The platform has summarized discussions on the country’s readiness for artificial intelligence implementation, addressing computational infrastructure, data quality, and labor market shifts.
What Happened
The Astana Open Dialogue platform, positioning itself as an independent analytical hub, has published a bulletin on Kazakhstan’s digital transformation. The document summarizes expert discussions on the country’s transition from electronic government (e-Gov) to artificial intelligence-based governance (AI-Gov). The authors analyze institutional readiness, infrastructure status, and social risks, drawing on data from relevant ministries and international experience. The document is available for review via the platform's resources.
Country and Market
The bulletin notes that 2026 has been declared the year of priority development for digitalization and AI in Kazakhstan. The document examines three global technology development models from which the country can draw lessons.
The European approach emphasizes ethics, algorithm transparency, and strict regulation. The American model relies on the dominance of the private sector and digital platforms. The Chinese strategy is characterized by rapid implementation driven by massive datasets and state investment. For Kazakhstan, the authors see the task as adapting these approaches: the country needs its own computational capacities and a unified data management policy to reduce dependence on foreign suppliers.
Why It Matters
The implementation of algorithms directly affects the employment structure. The document cites estimates from the Ministry of Labor and Social Protection of the Population of the Republic of Kazakhstan: about 2.2 million jobs, which is approximately a quarter of the total, are at moderate or high risk of automation. Over a million positions could be affected by the active use of generative AI.
At the same time, the bulletin’s authors point out the problem of personnel training. According to the data cited in the text, 650,000 students have undergone basic training through the national AI-Sana project. However, at the level of government agencies, there remains a varied understanding of data quality standards. Differences in information collection methodology hinder the creation of a consolidated catalog necessary for training reliable state AI models.
What’s Next
The main focus is shifting to the creation of local infrastructure. The government is discussing the modernization of data processing centers and the development of high-performance computing capacities. In parallel, a regulatory framework is being developed: classifying AI systems by risk level and defining the boundaries of responsibility for algorithmic decisions.
The public sector is gradually realizing that without unified data collection standards and a local computational base, fragmented departmental AI pilots will not form a sustainable national infrastructure.