Why AI Adoption In Quality Engineering Needs A Tailored Strategy [Forbes]
CAPGEMINI SE UNSP/ADR (CGEMY)
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Source: Forbes
Retail customers today demand seamless experiences, and quality engineering (QE) teams play a crucial role in delivering on those expectations. A robust QE team, for instance, can enhance scalability during peak periods, support the adoption of emerging technologies and protect customer trust by preventing outages and securing data. Yet, traditional QE processes are struggling to keep up with the complexity and scalability required in modern retail environments. To address these challenges, many retail companies are turning to AI-powered solutions to support their QE efforts. Many QE teams struggle with AI adoption, however, because they look at AI adoption as only a cost-cutting measure. They also adopt generic, one-size-fits-all AI solutions that aren't tailored to the specific pain points of QE teams. Let's look at those challenges more in-depth, and then explore how QE teams can integrate agentic and generative AI in ways that unlock new opportunities, enhance human product
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