Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review

Analysis

Why This Paper Matters

This comprehensive literature review offers a timely synthesis of how generative AI is reshaping productivity across multiple professional sectors. By systematically analyzing 159 publications using the PRISMA methodology, the authors provide a structured map of GAI applications that is valuable for both researchers and practitioners navigating this rapidly evolving field. The paper's focus on ethical considerations and analytical thinking addresses critical concerns that often lag behind technological adoption.

The bibliometric identification of ChatGPT as the dominant tool underscores the platform's outsized influence on current GAI research and practice. This finding is particularly relevant for organizations deciding where to invest in AI capabilities. The projected acceleration of research through 2024 signals that the field is far from mature, making this review a useful baseline for future work.

Technical Contributions

• PRISMA-based systematic review: Provides a reproducible methodology for analyzing GAI literature, setting a standard for future reviews.
• Sector-specific analysis: Maps GAI applications across academia, research, technology, communications, agriculture, government, and business, revealing domain-specific trends and gaps.
• Bibliometric analysis: Quantifies the dominance of Chatbots and Conversational Agents, with ChatGPT as the central tool, offering empirical evidence of tool adoption patterns.
• Content analysis: Identifies significant trends (e.g., accelerating research output) and gaps (e.g., lack of long-term user experience studies) in current GAI literature.

Results

The review finds a robust and accelerating trend in GAI research, expected to continue through 2024 and beyond. ChatGPT emerges as the most frequently cited tool in the bibliometric analysis, highlighting its central role in GAI evolution. The content analysis reveals that while GAI is being applied across multiple sectors, significant gaps remain in assessing its long-term impact on user experience and in strategic planning for deployment.

Significance

This paper provides a foundational reference for AI practitioners and researchers seeking to understand the current landscape of generative AI for productivity enhancement. By systematically cataloging applications and identifying dominant tools, it helps organizations benchmark their own AI strategies. The emphasis on ethical considerations and analytical thinking is particularly timely, as concerns about AI misuse and bias grow. The identified research gaps—especially around long-term user experience—point to important areas for future investigation, ensuring this review will remain relevant as the field evolves.