Just a few years ago, artificial intelligence in Kazakh business appeared more often on conference slides than in work processes. Today, neural networks check banking transactions, respond to customers, analyze video cameras in factories, prepare documents, predict breakdowns and help sellers create advertising. Kazakhstan is gradually moving from digitalization, when the paper process was simply transferred to an application, to a new model where the algorithm begins to participate in decision making. However, between a spectacular demonstration and a profitable implementation still lie data, money, security and human responsibility. Details in the material El.kz. Kazakhstan is moving from applications to algorithms The first stage of digital transformation of Kazakh business was associated with mobile applications, online payments, electronic documents and automation of individual operations. Now companies are trying to build artificial intelligence on top of this infrastructure. The difference is fundamental. A regular program operates according to a predetermined scenario. AI can search for patterns in large amounts of data, recognize speech and images, predict client behavior and formulate new
content. By the beginning of 2026, more than 100 startups related to artificial intelligence were operating in Kazakhstan. The volume of venture capital investment in this sector has grown from approximately $14 million in 2023 to $73 million in 2025. AI projects accounted for more than half of all venture investments in the country. The most active areas of Kazakh developers are associated not with humanoid robots, but with applied business solutions: automation of corporate processes, industry, marketing, education and medicine. AI does not come to Kazakhstan in the form of a metal android. More often it looks like an inconspicuous window in a banking application, a camera above a production line, or an assistant who parses a long contract in a few seconds. Banks became the first large laboratory. The financial sector turned out to be the most prepared for the implementation of AI. The reason is simple: banks already have large amounts of structured data, developed digital infrastructure and clear objectives that can be measured in money. According to a National Bank study published in 2024, 31 percent used artificial intelligence to some extent.
financial market participants. Among second-tier banks the figure reached 60 percent. AI (GROK) Algorithms are used to assess credit risks, identify suspicious transactions, prevent fraud, analyze customer behavior and automate support. When a user receives an instant loan offer, it does not necessarily mean that the decision is entirely made by a machine. But the algorithm is capable of processing in seconds a credit history, income level, movement of money through accounts and other parameters that would take an employee much more time to analyze. AI is also helping banks look for unusual transactions. If a customer usually pays in Almaty, and a few minutes later an attempt is made to make a large purchase from his account in another country, the system may stop the transaction or request additional confirmation. For the client this looks like convenience. For the bank, this means reducing losses and the ability to service millions of transactions without a proportional increase in staff. Chatbot becomes the first employee the client meets In banking applications, online stores and service companies, AI is increasingly becoming the client’s first interlocutor.
A chatbot can report the status of an application, find information on the tariff, explain delivery conditions, fill out a request and transfer a complex question to an operator. Modern systems are gradually moving away from “press one or two” style menus and are beginning to understand ordinary human speech. For business, this is one of the most understandable ways to save money. The operator does not have to answer the same questions hundreds of times about schedule, price or order status. He connects where a solution is needed, an exception to the rule or an emotionally difficult conversation. But support automation can easily become a trap. If the bot does not understand the request, repeats one answer and does not allow you to contact a person, the company saves on the contact center, but loses the trust of the client. Good AI should not become a concrete wall between the business and the customer. His task is to open the desired door faster. Retail is learning to predict the buyer Trade uses artificial intelligence where previously it relied on the experience of managers and general statistics. Algorithms can analyze which products are bought together, when demand increases, where empty shelves occur, and how price changes affect sales. Based on
Using this data, the business plans purchases, creates personal offers and distributes goods between warehouses. For a large network, an error in the forecast means millions of tenge. If you buy too little, the product will run out when demand is high. If there is too much, the money will hang in the warehouse, and perishable products will have to be written off. AI is also changing advertising. The company can divide the audience not only by age and city, but also by behavior: who was recently interested in a certain product, who has not made a purchase for a long time, and who is probably ready to switch to a competitor. This is how the business receives an increasingly accurate portrait of the client. At the same time, the question arises of how far the company can go in analyzing personal data and whether the user understands why he was shown a certain offer. In factories, AI looks through cameras In industry, one of the most visible technologies involves computer vision. The system analyzes images from conventional cameras and detects violations, defects or dangerous situations. It may notice an employee not wearing a hard hat, a person entering a restricted area, smoke, a leak, improperly positioned equipment, or product deviation from standard. AI (Cha
tGPT) As part of the Industrial AI Acceleration program, Kazakh enterprises offered developers more than 140 practical tasks. About 75 percent of requests related to corporate functions: finance, procurement, human resources, legal work, IT and communications. The rest were directly related to production, including equipment repair, quality control, robotics and industrial safety. One of the projects presented, SafeSight AI, works on top of an existing video surveillance system. According to the developers, using the platform can reduce the number of injuries and dangerous incidents by 20-40 percent, and the costs of inspections and investigations by up to 30 percent. These figures belong to the company itself and depend on the specific enterprise. But the direction is clear: the camera ceases to be a passive archive that is accessed after the incident. It becomes a warning tool. The oil industry creates digital copies of objects For the oil, gas and mining industries, AI is especially interesting because of the cost of any error. An unplanned equipment shutdown, accident, or process misconfiguration can cost a company a lot of money. Here are used
predictive analytics, engineering modeling and digital twins. A digital twin is a virtual model of equipment or a production process on which different operating modes can be tested without risk to the real object. At the Atyrau and Pavlodar oil refineries, engineering models are used to analyze process plants. Optimization of one of the columns of the Atyrau Refinery made it possible to increase its loading by 10 percent and increase the output of vacuum gas oil. At the end of 2024, the additional value of manufactured products was estimated at 2.6 billion tenge. This is an example of broader digital transformation, rather than the work of a single neural network. But it is precisely such systems that create the foundation for the subsequent application of AI: the algorithm is useful only when the equipment is connected to sensors, information is collected regularly, and the data can be compared. Small business received a digital assistant for the price of a subscription For a small company, creating your own neural network is almost impossible and usually not necessary. But generative AI has made the technology available through off-the-shelf cloud services. Entrepreneurs use neural networks to write product descriptions,
prepare advertising publications, translate texts, respond to reviews, create presentations and analyze tables. A small online store can prepare dozens of product cards in an evening. The cafe can create advertising campaign options. The lawyer receives a draft document, and the accountant receives an explanation of the complex table. However, saving time does not mean that the result can be published without verification. A neural network can come up with a non-existent law, make a mistake in a calculation, confuse the characteristics of a product, or write a convincing but incorrect answer. In small businesses, it is especially dangerous to perceive AI as a cheap replacement for a specialist. It is much more useful to see him as a trainee with great speed and no sense of shame: he produces results instantly, but does not blush when he makes a mistake. Marketing is getting faster, but risks becoming the same Generative systems have significantly reduced the cost of content production. Now companies can quickly create images, advertising texts, video scripts and mailing options. For small businesses, this is the democratization of marketing. Previously, a full-fledged campaign required a photographer, designer, copywriter and videographer. Today some of the work can be done
There is one employee with a set of digital tools. AI (ChatGPT) But a new problem arises: there is more content, but less recognition. If dozens of companies use the same models and similar requests, advertising begins to speak with one plastic voice. Therefore, human style, observation and understanding of local culture become more valuable, not less. AI can draw a beautiful yurt, but only a human will notice that the ornament, clothing or household detail looks implausible. Tools that can work efficiently in the Kazakh language are especially important for Kazakhstan. The development of national language models could make AI more useful for regional businesses, government services, and companies serving bilingual audiences. AI is changing not only processes, but also the structure of employment. The fear of mass replacement of people is still ahead of reality. In most Kazakh companies, AI does not fire an entire department with the click of a button. He gradually takes away individual tasks. A marketer spends less time on drafts. The operator answers standard questions less often. The analyst processes documents faster. The engineer receives a warning about a possible malfunction. Pr
and this gives rise to new responsibilities: checking the algorithm’s responses, preparing data, setting up the system, monitoring quality, and explaining why the machine made a certain decision. The labor market is divided not only into those who will be replaced by AI and those who will not be replaced. The division between professionals who know how to use new tools and those who continue to do the same work manually becomes much more noticeable. Kazakhstan already has corporate and educational programs for training in working with artificial intelligence. The government strategy envisions training millions of citizens with basic and advanced skills in this area. However, teaching an employee to write requests is not enough. He must understand the limitations of the model, the rules for working with confidential information, and the consequences of an automated solution. Kazakh startups rely on business, not science fiction. Most domestic AI startups create solutions for companies. They automate documents, analyze calls, help control production, recognize images, optimize sales and create internal corporate assistants. Such a market looks less impressive than
bots from the movies, but may turn out to be much more profitable. The enterprise is ready to pay not for “smart technology”, but for reducing downtime, reducing the number of errors or speeding up the processing of applications. As a result of the Astana Hub industrial program, 38 teams began working with corporate customers, seven pilot projects were launched, and enterprises expressed confirmed interest in more than 50 solutions. Among the program partners were companies from the mining, metallurgical, automotive, energy, transport and oil and gas industries. This shows an important shift. AI in Kazakhstan is emerging from startup incubators and trying to pass the most difficult test: withstanding dust, shift schedules, old equipment, internal bureaucracy, and the CFO’s question “how much will we earn on this?” The state is building infrastructure for the market. For the development of AI, the enthusiasm of developers is not enough. We need computing power, access to data, specialists and clear rules. The Alem.Cloud and AI-Farabium computing systems were launched in Kazakhstan. According to the first national AI market survey, their combined performance is estimated at approximately 3.6 exaflops. The country is also developing
National artificial intelligence platform, Alem.ai center and Kazakh-language models. The government expects the number of AI-powered products to increase fivefold by 2029. For businesses, this means potential access to infrastructure that a single company could not build on its own. Training a large model requires expensive GPUs, electricity, cooling, and a team of engineers. But government infrastructure will be useful only if there are transparent access rules, reasonable costs and the ability to work with commercial information without the risk of leakage. Artificial intelligence now has a legal framework In November 2025, Kazakhstan adopted a separate law “On Artificial Intelligence”. For business, this is an important transition: the use of algorithms ceases to be a territory where rules are formed only by internal company instructions. The law establishes requirements for security, data protection and respect for human rights. This is especially important for banks, insurance companies, employers and digital platforms where an algorithm's decision can affect credit, insurance rates, employment or access to a service. AI (ChatGPT) Company
They will have to be responsible not only for the technology itself, but also for what data it uses, how secure the information is, and whether a person can challenge the automated decision. Legal regulation creates additional costs, but at the same time reduces uncertainty. It is easier for a business to invest in technology when the boundaries of what is acceptable are clear. The most overlooked resource in AI is data. Many projects fail not because the neural network is not smart enough. The problem starts earlier: data is stored in different tables, documents are filled out differently, processes are not described, and employees use different names for the same indicator. AI does not automatically correct organizational chaos. He can only reproduce it very quickly in digital form. Before implementation, a company has to answer boring but crucial questions: who owns the data, who is responsible for its quality, whether it can be used, where it is stored and what happens if there is an error. Therefore, the real cost of an AI project is often hidden not in the model license, but in data cleaning, systems integration, and internal process redesign. Where is the line of responsibility? The more active the algorithms are in participation
in business operations, the more important the question is: who is responsible when the system makes mistakes? If an AI denies a customer credit, accuses an employee of wrongdoing, misidentifies a defect, or sends false information to a customer, the company cannot blame the machine as the independent culprit. AI (ChatGPT) Responsibility remains with the people and organization who selected the system, provided it with data and implemented the result into the process. Mature implementation therefore requires human oversight. The higher the cost of an error, the fewer decisions it is worth completely transferring to the algorithm. AI can recommend, alert and triage. But in medicine, finance, industrial safety and personnel matters, the last word should remain with a specialist who can explain and defend the decision. What the next phase will look like In the coming years, companies will likely move from individual chatbots to internal AI agents. Such systems will be able not only to answer the question, but also to perform a chain of actions: find data, prepare a document, coordinate it along a given route and generate a report. Computer vision, predictive maintenance and digital twins will expand in industry. In banks
Algorithms will become more involved in the fight against fraud and personalization of services. In trading, they will manage inventory and prices in near real time. But the main competitive advantage will not be access to a neural network. Thousands of companies will be able to buy the same tool. The advantage will be given to the business that knows its own processes better, has high-quality data and knows how to combine machine speed with human decisions. We wrote earlier that a vaccine created using AI was tested on humans for the first time.
