Kazakhstan can use AI to help diversify beyond hydrocarbons, but only if responsible governance is treated as foundational rather than an afterthought; the authors propose an eight-layer RAI-ED framework and phased roadmap linking national strategy, digital foundations, and sectoral transformation to long-term economic outcomes.

Main Finding

The paper develops a context-specific, conceptual Responsible AI Governance Framework for AI-Enabled Economic Diversification (RAI-ED) for Kazakhstan. It argues that responsible AI governance must be treated as a foundational, sequenced enabler of digital transformation and economic diversification—rather than a compliance add-on—and presents an eight-layer framework plus a phased implementation roadmap and policy recommendations for government, industry, and higher education.

Key Points

• Problem context

  • Kazakhstan is highly resource-dependent (hydrocarbons, mining) and faces the classic diversification challenge (Dutch disease, volatility).
  • National strategies (Digital Kazakhstan, Concept for AI 2024–2029) and recent institutional moves (Ministry of AI and Digital Development established 2025; Law No. 230‑VIII on AI in force Jan 2026) show strong political commitment but governance specification lags strategic ambition.

• RAI-ED framework (central contribution)

  • Composed of eight interdependent layers:
  • National strategic context
  • Digital foundations (connectivity, data infrastructure, compute)
  • Governance principles (responsible-AI norms adapted to context)
  • Institutional readiness (regulatory bodies, inter-ministerial coordination)
  • AI technologies (platforms, models, systems)
  • Sectoral transformation (energy, mining, agriculture, manufacturing, logistics, finance, healthcare)
  • Economic outcomes (productivity, exports, employment, SME growth)
  • Long-term national impact (diversification, resilience, inclusive growth)
  • Emphasises sequencing: build digital foundations and institutional readiness in parallel with governance; governance cannot be postponed until “infrastructure is ready.”

• Governance design observations

  • Kazakhstan’s Law No. 230‑VIII adopts a risk-tiered classification (minimal/medium/high), broadly analogous to the EU AI Act in logic, but the paper notes that international governance instruments require adaptation to Kazakhstan’s institutional and resource-dependent context.
  • The framework argues for embedding responsible-AI principles into policy and procurement to steer investment toward diversification goals.

• Sectoral levers where AI can drive diversification

  • Energy: predictive maintenance, smart grids, extraction optimisation.
  • Mining: autonomous/semi-autonomous operations, digital twins, predictive analytics.
  • Agriculture: precision agriculture (satellite/drone monitoring), water management, yield prediction.
  • Manufacturing: Industry 4.0, machine-vision quality assurance for export readiness.
  • Logistics: supply-chain optimisation and intelligent transport across Eurasian corridors.
  • Financial services: AI credit scoring, fraud detection, fintech to deepen finance for SMEs.
  • Healthcare: clinical decision support, telemedicine, image diagnostics to extend services.

• Readiness and gaps

  • Mixed readiness: good e-government (UN E-Government Rank #24 in 2024), improved connectivity, but limited advanced AI talent and R&D capacity; Oxford Insights Government AI Readiness Index: #60 in 2025.
  • AI venture investment grew (≈USD 75M in 2025 vs USD 14M in 2023) but remains modest.
  • Critical gaps: data quality/interoperability, regional connectivity and skills distribution, domestic R&D/venture ecosystem maturity, and under-specified operational governance mechanisms in strategy documents.

• Practical items

  • Offers a phased implementation roadmap and concrete policy recommendations addressing regulation, public procurement, sovereign data/compute platforms (National AI Platform / Smart Data Ukimet), skills/upskilling, university–industry links, and measurement/evaluation.

Data & Methods

• Approach: conceptual synthesis (no primary empirical data collection).
• Sources integrated:
  • National strategy and legal documents (Digital Kazakhstan, Concept for AI 2024–2029, Government Resolution No. 592, Law No. 230‑VIII).
  • Institutional developments and programmes (Ministry of AI and Digital Development, National AI Platform, Accessible Internet programme, Astana Hub).
  • International responsible-AI governance instruments and literature (EU AI Act design logic, OECD/UN/G20 guidance, and recent academic responsible-AI literature).
  • Technology-adoption and institutional theory literatures to structure sequencing and institutional-readiness arguments.
  • Secondary indicators and reporting (Oxford Insights Government AI Readiness Index, UN E-Government Index, venture-investment reports).
• Methodological stance:
  • Deliberately conceptual—synthesises policy documents, governance instruments, and theory to build an applied framework suited to Kazakhstan and similar resource-dependent emerging economies.
  • Limitations acknowledged: empirical evaluation of AI-driven diversification outcomes is not yet feasible given early-stage adoption and lack of comprehensive outcome data; the paper proposes an empirical research agenda to follow.

Implications for AI Economics

• Governance as an economic enabler

  • Responsible governance affects adoption rates, firm investment decisions, and the direction of AI-enabled innovation (e.g., whether AI capacity is channelled toward tradable, exportable services or captured within rent sectors).
  • Calibration of regulation (e.g., risk-tiering) shapes compliance costs and can either accelerate or constrain firm-level experimentation—thus affecting productivity returns and diffusion speed.

• Public goods and infrastructure economics

  • Data infrastructure, sovereign compute, and interoperable government data are public goods that lower private-sector entry costs for AI-enabled services; underinvestment creates coordination failures reducing aggregate returns to AI.
  • Strategic public procurement and sovereign platforms (National AI Platform) can kickstart local markets and create demand-side complementarities; these are policy levers to correct weak private-market signals in early-stage ecosystems.

• Distributional and labour-market effects

  • AI-driven diversification can create high-value employment in new sectors, but also poses displacement risks in legacy extractive sectors. Active reskilling and university curricula alignment are essential to capture net gains.
  • The sequencing of skills development matters: mismatched skill supply will bottleneck potential gains even when capital and platforms are available.

• Measurement and evaluation needs

  • Traditional productivity metrics may undercount AI-driven service exports and non-GDP benefits (quality-of-service, resilience). AI economics research should develop sector-specific metrics linking AI adoption to diversification outcomes (exports, firm entry, value-added composition).
  • Natural experiments (phased policy rollouts, procurement pilots, regional connectivity upgrades) and panel firm-level data will be crucial to estimate causal impacts of AI governance and infrastructure investments.

• Policy design and comparative calibration

  • International governance templates (EU, OECD) are not plug-and-play: regulatory design must be adapted to Kazakhstan’s institutional capacity, enforcement resources, and diversification objectives.
  • Comparative Central Asian studies are needed to understand regional spillovers, cross-border data flows, and coordinated governance approaches.

• Research agenda signposts for AI economics

  • Empirically estimate AI’s contribution to sectoral productivity and export diversification using firm and sector panel data.
  • Evaluate the economic impact of risk-tiered regulation (Law No. 230‑VIII) on innovation, compliance costs, and market entry.
  • Analyse optimal sequencing of public investments (data platforms, connectivity, skills) to maximise private-sector AI adoption and diversification.
  • Study distributional effects across regions and demographic groups to inform equity-focused policy design.

Overall, the paper reframes responsible AI governance as a strategic economic policy instrument for resource-dependent emerging economies: a governance design that is sequenced, locally adapted, and embedded in diversification strategy improves the prospects that AI will generate broad-based, sustainable economic transformation rather than narrow efficiency gains.