Alfa-Bank described “vibe leadership”: how AI took over routine work and accelerated the growth of the A-Token platform

Alpha Bank described "vibe leadership": how AI removed routine and accelerated the growth of the A-Token platform

A senior manager of Alfa Bank, Andrey Kalinin, explained how internal AI AlfaGen transformed the way teams work on the A-Token platform. Documentation automation, according to him, helped remove some manual workload, speed up processes, and free up product leaders to focus on architecture, strategy, and business scaling.

From routine to management

Kalinin links this shift to what he calls "vibe leadership." The point is not a trendy label, but a redistribution of executive time: fewer hours go to mechanical assembly of documents, approvals, and repetitive formulations, while more time is spent on decisions that drive the product. For fintech with high regulatory and technical burden, this is especially important because documentation here is not a side artifact, but part of the production loop.

In the case of A-Token, the effect proved noticeable not just in convenience. The team launched a digital financial assets platform from scratch, then grew from 7 to over 180 IT staff and, according to Kalinin, captured 50% of the Russian digital financial assets market. At such scale, any routine multiplied across dozens of people and processes quickly becomes an expensive drag on business. And the more complex the loop, the costlier the extra approval cycles.

What AlfaGen does

The main practical case is generation and maintenance of technical documentation in AsciiDoc. According to him, the internal AI helped write large volumes of documents, save millions of rubles, and reduce time costs by approximately 20–25%. For product and engineering teams, this means that part of the work can now be shifted from manual mode to a managed semi-automatic process. Especially where documents live alongside code, releases, and internal regulations.

• Drafting technical documentation in AsciiDoc
• Rapid updates of existing specifications
• Standardization of document structure and language across teams
• Reduction of manual workload for product managers, analysts, and tech leads
• Acceleration of launching new internal artifacts

"AI takes on the routine, while you handle architecture, strategy, and

what makes money."

This formula applies well beyond text work. When documentation stops being a bottleneck, approvals move faster, onboarding of new employees accelerates, and context is transferred more smoothly between product, development, and business. In this scheme, AI acts not as an autonomous leader, but as a tool that reduces the load on the most expensive specialists. As a result, quality control of solutions also speeds up. This also reduces the cost of communication errors.

Scale without overload

The A-Token story is interesting because it is not about an experiment on a small team, but about a mature fintech division with strict requirements for quality and speed. When an organization grows from a handful of people to hundreds, knowledge management becomes a separate problem: rules must be documented, changes must be communicated quickly, and decisions must not be lost in chats and calls. In such an environment, AI delivers impact not just in saving hours, but in reducing chaos.

Yet Kalinin's thesis goes beyond simply "let's have neural networks write documents." He speaks about a fundamental shift in the role of a product leader. If before a strong manager was often someone who personally pushed through texts, specifications, and formalization, now their value shifts toward systemic thinking: where the platform is headed, which constraints are critical, where a new service is needed, and where unnecessary complexity should be rejected. For Russian fintech, this is also a signal of the coming of age of internal AI tools that are embedded into daily operations where mistakes are costly.

What it means

The approach described by Alfa Bank illustrates one simple thing: the next wave of AI gains in corporations comes not from flashy demos, but from removing routine from people who make key decisions. If this model takes hold, product leaders will write less by hand and spend more time managing architecture, priorities, and business growth. This is where banks and large platforms can find practical, not decorative, scenarios for implementing models.