AI BENCHMARKING IN BUSINESS

Abstract

This paper presents a concise review of current AI benchmarks relevant to business applications, focusing on their design, evaluation metrics, and practical relevance. It examines how benchmarks in areas such as natural language processing, predictive analytics, and decision automation align with real-world business needs, including accuracy, scalability, fairness, and interpretability. The study highlights gaps between academic benchmarks and enterprise use cases, emphasizing the need for more context-aware and industry-specific evaluation frameworks. Ultimately, the paper aims to guide researchers and practitioners in selecting or designing benchmarks that better reflect the complex demands of AI deployment in business environments.