Achieve Complete Automation with AI and Machine Learning

Traditional automation in software testing is hitting its limits. Frequent requirement changes, manual script maintenance, and rising costs make complete automation an elusive goal.

This whitepaper by Infosys explores how Artificial Intelligence (AI) and Machine Learning (ML) can transform quality assurance into a self-learning, self-healing ecosystem — enabling testing teams to achieve true agility with minimal human intervention.

Discover practical frameworks and five proven use cases showing how enterprises can transition from fragmented automation to intelligent, end-to-end automation that drives speed, accuracy, and cost efficiency.

Key learnings and takeaways include:

• The major pain points that limit traditional automation success in QA
• Five real-world use cases where AI/ML elevate testing efficiency
• How clustering, NLP, and deep learning streamline automation test suites
• Building intelligent DevOps through predictive analytics and self-healing systems
• Strategies for achieving dynamic, self-maintaining automation frameworks
• How enterprises can future-proof their testing landscape and cut QA cycles by over 40%