Kazakhstan's President Kassym-Jomart Tokayev has endorsed the introduction of artificial intelligence in agriculture. The technology opens the way to reducing spending on water, energy and pesticides, and for the country — to the status of one of Eurasia's key food centers amid a global food shortage. However, the economic effect of the innovation is so far distributed extremely unevenly.
According to a study on the use of AI in the agricultural sector, by 2050 the Earth's population may reach 9.7 billion people, and food production will need to be increased by roughly 70%. Traditional methods are not enough for this — a transition to "Agriculture 4.0" is needed, where the Internet of Things, data analytics, automation and AI operate.
With the traditional approach, an agronomist cannot survey every plot of a large field and simultaneously account for the relationships between weather, soil and plant condition. The algorithm combines data from drones, sensors and weather stations, compares them with indicators from previous years and helps to predict the harvest more accurately, find problem areas and choose the harvest timing. Overall, the savings from using AI range from 50% to 95% and depend on the crop, weed density and field conditions.
Why the benefit so far goes to large farms
The barriers to AI adoption are technical, economic and institutional in nature. The effectiveness of water management depends on the availability of utility infrastructure and cloud computing platforms. A technical problem also arises in yield assessment: neural networks work well on familiar fields, whereas farmers need models that understand general patterns. In trials on six fields in Canada, the correlation of the forecast on familiar plots was 0.81, while on unfamiliar ones it dropped to 0.39 — a gap of 52%, critical for user trust.
Another obstacle is price: not every farmer can buy drones or hire specialists. The real effect of AI is currently obtained mainly by wealthy enterprises. For most Kazakh farmers, the mass adoption of neural networks is still a novelty: they have already mastered the digitalization of production, but they have yet to understand the benefits of AI and select tools for their tasks. There are plans to introduce precision farming and "smart farms," create a unified industry platform and form a data lake — a common storage of industry information.
A similar process is underway in Russia, where the AI market in the agro-industrial complex may grow 20-fold by 2030. As in Kazakhstan, those who switch to digital technologies fastest win first, while farms that delay digitalization risk losing competitiveness.
What other sectors Kazakhstan is moving to AI
New technologies are also changing business access to money: starting in 2027, large companies in Kazakhstan will have to pass an ESG check before receiving a loan. AI systems have already reduced the frequency of fraudulent transactions by 5–8 times in certain scenarios, but automation runs up against the cost of computing power. The authorities are applying the same approach to government spending: a neural network will search for unjustified budget expenditures, and four key financial systems of the country will join a single digital ecosystem. The Smart Data Finance system already cross-checks data from 80 sources and finds discrepancies between people's official incomes and their property.
Cryptalist analysis: Kazakhstan's case illustrates the classic problem of AI adoption in capital-intensive industries: the technology can deliver savings of 50–95%, but the entry threshold secures the advantage for large farms, reinforcing market concentration. For investors, this is a signal of the formation of a new class of agritech assets and demand for computing infrastructure, while small producers remain at risk. The success of the initiative will be determined not so much by the quality of the algorithms as by the availability of cloud capacity and the speed of digitalization in the industry.