Application of Artificial Intelligence in Contemporary Business: An Analysis for Content Management System Optimization

Authors

  • Mounika Mandapuram Cognizant Technology Solutions, Teaneck, New Jersey, USA

DOI:

https://doi.org/10.18034/abr.v7i3.650

Keywords:

Artificial Intelligence, CMS Optimization, Contemporary Business Environment, MIS

Abstract

Modern business is vital to finance, and AI has revolutionized the modern industry. Automation of many business operations has raised global concerns and alarms. Current business and related jobs are rising dramatically as the global population grows. Business administrators' conventional methods could be more efficient for these needs. These new tactics properly manage business products and services so industry persons may use technology to boost profits. It has protected harvest yield from environmental changes, overpopulation, changing business demands, and food safety challenges. Artificial intelligence can promote intelligent production methods to reduce loss and increase returns. Using artificial intelligence platforms, one can collect a considerable amount of data from government and public sites or real-time monitoring and collection of different data using IoT (Internet of Things) and then use it to empower business people to solve all their business problems. This research helps business people worldwide improve their business techniques. This paper uses the waterfall technique to develop and build an intelligent system by sequentially collecting data, analyzing requirements, planning, coding, testing, and implementing. This system can also generate ideas for managing common challenges in farm information systems, improving policy programs, augmentation and analysis, and managing production data. Finally, management information systems are analyzed, and suggestions for further development are made.

Downloads

Download data is not yet available.

References

CEMA. Digital Farming: What Does It Mean? Available online: http://www.cema-agri.org/publication/digital-farming-what-does-itreally-mean (accessed on 17 September 2019).

European Commission. (2012). Generational Renewal in EU Agriculture: Statistical Background; DG Agriculture Rural Development: Economic analysis of EU agriculture unit: Brussels, Belgium, pp. 1–10.

Fadziso, T. (2017). Understanding the Unending Learning Language Technique. Asian Journal of Humanity, Art, and Literature, 4(2), 141-146. https://doi.org/10.18034/ajhal.v4i2.560

FAO. (2017). Food and Agriculture Organization of the United Nations, 2017. THE STATE OF FOOD AND AGRICULTURE LEVERAGING FOOD SYSTEMS FOR INCLUSIVE RURAL TRANSFORMATION. 978-92-5-109873-8pp. 1–181

Kucera, M., and Lˇ ate´ ckovˇ a, A. (2006). Information and knowledge systems in the operation of agricultural and food-processing enterprises. Agricultural Economics – Czech, 52, 353-357.

Mandapuram, M. (2016). Applications of Blockchain and Distributed Ledger Technology (DLT) in Commercial Settings. Asian Accounting and Auditing Advancement, 7(1), 50–57. Retrieved from https://4ajournal.com/article/view/76

Silerovˇ a, E., and Lang, K. (2006). Information systems - tool for changing our future, Agricultural Economics – Czech, 52, 447-450.

Downloads

Published

2017-12-31

How to Cite

Mandapuram, M. (2017). Application of Artificial Intelligence in Contemporary Business: An Analysis for Content Management System Optimization. Asian Business Review, 7(3), 117-122. https://doi.org/10.18034/abr.v7i3.650

Similar Articles

1-10 of 167

You may also start an advanced similarity search for this article.