Artificial Intelligence Tools for Productivity Enhancement: A Systematic Literature Review 

Abstract

Artificial Intelligence (AI), particularly generative AI based on Large Language Models (LLMs), has rapidly transformed the execution of knowledge-intensive activities across multiple domains. AI-powered tools such as ChatGPT, GitHub Copilot, Microsoft Copilot, Google Gemini, Claude, and Notion AI have increasingly been adopted to automate repetitive tasks, support decision-making, accelerate software development, and improve content production. However, despite the rapid expansion of these technologies, scientific evidence regarding their effectiveness as productivity enhancers remains distributed across different research areas. This study presents a systematic literature review aimed at synthesizing current evidence on the role of AI tools in improving productivity in business, education, software engineering, and scientific research. A structured literature search was conducted across major academic databases, including Google Scholar, IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Scopus. Studies published between 2020 and 2026 were analyzed according to predefined inclusion and exclusion criteria. The reviewed literature indicates that AI tools can improve productivity by reducing task completion time, assisting knowledge creation, supporting programming activities, and enhancing information processing. Nevertheless, significant challenges remain, including inaccurate outputs, algorithmic bias, privacy concerns, ethical risks, and excessive reliance on automated systems. The findings suggest that AI achieves the greatest productivity benefits when used as a collaborative technology that augments human capabilities rather than replacing human expertise. Future research should investigate long-term productivity impacts, organizational adaptation strategies, responsible AI governance, and the integration of advanced multimodal AI systems into professional workflows.