Identifying and Building Generative AI Use Cases Within Enterprise Software Products

Authors

  • Arjun Raj Bhalla  Independent Researcher, India

DOI:

https://doi.org/10.32628/IJSRST2221185

Keywords:

Generative AI (GenAI), enterprise software, AI use cases, machine learning models, transformer models, AI-driven automation, business applications, natural language processing (NLP), image generation, data augmentation, deep learning applications, ethical AI, AI governance, monetization strategies, subscription-based AI services, API-based pricing models, automation, decision-making, structured frameworks, implementation, scaling AI capabilities.

Abstract

Generative AI (GenAI) is revolutionizing enterprise software by enabling text generation, image synthesis, and predictive modeling, leading to enhanced user experiences, workflow automation, and new business value. However, integrating GenAI into enterprise applications requires navigating technical, operational, and ethical challenges. This paper presents a structured framework for identifying and implementing GenAI use cases across various industries, addressing considerations such as data privacy, model interpretability, and computational efficiency. By categorizing GenAI applications, outlining best practices, and detailing fine-tuning methodologies, this research provides a comprehensive guide for enterprises to leverage GenAI effectively while ensuring ethical and sustainable AI deployment.

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International Journal of Scientific Research in Science and Technology

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Published

2023-07-30

Issue

Volume 10, Issue 4, July-August-2023

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Arjun Raj Bhalla "Identifying and Building Generative AI Use Cases Within Enterprise Software Products" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 10, Issue 4, pp.723-732, July-August-2023. Available at doi : https://doi.org/10.32628/IJSRST2221185