FROM PILOT TO PROCUREMENT: INSTITUTIONAL CONDITIONS FOR SCALING PUBLIC-SECTOR AI IN KAZAKHSTAN WITH COMPARISONS TO AZERBAIJAN AND SOUTH KOREA

Governments increasingly test artificial intelligence (AI) through pilots, but technical success does not establish a purchaser, a lawful contract route, or operating capacity. This article examines the institutional arrangements through which public-sector AI pilots may progress to regular procurement, deployment, and capability building. It develops a five-condition framework covering problem ownership, public-data access, a post-pilot procurement pathway, evaluation and accountability, and capability transfer. A structured, focused comparison of Kazakhstan, Azerbaijan, and South Korea maps selected official instruments issued by August 2, 2026. The analysis is typological and does not estimate actual conversion rates or causal effects. It is organized around a Kazakhstan-specific puzzle: why has one of post-Soviet Eurasia’s most advanced digital states built strong infrastructure for AI experimentation without an equally specified route from trial results to procurement? Kazakhstan combines a national AI platform, data-governance rules, and an AI law with an incompletely specified route from trial evidence to budgeted acquisition in the consulted record. Azerbaijan’s 2026–2028 action plan connects sandbox integration with measures supporting innovative firms’ access to procurement, although these provisions remain prospective. South Korea provides a codified contrast through designation, pilot purchasing, and public-user evaluation; its program adjustments also recognize that initial participation does not assure later demand. The clearest difference across the selected architectures concerns the procurement pathway, the usefulness of which depends on agency ownership, reliable evidence, and accountable operation. Interpreting these arrangements in light of Kazakhstan’s administrative development and procurement political economy, the article proposes an AI Innovation Procurement Track with competition safeguards, public evaluation, and capability-transfer requirements. These proposals follow from institutional design reasoning and require legal review and subsequent empirical testing.

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Publication Details

Journal
Journal of Central Asian Studies
Published
2026-09-29
DOI
https://doi.org/10.52536/3006-807x.2026-3.002
Primary Topic
Ethics and Social Impacts of AI
Type
article
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article

FROM PILOT TO PROCUREMENT: INSTITUTIONAL CONDITIONS FOR SCALING PUBLIC-SECTOR AI IN KAZAKHSTAN WITH COMPARISONS TO AZERBAIJAN AND SOUTH KOREA

Elliott SeokHyun Ahn
Journal of Central Asian Studies
Ethics and Social Impacts of AI
article

FROM PILOT TO PROCUREMENT: INSTITUTIONAL CONDITIONS FOR SCALING PUBLIC-SECTOR AI IN KAZAKHSTAN WITH COMPARISONS TO AZERBAIJAN AND SOUTH KOREA

Elliott SeokHyun Ahn
article en

Abstract

Governments increasingly test artificial intelligence (AI) through pilots, but technical success does not establish a purchaser, a lawful contract route, or operating capacity. This article examines the institutional arrangements through which public-sector AI pilots may progress to regular procurement, deployment, and capability building. It develops a five-condition framework covering problem ownership, public-data access, a post-pilot procurement pathway, evaluation and accountability, and capability transfer. A structured, focused comparison of Kazakhstan, Azerbaijan, and South Korea maps selected official instruments issued by August 2, 2026. The analysis is typological and does not estimate actual conversion rates or causal effects. It is organized around a Kazakhstan-specific puzzle: why has one of post-Soviet Eurasia’s most advanced digital states built strong infrastructure for AI experimentation without an equally specified route from trial results to procurement? Kazakhstan combines a national AI platform, data-governance rules, and an AI law with an incompletely specified route from trial evidence to budgeted acquisition in the consulted record. Azerbaijan’s 2026–2028 action plan connects sandbox integration with measures supporting innovative firms’ access to procurement, although these provisions remain prospective. South Korea provides a codified contrast through designation, pilot purchasing, and public-user evaluation; its program adjustments also recognize that initial participation does not assure later demand. The clearest difference across the selected architectures concerns the procurement pathway, the usefulness of which depends on agency ownership, reliable evidence, and accountable operation. Interpreting these arrangements in light of Kazakhstan’s administrative development and procurement political economy, the article proposes an AI Innovation Procurement Track with competition safeguards, public evaluation, and capability-transfer requirements. These proposals follow from institutional design reasoning and require legal review and subsequent empirical testing.

Journal of Central Asian StudiesVol. 24(3)
Gwangju Institute of Science and Technology (KR)
Industry, innovation and infrastructure
Openalex Percentile: Top 8%
Ethics and Social Impacts of AI
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