Abstract:

Artificial Intelligence (AI) integration provides among other facets of the engineering process ground-breaking opportunities to raise standards, analysis, verification, and management. Machine learning, natural language processing, and automated arguments are three examples of AI-operated technologies that enable effective demand extraction from a range of sources, detect ambiguities and discrepancies, and automate repeated operations.   These skills improve accuracy and speed of stakeholder contact by providing real-time analysis and intelligent decision aid.   Moreover reducing the risks of human mistake, improving traceability, and enabling management of changing needs are AI-powered devices.   Applying artificial intelligence in requirements engineering has various difficulties despite these advantages like ensuring the accuracy, interpretability, and ethical ramifications of solutions produced by AI.   Businesses should make use of robust verification and validation processes in order to maintain the accuracy and dependability of insight motivated by artificial intelligence. 

Additionally, continuous monitoring and adaptation are necessary to address the requirements and emerging challenges developed in software development. Engineering requires close cooperation between AI researchers, software engineers and domain experts to achieve a successful integration of AI in requirements. By constantly addressing potential risks and taking advantage of AI's strength responsibility, organization can use its full potential to increase requirements, to increase engineering practices, improve software quality and innovation in system development.