Generative AI Has Shifted to General‑Purpose Functionality Modern generative AI models now perform a wide array of tasks, unlike earlier predictive AI that focused on narrow predictions, making it difficult to form a reliable picture of how they are employed across sectors [1].

Current Industry Reports Contain Fragmented and Incomplete Usage Data Academic, policy, and provider studies on generative AI usage appear increasingly, yet the data remain incomplete, ambiguous, and often lack methodological detail, limiting their analytical value [1].

Integrative Review Produces Multi‑Dimensional Reporting Framework Researchers conducted an integrative review to construct a framework that specifies which information about generative AI use should be reported and how, aiming to standardize disclosures and enhance analytical utility [1].

Application to Over 110 Documents Reveals Systematic Omission Patterns Applying the framework to more than 110 industry reports uncovered recurring gaps, indicating that existing reporting fails to capture many deployment aspects; the authors call for standardized, methodologically specific reporting to prevent skewed narratives [1].