A Review of Artificial Intelligence Approaches and a Proposed Framework for Intelligent Productivity Systems

This paper presented an AI-driven productivity and task-management framework, My Aiger, designed to address the limitations of traditional task-management systems. By integrating natural language processing, machine learning based prioritization, adaptive scheduling, predictive risk analysis, and intelligent insights, the proposed system offers a comprehensive and user-centric approach to productivity management. The architecture and workflows demonstrate how intelligent automation can support users in managing tasks more effectively while maintaining transparency and control. Although the proposed system addresses several critical research gaps, there remains scope for further enhancement. Future work may focus on improving model explainability to enhance user trust, incorporating advanced learning techniques for personalized recommendations, and conducting large-scale empirical evaluations to validate system performance across diverse user groups. Additionally, integration with external productivity tools and enterprise platforms can further extend the applicability of My Aiger. Overall, the proposed approach establishes a strong foundation for the development of intelligent, adaptive, and scalable productivity management systems.

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