Prompt-Driven Integration Workflow Generation: A Technical Analysis - Rajesh Vasa (International Journal of Computational and Experimental Science and Engineering, 2026)
Generative AI systems have been incorporated into innovation and process optimization in organizations. Automated orchestration, code generation, and generative creativity have been introduced as well. With the introduction of generative AI, organizations should consider governance and quality management. Measurable productivity, defect, and system reliability improvements have been achieved using large language model capabilities with structured enterprise integration platforms. Modern engineering design, content generation, multimedia creation, and scientific experimentation conducted by organizations show that meaningful savings in development time, configuration accuracy, and productivity can be realized by the systematic integration of artificial intelligence technologies into existing quality management systems. Artificial intelligence integration requires established governance frameworks, human-in-the-loop verification, and explainable artificial intelligence to ensure compliance with the organization's values and legislation. Combining theoretical constructs, empirical evidence, and practical applications, an agnostic model is formed, enabling organizations to have the accountability, operational and human oversight needed to embrace responsible AI-enabled automation of enterprise systems and processes.