ASTANA — Financial institutions across Central Asia are accelerating the adoption of artificial intelligence, with 36% already using AI technologies and 56% planning implementation within a year, according to a report released Feb. 13 by the National Bank of Kazakhstan.
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Based on a survey of 232 financial organizations in Kazakhstan, the Kyrgyz Republic, and Tajikistan, the study offers the region’s first comprehensive assessment of AI integration in financial markets.
Despite strong interest, implementation remains at an early stage: 38% of respondents are conducting research, 28% are running pilot or partial projects, and only 2% have reached full-scale deployment.
“In 2026, the National Bank will focus on transitioning to the practical implementation of a digital asset market and integrating it into Kazakhstan’s existing financial system. Plans also include the development of a unified AI ecosystem, the creation of a platform of specialized AI agents within the Bank, and regional collaboration in AI to develop practically applicable AI solutions,” said National Bank Governor Timur Suleimenov in the paper’s foreword.
According to him, this approach ensures risk manageability, institutional readiness and compliance with regulatory requirements as AI agents, autonomous systems capable of performing tasks with limited human oversight, become more complex.
The digital asset initiative is part of a broader transition toward AI-supported supervision and agent-based systems that automate analytical and monitoring functions in the financial sector.
From operational automation to strategic use
The report shows that AI adoption in Central Asia is gradually moving beyond basic process automation toward predictive analytics and risk-focused applications. Early use centered on improving operational efficiency. Financial institutions are now increasingly applying AI to fraud detection, credit assessment and customer data analysis to support decision-making.
In Kazakhstan, 75% of banks use AI, and 88% intend to expand their application. However, deployment remains concentrated in transactional and customer-facing areas, particularly credit scoring and anti-fraud systems. Use in strategic planning, compliance oversight and comprehensive risk management remains limited and largely at an initial stage of implementation.
The report also highlights the growing importance of generative AI, systems capable of producing text, images, or other content, and AI agents that can perform semi-autonomous tasks.
Globally, agent-based AI systems are projected to grow at an average annual rate of 45% over the next five years. The U.S.-based research firm Gartner forecasts that by 2027, AI agents could participate in or support 50% of business decisions. McKinsey data cited in the report suggest that restructuring workflows around AI agents can reduce task execution time by 60% to 90%, depending on the application.
Global expansion, regional readiness gap
The study places Central Asia within a rapidly expanding global AI landscape. Corporate investment in AI reached $252.3 billion in 2024, up 26% from 2023. Over the past decade, investment volumes have grown nearly thirteenfold.
International surveys referenced in the report show that 78% of organizations worldwide now use AI in at least one business function, up from 55% in 2023. At the same time, the cost of AI model inference, the process of generating results from trained models, has fallen 280-fold since 2022, reducing entry barriers for smaller businesses through cloud-based and low-code platforms.
Despite these global advances, Central Asian countries remain outside the top 50 in AI readiness rankings. In 2024, Kazakhstan ranked 76th globally, Uzbekistan 70th, Tajikistan 131st and the Kyrgyz Republic 134th. The report identifies technological maturity and human capital development as the principal constraints slowing regional convergence.
Capacity, governance and risk considerations
Across the three countries, a shortage of specialists with combined expertise in finance, data analytics and risk management remains a major constraint on broader AI implementation. Apart from computing infrastructure, sustainable AI integration also requires institutional readiness, including reliable data systems and coordinated governance frameworks.
Data fragmentation, inconsistent quality standards and limited access to advanced computing resources constrain scaling beyond pilot programs. For example, in Tajikistan, 65% of financial executives consider AI critically important for future competitiveness, yet 33% of institutions currently use AI. Most innovation projects remain pilot-based and are not yet integrated into core business processes, limiting their overall impact.
In the Kyrgyz Republic, regulators are prioritizing AI use in payment monitoring, compliance automation and supervisory analytics. The report shows that institutional readiness, including unified risk management approaches and secure cross-border data exchange, remains a shared regional challenge.
The regulatory landscape is evolving alongside technological expansion. More than ten countries have established AI Safety Institutes to develop standards for secure deployment. Frameworks such as the European Union’s AI Act require labeling of AI-generated content, though only 38% of systems globally implement watermarking mechanisms.
Cybersecurity risks are also rising. A survey cited in the report found that 68% of organizations in the United States and the United Kingdom experienced data leakage linked to employee use of AI tools. In comparison, only 23% had comprehensive AI security policies in place.
Environmental impact adds another dimension to policy considerations. Global data center energy consumption increased 72%, and annual water use reached 560 billion liters. At the same time, AI-enabled technologies are being applied to improve efficiency in agriculture, logistics and energy management.
Strategic implications for Central Asia
The report showed that AI in Central Asia’s financial markets is no longer confined to experimentation. Adoption rates and planned expansion signal structural change, yet structural constraints in infrastructure, human capital and regulatory capacity continue to limit large-scale integration.
For foreign investors and international partners, the data highlight a region in transition: one that is actively aligning with global AI trends, investing in supervisory capacity and digital infrastructure, but that faces measurable constraints in talent, scale, and technological depth.
