Artificial Intelligence in United States Enterprises: An Integrative Review of Adoption Patterns, Sectoral Applications, and Organizational Outcomes

• DOI: https://doi.org/10.22533/at.ed.1317682629062

• Palavras-chave: ...

• Keywords: artificial intelligence; generative AI; enterprise adoption; firm productivity; technology diffusion; AI governance; United States

• Abstract: Abstract Artificial intelligence has crossed a threshold in American business. In just a few years it moved from a research curiosity to something firms now treat as ordinary infrastructure for producing goods, serving customers, and competing. This article reviews how AI is actually used in United States enterprises, drawing together peer-reviewed research, nationally representative government statistics, and large industry surveys published through early 2026. Three questions drive the analysis. How widely, and how evenly, has AI spread across American firms? Where is it producing measurable value? And what conditions decide whether that value materializes? The evidence splits sharply. Surveys put self-reported use above three-quarters of organizations, while nationally representative firm-level data place production-grade adoption in the high single digits, closer to a fifth once weighted by employment. Real deployments concentrate in a handful of places: customer service, marketing, software engineering, financial risk management, clinical and administrative healthcare, and factory quality control. Field experiments keep finding double-digit productivity gains, and those gains fall mostly to less-experienced workers. Yet most firms report no effect on profits. The review reads this gap through general-purpose-technology theory and the complementary-intangibles hypothesis, and locates it within an unsettled United States governance landscape. Implications for managers, policymakers, and future research follow.