AI in Russian: Russian business at a crossroads — benefits, risks, and the reality of implementing neural networks
Russian businesses are actively implementing artificial intelligence, but not all projects bring the expected benefits. The question of how to measure the real return on AI is becoming critical. According to industry surveys, many companies are concluding that regular employees are cheaper than neural networks. However, a complex picture lies beneath this superficial assessment.
Hidden Costs and Organizational Complexity
The real cost of implementing AI is not just licenses and tokens. It includes infrastructure, integration with existing systems, staff training, and subsequent support. The main challenge for companies is organizational complexity: how to safely embed AI into the internal network, comply with information security and regulatory requirements, and avoid spending years building infrastructure from scratch. At the same time, in certain scenarios, the return on investment can reach hundreds of percent, and in the most radical cases, automation allows a shift from dozens of employees to just a few without loss of quality.
How to Measure the Return on AI?
We view AI not as a "toy for response speed," but as a tool for improving operational and financial metrics: reducing the time to launch new services, decreasing IT infrastructure costs, and simplifying the scaling of AI workloads. The return is measured through two layers: the infrastructure layer (increased productivity and reduced operating costs) and the business layer (speed and cost of launching AI services for internal and external users).
Impact on Personnel and Skills
Contrary to concerns, workforce reduction due to AI implementation is not the primary goal. The companies we work with primarily reallocate efforts: spending less on building and supporting low-level infrastructure and more on creating specific AI scenarios for the business. This changes the task profile for IT teams but does not directly lead to layoffs. Employees quickly realize that AI takes over routine operations and smooths out the pace of daily work, and during peak seasons, it allows them to accomplish more tasks in the same time, reducing stress. Skill degradation is not observed where AI is implemented as part of a controlled infrastructure, rather than as a "black box," and is accompanied by training and quality standards.
Errors, Risks, and Data Security
We initially assume that generative models can make mistakes, and we design solutions so that critical decisions remain with humans. The issue of responsibility and risks is one of the reasons why we bet on a platform within our own network with a transparent architecture. Given regulatory requirements and the expected tightening of approaches to cross-border data transfer, we consider deploying infrastructure and models so that the company can transparently answer where and how its data is stored as a basic approach. We emphasize the possibility of a fully domestic technological base.
Regulation: Freedom or a Zone of Uncertainty?
The current situation is intermediate: the lack of strict regulation gives businesses the opportunity to experiment but creates uncertainty about responsibility, especially regarding generative content and data handling. For integrators and clients, this means they must establish their own frameworks: from architecture to internal policies and contractual bases. We advocate for a risk-assessment-oriented 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.
Expert Commentary from Cryptalist: The Russian AI market is in a phase of active formation. The key takeaway is that the success of implementation depends not on choosing the "smartest" model, but on the quality of organizational preparation and understanding of real business goals. Companies that ignore infrastructure and personnel aspects risk finding that "cheap" AI ends up costing more than human labor. In the next year or two, we will see market consolidation around a few proven platform solutions and the emergence of clear regulatory frameworks, which will reduce uncertainty for investors and clients.