How Sovcombank Reduced Product Team Routine Work by 50% Using an AI Assistant

When a product team spends more than half the day on documents, approvals, and requirement decoding, the problem is no longer about personal efficiency but about how the process is structured. Sovkombank came to this conclusion after their portfolio of digital products began growing faster than the product manager team. At some point, it became clear that PMs were increasingly occupied not with finding user value, but with maintaining the internal machine, which demanded more and more texts, tables, checklists, and explanations.

In such a configuration, even strong specialists begin to lose time not on the product itself, but on servicing its bureaucratic contour. The article's author describes a situation familiar to almost any fintech. The more products, the higher the density of communications between business, development, analytics, compliance, and adjacent teams.

In the banking environment, this is felt especially sharply: almost any change needs to be explained, documented, clarified, and agreed upon so that all process participants understand it equally. As a result, the product manager becomes a constant interface between teams. They need to document decisions, translate business formulations into understandable tasks, clarify context, prioritize the backlog, gather materials for meetings, and simultaneously not lose sight of user problems.

At Sovkombank, they estimated that one PM could spend up to 60% of working time on such routine tasks. This is no longer a localized overload but a systemic growth constraint. Instead of yet another attempt to manually optimize calls, templates, and spreadsheets, the team built an AI assistant.

Importantly, this is not about a complex platform with dozens of integrations, but about a pragmatic solution: essentially, a large but well-structured prompt that turns an LLM into a product partner. Such an assistant takes on a significant portion of textual and analytical routine. They can delegate initial documentation preparation, processing incoming briefs, formulating requirements, drafting prioritization, structuring the backlog, and preparing materials for discussing hypotheses or strategy.

What previously required several iterations of manual assembly can now be obtained faster as a working draft and then refined to final quality by the PM. The strength of this approach lies in the fact that it does not attempt to replace humans where responsibility and product judgment are needed. The article does not sell an image of magical AI that understands the customer better than the team.

The assistant is described as a tool alongside the human: it speeds up preparation, helps structure chaos, suggests formulations, and removes repetitive operations, but does not take over final decision-making. For product work, this is critical. Product value rarely comes down to writing another document; it lies in noticing conflicts of interest, choosing compromises, asking the right question, and timely validating the solution with users.

An LLM is useful precisely where you need to quickly process a large volume of text, assemble draft logic, and eliminate mechanical intermediate steps. It is also telling that the foundation of the solution was not a separate corporate product, but a well-constructed interaction scenario with the model. This is an important signal for teams waiting for AI to require necessarily complex infrastructure.

In many cases, tangible benefit appears earlier: when an organization clearly describes recurring tasks, documents the desired format of results, and gives the model a role with clear constraints. Then AI begins to work not as a technology demonstration, but as an applied tool within a specific function. The result stated in the article—a 50% reduction in routine work for the product team—means more than just saving hours in the calendar for product managers.

In fact, the team regains resources for what brings real business value: conversations with clients, hypothesis testing, analysis of user behavior, and making product decisions. Sovkombank's story also shows a broader trend: the fastest effects from AI in corporations often come not from ambitious autonomous agents, but from point-built assistants for a specific role. If you correctly describe recurring tasks and embed an LLM in the daily workflow, even one strong prompt can significantly offload the team and eliminate a bottleneck that long seemed inevitable.