Enhancing SAP S/4HANA and Salesforce Quality Assurance & Quality Control through AI-Driven Test Automation Frameworks for Regulatory Compliance

Abstract

Both enterprise resource planning and customer relationship management systems are under increasing pressure to ensure that they remain compliant with their regulations and provide efficient quality assurance processes. The outdated testing models are not capable of keeping up with the dynamics of current enterprise platforms, especially those that are closely regulated, such as the healthcare and medical device manufacturing industries. In this article, an AI-based test automation framework is introduced to support quality assurance and quality control processes during the implementation of SAP S/4HANA and Salesforce by means of machine learning algorithms and natural language processing. It has an intelligent test case generation, predictive risk evaluation, self-healing test scripts, and an automated compliance checking framework. Case studies of implementations show that efficiency of testing, detection of defects, and coverage of regulatory compliance have been greatly enhanced in the financial services and healthcare sectors, as well as within the medical device manufacturing settings. The framework considers major technical issues such as data quality management, the complexity of system integration, and regulatory validation requirements with the help of modular architecture and standard protocols of interfaces.