Kazakhstan rises 16 places in the Government AI Readiness Index 2025
15:00, 25 December 2025
Kazakhstan moved up 16 positions to rank 60th out of 195 countries in the Government AI Readiness Index 2025, published by Oxford Insights. The Ministry of Artificial Intelligence and Digital Development credits the jump to stronger institutions, improved infrastructure, and real public-sector adoption of AI - not just pilots on paper.
One number stands out: 73.59 points for Public Sector Adoption. That reflects a clear push on e-government platforms and proactive services for citizens and businesses. It also confirms Kazakhstan's lead in Central Asia on AI readiness.
What moved the needle
The government has put strategy, regulation, and delivery under one umbrella. Policy documents are in place, a regulatory base is forming, and agencies are actually deploying AI in service delivery. This combination is how you convert strategy into measurable outcomes.
The latest index uses an updated methodology that weighs the state's full role - from planning and standards to implementation and economic impact. In that context, Kazakhstan's rise suggests the basics are getting done: governance, talent, infrastructure, and projects that touch real users.
Where the opportunity is next
The ministry noted the next frontier is outside the walls of government: commercialization, an active startup scene, and broader market adoption. That means easier access to data (with safeguards), better financing tools, and faster paths from prototype to production. It also means more private-sector participation in public delivery.
What practitioners in government, IT, and development can do now
- Target high-friction services first. Licensing, permits, inspections, customs, taxation, social services - define 6-12 month pilots with clear KPIs (processing time, accuracy, satisfaction, cost per case).
- Modernize procurement. Use outcome-based contracts, pre-qualified AI vendor pools, and lightweight sandbox agreements. Require security-by-design, audit logs, model documentation, and clear data handling terms.
- Strengthen data foundations. Stand up shared data catalogs, implement role-based access, and publish stable APIs for non-sensitive datasets. Where needed, use privacy-preserving techniques and synthetic data for testing.
- Standardize delivery. Provide a common MLOps platform, model registry, and evaluation pipeline across agencies. Plan GPU/accelerator capacity and a cloud posture that balances sovereignty, security, and cost.
- Adopt practical governance. Align programs with the NIST AI Risk Management Framework, set up risk tiers, human oversight rules, incident reporting, and regular red-teaming and bias testing.
- Invest in skills. Create standardized AI role families (product, data, ML engineering, evaluation, policy), offer cross-agency training, and enable short-term secondments with universities and industry.
- Fund what works. Use innovation challenges, matched funding, and outcomes-based financing for pilots that hit KPI targets. Reserve a share of contracts for SMEs and startups to accelerate commercialization.
- Collaborate regionally. Build shared benchmarks and sandboxes across Central Asia to reduce duplication and support interoperability.
Signals to watch in 2026
- Operational AI in high-volume services (tax audits, customs risk scoring, permit triage) with measurable cycle-time reductions and fewer backlogs.
- Progress on a national compute strategy and support for multilingual models serving Kazakh and Russian speakers.
- Growth in startup deal flow and a higher share of public procurement awarded to innovative SMEs.
- More high-quality open datasets, consistent evaluation reports for deployed models, and published guardrails for safety and privacy.
- Citizen and business satisfaction trends, not just internal efficiency metrics.
Why this matters for policy and delivery
Moving up the index is useful, but the real value is compound productivity across services, better policy execution, and trust. With governance capacity in place, the next step is to scale proven use cases, open responsible data flows, and create a predictable path for private solutions to plug into public delivery.
The recent appointment of a First Deputy Minister of Artificial Intelligence and Digital Development signals institutional ownership. That's the kind of structure that keeps momentum - and accountability - intact.
Read more
Oxford Insights: Government AI Readiness Index
NIST AI Risk Management Framework
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