Incorporating Artificial Intelligence in Small Businesses: A Practitioner-Oriented Literature Review (2020–2025)

Abstract

Artificial intelligence (AI) adoption among small businesses has accelerated since 2020, but success has not been consistent among users. A few constraints recur: limited skills, weak data readiness, and thin governance capacity. This practitioner-oriented narrative review draws together peer-reviewed, policy, and practitioner evidence published between 2020 and 2025 to examine how small firms are adopting AI, where the value lies, and which barriers persist. The evidence base comprises 20 sources—academic studies, institutional and policy reports, and practitioner-oriented material—organized thematically and supplemented by anonymized illustrative observations from the author’s professional experience (the Method section provides the selection criteria and source profile). Small firms most often use AI for customer engagement, marketing content creation, and administrative automation, and they report productivity gains when tools are paired with clear workflows and human oversight. Beyond that, outcomes vary, reflecting differences in readiness and complementary assets. Limited skills and weak data readiness are the barriers reported most consistently. This review distinguishes AI from related technologies, maps adoption across functional areas, and consolidates the evidence into an incremental adoption framework: thin-slice use cases, minimum viable governance, hybrid skill development, and sequenced investment. The contribution is translational; it connects what the research reports to what a resource-constrained firm can act on.