Author
Abstract
This article examines the transformative impact of artificial intelligence on Customer Relationship Management (CRM) systems through the integration of cloud architecture, system integration, and process automation capabilities. It explores how cloud-based CRM platforms provide scalable infrastructure supporting advanced functionality while eliminating traditional deployment barriers. The discussion addresses critical integration challenges and methodologies, creating unified customer data ecosystems across enterprise applications. Particular attention is given to AI applications, including natural language processing for conversational interfaces, machine learning algorithms for lead scoring, and predictive analytics for customer behavior modeling. The article presents implementation frameworks balancing automation efficiency with appropriate human intervention points, highlighting organizational considerations beyond technical requirements. Case examples illustrate successful deployments across industries, including healthcare, financial services, and manufacturing, demonstrating business impact through enhanced customer experiences and operational efficiencies. Governance frameworks ensuring ethical AI implementation and data quality maintenance are examined alongside future development directions. In addition to identifying ongoing learning opportunities in this quickly developing field, the thorough investigation offers practitioners implementation advice.
Suggested Citation
Pradeep Kiran Veeravalli, 2025.
"AI-Driven CRM: Integration of Cloud Architecture and Intelligent Automation in Enterprise Customer Management,"
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(5), pages 291-307, October.
Handle:
RePEc:jbh:ijsrcs:v11:y2025:i5:id:1741
DOI: 10.32628/CSEIT251117132
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT251117132
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jbh:ijsrcs:v11:y2025:i5:id:1741. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Pankaj Sharma (USA) (email available below). General contact details of provider: https://ijsrcseit.com/home .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.