From Pilot to Procurement: Institutional Conditions for Scaling Public-Sector AI in Kazakhstan with Comparisons to Azerbaijan and South Korea

Authors


DOI:

https://doi.org/10.52536/3006-807X.2026-3.002

Keywords:

public procurement for innovation, artificial intelligence, pilot scaling, Kazakhstan, Azerbaijan, South Korea

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.

Author Biography

  • Elliott Ahn, Gwangju Institute of Science and Technology (GIST), Republic of Korea

    Elliott Ahn is a Distinguished Professor at the Graduate School of AI Policy and Strategy, Gwangju Institute of Science and Technology (GIST), Republic of Korea, and a member of the Expert Board of the ASAN AI Hub in Azerbaijan, where he advises on public-sector AI adoption and procurement. He also serves as Executive Director at Antler IBEX, a global venture builder, and advises startup programs for JETRO Osaka, the Okinawa Institute of Science and Technology (OIST), Gyeonggi Province, and Korea's Ministry of SMEs and Startups. His research examines the institutional conditions for scaling public-sector AI, public procurement for innovation, and digital governance in Central Asia and the Caucasus, together with natural language processing for Turkic languages. His recent publications include an essay on Azerbaijan's post-hydrocarbon development model in Baku Dialogues (ADA University, Summer 2026), a software paper on USD-NLP, a toolkit for multilingual NLP pipelines, in SoftwareX (Elsevier, 2026), and a policy brief for the OSCE Academy in Bishkek on state-language AI in public procurement. Before entering academia, he held corporate finance and M&A roles at Hyundai Motor, SK Telecom, and Nissan Motor. He holds a PhD from Kwangwoon University, an LL.M. from Georgetown University Law Center, and an MBA from Doshisha University. 

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Published

2026-09-29

How to Cite

Ahn, E. (2026). From Pilot to Procurement: Institutional Conditions for Scaling Public-Sector AI in Kazakhstan with Comparisons to Azerbaijan and South Korea. Journal of Central Asian Studies, 24(3), 27-57. https://doi.org/10.52536/3006-807X.2026-3.002