• Manish Tripathi
• Dr Amit Kumar Jain

Generative AI is transforming content tagging by automating the process of classifying, annotating, and organizing digital content, significantly improving efficiency and reducing costs compared to traditional manual methods. By utilizing natural language processing and deep learning, AI models can quickly generate metadata, enhancing the accuracy and speed of content categorization across various industries, including e-commerce and media. This automation reduces the reliance on human labor, minimizes errors, and accelerates content discovery, leading to greater operational efficiency. Additionally, AI-driven tagging improves content retrieval and resource management while maintaining scalability for large datasets. Despite its benefits, challenges such as AI bias and data privacy concerns remain, requiring careful oversight. Ultimately, AI-based tagging offers organizations substantial cost savings and a competitive advantage by optimizing content management workflows and boosting digital asset utilization, with potential for further advancements in accuracy and adaptability in the future.

Generative AI, content tagging, automation, metadata generation, natural language processing, deep learning, content classification, operational efficiency, cost savings, digital asset management, scalability, content discovery, AI bias, data privacy, workflow optimization.

"Generative AI Content Tagging: Cost Savings and Efficiency in Content Management", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.j466-j486, March-2025, Available :http://www.jetir.org/papers/JETIR2503962.pdf

"Generative AI Content Tagging: Cost Savings and Efficiency in Content Management", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppj466-j486, March-2025, Available at : http://www.jetir.org/papers/JETIR2503962.pdf