The Economic Feasibility of Using Artificial Intelligence in Software Testing for Cost Optimization and Enhancing the Competitiveness of IT Enterprises

Annotation. This article investigates the economic rationale for introducing artificial intelligence (AI) into software testing as a means of reducing operational costs and strengthening the competitiveness of IT enterprises. Against the backdrop of rising complexity in digital products and tightening time-to-market demands, conventional manual and scripted testing approaches often generate excessive expenses without guaranteeing comprehensive coverage. By contrast, the adoption of AI-driven methods – ranging from machine learning-based defect prediction and natural language processing for automated test case generation to reinforcement learning for adaptive test optimization – opens a new model of quality assurance where testing becomes a continuous, predictive, and cost-efficient process. The study explores several categories of algorithms and models that have proven to be effective in software quality management: supervised and unsupervised classifiers for bug detection, deep learning architectures for pattern recognition in large test datasets, and hybrid frameworks that combine rule-based systems with generative models to accelerate regression testing. Particular attention is given to the integration of AI-oriented orchestration platforms, which enable enterprises to balance accuracy with resource allocation, thereby cutting infrastructure costs and shortening release cycles. Methodologically, the research relies on a comparative analysis of industry reports, academic publications, and case studies of Ukrainian and international IT companies that have partially or fully adopted AI-supported testing workflows. Data sources include enterprise performance reports, benchmarking studies, and structured expert interviews. The findings demonstrate that enterprises applying AI-enhanced testing achieve measurable reductions in quality assurance budgets (on average 20–30%), along with faster detection of critical defects and higher scalability of testing teams. Beyond direct cost savings, the article emphasizes strategic advantages such as improved market reputation, increased client retention, and stronger positioning in outsourcing markets. The study concludes that AI-based testing is not merely a technological novelty but an economically justified business strategy for IT enterprises operating in conditions of global competition and resource constraints.