AI in Russian: Russian business at a crossroads between profit and disappointment
The integration of artificial intelligence into business processes has become a global trend, and Russia is no exception. However, as practice shows, the widespread euphoria around neural networks often clashes with harsh reality: not all companies can derive real benefits from transitioning to AI. According to industry research, many come to a paradoxical conclusion — simple employees are cheaper than implementing expensive neural networks. What is the root of the problem, and how is Russian business overcoming these challenges?
Hidden Costs: AI is Not Just "Buy and Forget"
The real cost of implementing AI consists not only of the price of licenses and tokens. It always includes infrastructure, security, integration with existing systems, staff training, and ongoing support. The main challenge for companies, in my opinion, is not so much the cost of resources as the organizational complexity. How can AI be safely embedded into the internal network, comply with information security requirements and regulations, and avoid spending years building infrastructure from scratch? These are the issues that become stumbling blocks.
Return Metrics: From "Toy" to Tool
Successful companies view AI not as a "toy for faster responses" but as a tool for improving operational and financial performance. Returns are measured through two layers: the infrastructure layer (increased productivity and reduced operating costs) and the business layer (speed and cost of launching new AI services for internal and external users). In some scenarios, return on investment can reach hundreds of percent, and in the most radical cases, automation can reduce dozens of employees in a department to just a few without losing quality.
Employees: Not Replacement, but Redistribution
Contrary to fears, in our projects we do not see mass layoffs due to AI implementation. The companies we work with primarily redistribute efforts: they spend less on building and maintaining low-level infrastructure and more on creating specific AI scenarios for the business. Employees quickly realize that AI takes over routine operations, smooths out the pace of daily work, and allows them to accomplish more tasks in the same time. Skill degradation does not occur where AI is implemented as part of a controlled infrastructure, rather than as a "black box."
Security and Regulation: Playing the Long Game
The issue of responsibility and risks is one reason why the focus is on an in-house platform with a transparent architecture. Given regulatory requirements and the expected tightening of approaches to cross-border data transfer, this approach is becoming fundamental. Infrastructure and models must be deployed so that the company can transparently account for where and how its data is stored. That is why the possibility of a fully domestic technological base is emphasized.
My opinion: The current situation is an intermediate phase. The lack of strict regulation gives businesses freedom to experiment but creates uncertainty about responsibility, especially regarding generative content. For integrators and clients, this means they must establish their own frameworks: from architecture to internal policies. The future lies in a balanced approach, where requirements depend on the level of the system's impact on people and business, rather than being uniformly applied to all AI services. The Russian market, especially the financial sector, remains one of the most active testing grounds for the practical application of these technologies.