Implementing AI-Driven Test Automation Frameworks in Enterprise Digital Transformation Projects

Abstract

The growing Complexity of the enterprise applications and the speed at which the digital transformation is taking place has rendered the traditional testing methods ineffectual, and this has necessitated intelligent automation solutions. The significance of this study is that currently organizations rely on AI-based tools to guarantee the quality of software, lessen the number of manual data flows, and facilitate constant delivery. This paper discusses the application of AI-based test automation in efficiency and scalability of enterprise systems. The use of artificial intelligence (AI) and machine learning (ML) is demonstrated to reduce the maintenance issue and enhance the detectability of defects and script stability. The main methods of test optimization, automatic test generation, and intelligent prioritization are discussed to show how they can simplify the work of testing and enable faster releases. The combination of AI and CI/CD pipelines also allows the processes of constant validation, fast deployment, and automatic failure analysis. The paper also identifies such challenges as prioritization, issues of collaboration, cost of maintenance, and fragmentation of devices. All in all, the results of the research point to the idea that AI-based test automation is a strategic enabler of enterprise agility, operational resilience, and sustainable digital development.