{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T06:36:42Z","timestamp":1793083002097,"version":"4.5.0"},"reference-count":36,"publisher":"AOSIS","issue":"1","license":[{"start":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T00:00:00Z","timestamp":1793145600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Background: Small- and medium-sized enterprises (SMEs) are increasingly experimenting with artificial intelligence (AI), yet many struggle to translate early use into scalable and trusted organisational value.\nObjectives: This study aimed to identify the conditions that enable or constrain SME AI adoption and assimilation, explain how responsible AI governance shapes progression towards trusted and commercially durable use and interpret the implications for market access and cash-flow-relevant value in Cape Town and the Western Cape.\nMethod: A PRISMA 2020-guided scoping review synthesised global peer-reviewed studies published from January 2021 to December 2025. Database searching and citation chaining identified 486 records, of which 72 met the inclusion criteria. Cape Town and the Western Cape were used as an interpretive application lens rather than as the empirical study setting.\nResults: Small- and medium-sized enterprise AI adoption and assimilation were shaped by technological readiness, organisational capability, environmental support and governance quality. Trusted data, interoperable systems, leadership commitment, hybrid skills and process discipline supported routinised use. These capabilities enabled service responsiveness, forecasting and customer engagement that may strengthen market access and cash-flow visibility. Fragmented data, skills scarcity, vendor dependence, low trust and regulatory uncertainty constrained scaling.\nConclusion: Responsible AI governance functions not as a downstream compliance add-on but as a practical scaling mechanism through which SMEs reduce uncertainty, preserve trust and convert pilots into repeatable value.\nContribution: The study positions responsible AI governance as an information management capability that shapes data stewardship, decision traceability and institutional trust in SME AI adoption.<\/jats:p>","DOI":"10.4102\/sajim.v28i1.2225","type":"journal-article","created":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T06:17:39Z","timestamp":1793081859000},"update-policy":"https:\/\/doi.org\/10.4102\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Scaling small- and medium-sized enterprise artificial intelligence through governance: A Technology\u2013Organisation\u2013Environment readiness synthesis for information 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