Evaluating entity-centric AI systems presents significant challenges, particularly when there is no ground truth available for comparison. These systems, which are commonly employed in business contexts such as data integration and information retrieval, require specialized methods to assess their performance accurately. The absence of definitive reference data complicates traditional evaluation approaches, necessitating alternative strategies that can provide reliable measurements despite this limitation. Recent discussions in the field highlight the importance of variance-bounded evaluation techniques, which aim to quantify uncertainty and improve the robustness of assessments. By focusing on entity-centric tasks, researchers address practical applications where AI systems must reconcile and interpret complex data without explicit validation benchmarks. This ongoing work contributes to a deeper understanding of how to measure AI effectiveness in real-world scenarios where ground truth is often unavailable or incomplete. Consequently, these advancements support more informed deployment and refinement of AI technologies in critical business operations.