, skills, training, acceptance, trust, technostress, job insecurity, and governance appear to be important conditions shaping the results of AI adoption. On the basis of these findings, the paper proposes a conceptual framework in which AI adoption may influence employee productivity and wider operational performance through employee AI readiness, skills, AI acceptance, and effective AI use. Training, technostress, job insecurity, trust, and AI governance may strengthen or weaken these relationships. The review therefore points toward a more human-centred approach to AI adoption and suggests that future research should ask not only whether AI improves manufacturing performance, but also how, when, and under what conditions such improvements take place.