40% Effort Reduction & 25% Cost Savings via GenAI-Driven Test Automation
For a Global Cosmetics Company
“Achieving operational excellence in a complex multi-cloud environment requires unified visibility, predictive insights, and proactive cost management.”
– A worried CTO at a global consumer goods company
When a global cosmetics company aimed to modernize its software testing lifecycle using automation, analytics, and GenAI, the initiative delivered measurable impact:
25%
Reduction in cost
10%
Increase in productivity
30–40%
Reduction in effort to develop test cases and automation scripts
Improved knowledge reuse and reduced SME dependency
Business Problem
However, as the organization attempted to revamp its software testing life cycle, certain working challenges impeded agility and efficiency.
Heavy reliance on SMEs resulted in knowledge transfer being time-consuming and laborious. Manual test script writing was inefficient and error-prone.
The lack of active defect trend analysis restricted the ability to prioritize testing on high-risk modules. Insufficient reuse of test assets hindered overall productivity.
There was a need for an intelligent, automated, and analytics-enabled testing environment to minimize manual effort, facilitate knowledge reuse, and promote scalable continuous improvement.
Customer Challenge
A global cosmetics company faced significant challenges in modernizing its software testing lifecycle. Heavy reliance on SMEs created knowledge silos, making transfer slow and resource-intensive.
Manual test case creation and scripting were inefficient and error-prone, limiting speed and scalability. Historical defect data was underutilized, preventing proactive identification of high-risk modules.
Limited reuse of test assets and the absence of intelligent automation increased effort, constrained productivity, and slowed delivery, leaving teams without a scalable, analytics-driven testing framework.
Movate AI Solution
A test automation and analytics framework was developed using GenAI for the modernization of the testing process. High-defect modules were identified through predictive analytics, and GenAI automatically generated test cases based on user stories and ensured compliance with automated scripts that included validation.
A Universal Bot minimized reliance on SMEs, and continuous learning updated scripts based on changes in the product for intelligent testing operations.
Movate AI Differentiator
The testing framework enabled by GenAI brought efficiency and cost savings to the software development life cycle.
The cost of testing was reduced by ~25% by optimizing redundancy, while the overall effort was reduced by 40%. The effort for developing test cases and automation scripts reduced by 30-40%, shortening the release cycle.
Productivity increased by ~10% by aligning testing to high-priority modules using predictive defect analytics. Knowledge reuse improved significantly, minimizing SME bandwidth constraints and enabling scalable, continuous improvement.
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