AI Adoption, Business Intelligence, and Small-Firm Productivity: A Comparative Sectoral Analysis in the United States
DOI:
https://doi.org/10.51699/cajitmf.v4i10.1121Keywords:
Artificial Intelligence, SMEs, Labor Productivity, Generative AI, Sectoral Analysis, Business Intelligence, Entrepreneurship, Policy GovernanceAbstract
This article investigates the effects of artificial intelligence (AI) adoption, particularly generative AI on the productivity, entrepreneurship, and labor outcomes of small firms across U.S. industries. Drawing on national data sources and a novel AI Exposure Index, the paper applies a comparative methodology to evaluate differences in performance metrics across highly exposed and less exposed sectors. The study focuses on real output per worker, job creation, and firm formation patterns from 2020 to 2024, offering both a temporal and sectoral perspective. Findings indicate a positive association between AI adoption and labor productivity, as well as between AI adoption and new firm formation in AI-relevant fields such as professional services and retail. In addition to presenting quantitative evidence, the article explores microenterprise use cases and emerging workforce trends. A tailored governance model is also proposed, grounded in the NIST AI Risk Management Framework, along with sector-specific insights and policy recommendations for ethical AI deployment in small businesses. These findings offer actionable insights for policymakers, entrepreneurs, and researchers seeking to understand the evolving intersection between AI technologies and small-business ecosystems in the digital era.
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Copyright (c) 2023 Malik Normuradov
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