Overview
In today’s digital economy, ensuring software quality is more critical than ever. Gartner reports that 91% of organizations are pursuing digital initiatives and 87% rank digitalization as a top priority. Yet many digital transformations falter: Deloitte notes that as many as 70% of these efforts fail, underscoring the need for robust quality assurance at every phase. Quality Engineering (QE) now drives competitive advantage, and analysts emphasize that QE metrics must align with business outcomes, making quality a strategic investment rather than a mere checkbox. In practice, this means measuring QA by its impact on customer satisfaction, uptime, and compliance, not just by counting tests or defects.
Into this environment now steps-in generative AI. McKinsey estimates that GenAI could add $2.6–$4.4 trillion to the global economy by accelerating development and improving product quality. This potential is especially relevant to software: Twilio’s Chief Product Officer notes that AI will bring “improvements in the quality of products” through deeper analysis and smarter recommendations. Industry surveys confirm that QA is at the forefront of this change. For example, a 2024 Tricentis survey found that 60% of DevOps practitioners consider testing the most valuable area for AI investment, and about one-third expect AI tools to save them over 40 hours per month. These findings illustrate that GenAI is already delivering concrete efficiency gains in QE.
In this whitepaper we cover:
- How QualiZeal’s QMentisAI platform is transforming QE across industries
- Challenges organizations face and QMentisAI resolves them.
