Artificial intelligence, machine learning, and deep learning for enhancing managerial productivity and task efficiency
Abstract
The issue of managerial productivity and task efficiency are two critical issues in organizations operating in more complex, data-intensive, and fast-evolving business environments. Conventional managerial practices usually encounter difficulties dealing with the flow of information, ineffectiveness of workflow, slow decision making, and redundant administrative processes, which provokes the necessity to resort to high-tech interventions. Machine Learning, Artificial Intelligence, and Deep Learning are the revolutionized tools that can optimize managerial decisions, intelligent automation, streamlining of workflows and performance of an organization. This literature review employs PRISMA model to effectively screen, identify and synthesize the available literature that addresses Artificial Intelligence, Machine Learning and Deep learning in promoting managerial productivity and task efficiency. The review discusses how intelligent automation, predictive analytics, natural language processing, generative AI, large language models, robotic process automation, decision support systems, and hyper automation in managerial settings have evolve. Special focus is put on new tendencies in explainable AI, the work of humans and AI, knowledge management, enterprise analytics, and algorithmic management. The results show that AI-based technologies can greatly enhance managerial efficiency toward automatizing repetitive activities, increasing resource distribution, enabling intelligent schedulization, predictive modelling and strategic planning. Another use that enhances productivity of employees, business intelligence, process mining, and data-driven decision-making is the use of Machine learning and Deep learning to make predictions, perception, and adaptive learning solutions. Issues embracing AI governance, ethical, openness, workforce adjustment, and integration complexity are crucial concerns that can be considered in the managers logical implementations.
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References
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Copyright (c) 2026 Victoria Ayah Zeb-Obipi (Author)
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Copyright (c) 2025 International Journal of Applied Resilience and Sustainability (IJARS) 
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