1 Cloud Architect
2 Pace University, New York, NY, USA.
World Journal of Advanced Engineering Technology and Sciences, 2025, 15(01), 1931-1950
Article DOI: 10.30574/wjaets.2025.15.1.0431
Received on 13 March 2025; revised on 01 April 2025; accepted on 04 April 2025
Generative AI (genAI) is altering how enterprises work by automating creative tasks, speeding up innovation, and enhancing decision-making in several industries. Traditional AI learns patterns from the massive data to create new content; GenAI does the same but pushes further ahead to develop new solutions. Its scalability, flexibility, and cost efficiency are multiplied by public cloud infrastructure, and businesses can quickly and at scale deploy AI solutions. Tech companies are ready to integrate GenAI services in major cloud providers like AWS, Azure, and Google Cloud for apps such as personal marketing, intelligent customer service, predictive maintenance, advanced R&D, and more, to be done in real-time, with massive storage, and democratized access to AI toolkits in a cloud-native environment to allow cross-functional teams to co-innovate. As case studies demonstrate, GenAI is already making a dramatic impact in boosting productivity, agility, and competitive advantage in finance, healthcare, retail, and manufacturing. Still, the impediments to the enterprises include data privacy, systems integration, and talent gaps. Ethical AI governance and monitoring models all the time will do the job of bringing sustainable adoption. This is where the enterprises on the board of AI evolution will be at the forefront of the digital age with their cloud-native, ethical, and people-coupled AI strategy.
Generative AI; Public Cloud Computing; Enterprise Digital Transformation; AI-Powered Automation; Scalable Cloud Infrastructure
Preview Article PDF
Piyush Patil. Generative AI in Enterprise: Transforming processes across industries adopting public cloud. World Journal of Advanced Engineering Technology and Sciences, 2025, 15(01), 1931-1950. Article DOI: https://doi.org/10.30574/wjaets.2025.15.1.0431.