Implementing AI Quality Assurance for User Trust

This document is a report that discusses the critical need for AI quality assurance in organizations to enhance user trust and improve AI system performance. It outlines the current state of AI quality assurance practices, revealing that many organizations rely heavily on manual processes and lack essential capabilities for ensuring the security, reliability, and ethical compliance of AI systems. The report highlights findings from a survey conducted by Forrester Consulting, commissioned by Cognizant and Microsoft, which indicates that trust in AI systems is a significant barrier to adoption. It emphasizes the importance of robust quality assurance strategies in validating AI outputs and building user confidence. The report also details the challenges organizations face, such as limited governance and technical expertise, which hinder the establishment of effective quality assurance frameworks. Furthermore, it discusses the implications of inadequate quality assurance, including potential business risks and negative outcomes, underscoring the necessity for organizations to allocate more resources toward improving AI testability and traceability.