Journal of Information Technology Review Pit Pichappan 17 4 2026 https://doi.org/10.6025/jitr/2026/17/4/179-203 https://www.dline.info/jitr/fulltext/v17n4/jitrv17n4_1.pdf The rapid proliferation of artificial intelligence has created a complex ecosystem of products, platforms, and organizations, yet empirical understanding of its structural characteristics remains limited. This study empirically maps the observed AI product ecosystem by examining 3,027 product records, focusing on market concentration, organizational diversity, startup-product relationships, and pricing metadata completeness. The analysis reveals a dual structural pattern. Within the subset of products with identifiable organizational relationships (42 products across 17 startups), product representation is highly concentrated: four companies account for 57.14% of linked products, with a Herfindahl Hirschman Index of 1,281 and a Gini coefficient of 0.434. However, the ecosystem simultaneously demonstrates organizational diversity, with a Shannon diversity index of 2.450 and Simpson diversity measure of 0.872. Critically, the study identifies substantial metadata limitations: only 1.39% of product records contain identifiable company associations, while 97.72% lack explicit pricing classifications. These findings demonstrate that ecosystem structure cannot be assessed independently of data quality observed concentration among major companies coexists with broad organizational participation, but incomplete entity linkage and pricing metadata severely constrain inference. The study contributes methodologically by distinguishing between substantive ecosystem characteristics and data quality artifacts, and empirically by characterizing the AI product ecosystem as organizationally diverse yet product concentrated. Results highlight the need for improved entity resolution, standardized pricing taxonomies, and multi source data integration to support robust future analyses of the evolving AI product landscape.