For persons over 14+
July 17, 2025
Market leaders named barriers and recommendations for the introduction of industrial AI in Russia
Experts from customers, a solution developer and an IT integrator at INNOPROM at the session of the IT company K2tech named the key barriers to the introduction of artificial intelligence in Russia, and also offered their recommendations for more efficient and rapid application of the technology in the Russian Federation.
"AI is already becoming a powerful driver of industrial development and is bringing significant economic benefits. The potential of the technology is huge, but so far a number of barriers are hindering mass adoption — from a lack of personnel and the unavailability of the IT landscape of companies to issues of distrust of AI," said Igor Zeldets, Deputy CEO of the IT company K2tech.
"The use of AI in complex industrial ERP and other systems is the future. Everyone is developing platforms now, and they are getting more complex. For example, it takes a very long time for new specialists to immerse themselves in ERP, so the use of AI there is a necessity," said Oleg Lukyanov, head of Severstal's Industry Solutions Department.
TOP 5 barriers to the introduction of industrial AI
1. The data is scattered, local, and not ready for AI implementation
2. The staff is not qualified enough in the field of AI, there are not enough engineers
3. Companies and management do not trust innovations, including their safety
4. Infrastructure and hardware are not ready for implementation
5. There are not enough budgets.
"We are transferring all the operational costs that were previously associated with people and equipment to the investment stage. And without the ability to use cheap capital, it becomes impossible. We need cheaper software, we need cheaper cameras and so on, then the problem of high cost of capital will fade into the background," commented Eldar Shavaliev, CEO of KAMAZ DIGITAL, on the last barrier.
"Many companies need government assistance to implement AI. The Ministry of Industry and Trade is working on a new measure of government support aimed at increasing labor productivity. It is assumed that the subsidy will partially cover the costs of purchasing licenses, software and hardware systems, including information security tools, and other expenses that are sensitive to enterprises," said Maxim Minin, Deputy Director for Information Technology at the Digital Industrial Technologies Center.
"In many companies, there is a "patchwork" in the form of infrastructure, and another "blanket" of business applications is superimposed on it. And business solutions are based on this, and developers are trying to modify them or create new ones. In such a situation, the introduction of AI will be problematic and inefficient," said Svyatoslav Smirnov, product manager at K2 Neurotech, commenting on the infrastructure barrier.
TOP 5 recommendations for the implementation of industrial AI
1. Work with management to identify a specific goal and measurable criteria for success, to understand which problem AI will solve.
2. Consider feedback from staff who use and implement AI
3. First, prepare the processes for implementation, and then implement AI.
4. Collect and structure data before implementation, create a "single point of data truth"
5. Think bigger, look for new partnerships and at the same time not build high expectations.
"The race for innovation and investment in the current reality requires the collaboration of leading market players. It is already important to think about the markup, cataloging, description and labeling of data. Models can change, but your data will stay with you for a long time," said Maxim Vlasyuk, Director of the Arenadata Group's Industrial Sector Department.
"It is difficult to calculate the effect of using AI. The most obvious thing is to reduce labor costs for routine operations. But if we dig a little deeper, we don't fully know what cumulative effect AI will give us in the future. At the same time, companies that do not start using this technology will be uncompetitive," said Evgeny Vasiliev, Director of Industry solutions for Mechanical Engineering at the IT company K2tech.
"AI is already becoming a powerful driver of industrial development and is bringing significant economic benefits. The potential of the technology is huge, but so far a number of barriers are hindering mass adoption — from a lack of personnel and the unavailability of the IT landscape of companies to issues of distrust of AI," said Igor Zeldets, Deputy CEO of the IT company K2tech.
"The use of AI in complex industrial ERP and other systems is the future. Everyone is developing platforms now, and they are getting more complex. For example, it takes a very long time for new specialists to immerse themselves in ERP, so the use of AI there is a necessity," said Oleg Lukyanov, head of Severstal's Industry Solutions Department.
TOP 5 barriers to the introduction of industrial AI
1. The data is scattered, local, and not ready for AI implementation
2. The staff is not qualified enough in the field of AI, there are not enough engineers
3. Companies and management do not trust innovations, including their safety
4. Infrastructure and hardware are not ready for implementation
5. There are not enough budgets.
"We are transferring all the operational costs that were previously associated with people and equipment to the investment stage. And without the ability to use cheap capital, it becomes impossible. We need cheaper software, we need cheaper cameras and so on, then the problem of high cost of capital will fade into the background," commented Eldar Shavaliev, CEO of KAMAZ DIGITAL, on the last barrier.
"Many companies need government assistance to implement AI. The Ministry of Industry and Trade is working on a new measure of government support aimed at increasing labor productivity. It is assumed that the subsidy will partially cover the costs of purchasing licenses, software and hardware systems, including information security tools, and other expenses that are sensitive to enterprises," said Maxim Minin, Deputy Director for Information Technology at the Digital Industrial Technologies Center.
"In many companies, there is a "patchwork" in the form of infrastructure, and another "blanket" of business applications is superimposed on it. And business solutions are based on this, and developers are trying to modify them or create new ones. In such a situation, the introduction of AI will be problematic and inefficient," said Svyatoslav Smirnov, product manager at K2 Neurotech, commenting on the infrastructure barrier.
TOP 5 recommendations for the implementation of industrial AI
1. Work with management to identify a specific goal and measurable criteria for success, to understand which problem AI will solve.
2. Consider feedback from staff who use and implement AI
3. First, prepare the processes for implementation, and then implement AI.
4. Collect and structure data before implementation, create a "single point of data truth"
5. Think bigger, look for new partnerships and at the same time not build high expectations.
"The race for innovation and investment in the current reality requires the collaboration of leading market players. It is already important to think about the markup, cataloging, description and labeling of data. Models can change, but your data will stay with you for a long time," said Maxim Vlasyuk, Director of the Arenadata Group's Industrial Sector Department.
"It is difficult to calculate the effect of using AI. The most obvious thing is to reduce labor costs for routine operations. But if we dig a little deeper, we don't fully know what cumulative effect AI will give us in the future. At the same time, companies that do not start using this technology will be uncompetitive," said Evgeny Vasiliev, Director of Industry solutions for Mechanical Engineering at the IT company K2tech.
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