Reliable ground truth is the foundation of effective GenAI evaluation. Without accurate human labels, metrics like accuracy, robustness, or factuality lose meaning, leading to flawed conclusions and poor decision-making. As use cases scale and diversify, inconsistencies in annotation practices can undermine trust, skew evaluation results, and slow improvement cycles. Building and maintaining high-quality human-labeled data supports better benchmarking, tuning, and overall performance. It also enables organizations to validate automated methods, train new evaluators, and track solution quality over time.