As enterprises integrate AI into their business processes, ensuring AI systems’ quality and reliability is a significant challenge. Quality Engineering (QE) is the cornerstone across the entire AI adoption lifecycle, from assessing AI readiness to ensuring robust implementation and scaling efforts. However, traditional QE functions struggle to address the unique AI-specific assurance challenges, such as data bias, ethical considerations, and continuous performance monitoring.

In this report, we explore how enterprises can evolve their QE capabilities to address AI systems’ complexities and ensure successful AI adoption. The report outlines a transformative framework, Quality@360⁰, focusing on advancing the skillset, mindset, and toolset needed for end-to-end AI assurance. Enterprises that proactively adapt their QE functions to meet AI’s demands will realize increased value from their AI initiatives and achieve long-term business success.

Scope

All industries and geographies

Contents

In this report, we:

• Examine the current enterprise AI adoption status
• Analyze the strategic confluence of AI and the quality function
• Assess levers to evolve the quality function to take on the AI assurance mandate

Memberships

Application Services

Sourcing and Vendor Management