Artificial intelligence (AI) has become a central technology in debates on productivity, employment, and organizational competitiveness. This desk-based qualitative study examines whether AI primarily enhances or replaces human productivity in service and manufacturing sectors. Drawing mainly on recent studies from 2021–2026, the paper synthesizes empirical, theoretical, and policy literature on generative AI, industrial AI, robotics, human–AI collaboration, and task automation. Evidence from service-sector experiments shows that AI can substantially increase productivity in customer service, professional writing, software development, consulting, and administrative work, particularly when tasks are structured, information-intensive, and supported by clear performance metrics. However, gains are uneven and depend on worker skill, task type, data quality, organizational redesign, and human judgment. Manufacturing evidence suggests that AI and robotics can enhance productivity through predictive maintenance, quality control, scheduling, process optimization, and human–machine coordination, but short-term adoption costs, workforce disruption, and integration complexity may delay productivity gains. The analysis concludes that AI neither universally enhances nor simply replaces human productivity. Rather, AI restructures productivity by automating routine tasks, augmenting cognitive and operational decision-making, and shifting human value toward judgment, creativity, coordination, accountability, and socio-technical problem solving.