Moving Beyond "Does AI Work" to "Does AI Work at Scale, for Whom, and Under What Conditions?"
DOI:
https://doi.org/10.68050/JAMS.2026.757Keywords:
Artificial Intelligence, Scalability, Equity, Socio-Technical Systems, Digital TransformationAbstract
This study examined the transition from pilot AI implementations to scalable, equitable deployment by addressing the question: Does AI work at scale, for whom, and under what conditions? Guided by Socio-Technical Systems Theory and the Technology Acceptance Model, the study pursued four objectives: assess AI scalability, analyze distributional impacts, identify enabling and constraining conditions, and develop a framework for evaluating AI at scale. A mixed-methods explanatory sequential design was employed across 12 public and private institutions in Sub-Saharan Africa. Quantitative data were collected from 210 managers and frontline staff using structured surveys, while qualitative data were obtained from 28 semi-structured interviews with AI project leads and policymakers. Data was analyzed using SPSS for descriptive and inferential statistics and NVivo for thematic analysis. Findings show that AI improved efficiency by 35-50% and accuracy by 28-42% in pilot phases, but performance declined by 18-25% at scale due to infrastructure gaps, data fragmentation, and skills shortages. Benefits were uneven: urban, well-resourced units gained most, while rural and under-resourced units faced exclusion. Key enabling conditions included interoperable data systems, leadership commitment, workforce capacity, and clear governance. The study concludes that AI’s value at scale depends on socio-technical alignment and equity. Recommendations include adopting phased scaling, embedding equity assessments, strengthening infrastructure and skills, and using multi-dimensional evaluation frameworks.
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