International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Harnessing Artificial Intelligence for Salesforce Metadata: Advanced Migration Strategies and Strategic Business Benefits |
|---|---|
| ABSTRACT | Artificial intelligence is an expanding speciality of computer science concentrated on developing state-of-the-art structures and algorithms to automate complex tasks. Salesforce configurations are becoming more and more complex; therefore, the demand for a rapid and accurate impact analysis increases. This supports the contention that manual methods for identifying changes cannot provide the advantages mentioned earlier since the manual impact analysis takes time and is prone to manual errors. Artificial intelligence can be applied to automate the process of identifying changed data in Salesforce. Thus, the use of artificial intelligence can increase the capability for quicker, accurate impact analyses while decreasing the amount of manual effort typically needed for both ease of audits and compliance. Artificial intelligence also allows for analysis of vast historical datasets, enabling a more accurate identification of changes while reducing false-positives. A significant advantage of using artificial intelligence is enhanced optimization of regression testing using intelligent dependency mapping, leading to consistent impact analyses using artificial intelligence, while enabling a quality check of the impact analyses with domain expertise. Quicker releases with lower operational costs can result. Artificial intelligence also provides robust solutions that are always learning based on the input of the user, although for organizational operational to be met, data intensive enterprise-specific models need often to be built. Consequently, AI systems related to Salesforce configurations are advancing more and more to provide sophisticated and efficient impact analysis and automation capabilities. |
| AUTHOR | BIJAL LALITKUMAR DAVE Full Stack Lead, Istream Solution, USA |
| VOLUME | 164 |
| DOI | DOI: 10.15680/IJIRCCE.2024.1212107 |
| pdf/107_Harnessing Artificial Intelligence for Salesforce Metadata Advanced Migration.pdf | |
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