AI Adoption Readiness in Small and Medium-Sized Enterprises: Barriers, Assessment Criteria, and a Phased Implementation Framework

Small and medium-sized enterprises are repeatedly told that artificial intelligence is within reach, and repeatedly discover that it is not. This paper argues that the discrepancy is not explained by the dominant framings of adoption. Acceptance theory explains individual intention, and technology–organization–environment theory explains the contextual pressures on the adoption decision, but neither describes what an enterprise must have in place before an automated decision can be specified, operated and defended. Three claims are developed. First, readiness is an organizational property distributed across strategy, data, process, people and governance, rather than a property of a technology stack. Second, small and medium-sized enterprises are not scaled-down large firms: Because they cannot amortize a failed initiative, cannot run parallel workstreams, and have no dedicated data, governance or transformation function, readiness must be sequenced rather than assessed in parallel and remediated concurrently. Third, the binding constraint on adoption is usually process determinacy rather than data volume, because decision logic is typically undocumented and process variation is typically unmeasured. From these claims the paper derives three artifacts: A five-family taxonomy of adoption barriers, a Five-Domain SME AI Readiness Model with a four-level rubric across nineteen assessment criteria, and a five-phase implementation framework with explicit gate criteria. Eight propositions are stated for empirical testing. The contribution is conceptual; it has not been empirically validated, and the paper distinguishes throughout between claims that are evidenced and claims that are reasoned.