@article{4839,
  author = {Pit Pichappan},
  title = {Mapping the AI Product Ecosystem: An Empirical Analysis of Market Concentration, Organizational Diversity, and Pricing Metadata Limitations},
  journal = {Journal of Information Technology Review},
  year = {2026},
  volume = {17},
  number = {4},
  doi = {https://doi.org/10.6025/jitr/2026/17/4/179-203},
  url = {https://www.dline.info/jitr/fulltext/v17n4/jitrv17n4_1.pdf},
  abstract = {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.},
}