How Sovcombank Cut Its Product Team's Routine Work by 50% with an AI Assistant

When a product team spends more than half its day on documents, approvals and decoding requirements, the problem is no longer personal efficiency but the way the process is built. Sovcombank reached that conclusion after its portfolio of digital products began growing faster than its team of product managers. At some point it became clear that PMs were increasingly busy not with finding user value but with feeding an internal machine that demands ever more text, spreadsheets, checklists and explanations. In such a setup, even strong specialists start losing time not on the product but on maintaining its bureaucratic apparatus.

The author of the article describes a situation familiar to almost any fintech. The more products there are, the denser the communication between business, development, analytics, compliance and adjacent teams. In banking this is felt especially sharply: almost any change has to be explained, recorded, clarified and approved so that every participant understands it the same way.

As a result, the product manager turns into a permanent interface between teams. They have to document decisions, translate business wording into clear tasks, clarify context, prioritize the backlog, gather materials for meetings and, at the same time, not lose the thread of user problems. Sovcombank estimated that a single PM could spend up to 60% of working time on this routine. That is no longer a local overload but a systemic limit on growth.

Instead of yet another attempt to optimize calls, templates and spreadsheets by hand, the team built an AI assistant. Importantly, this is not a complex platform with dozens of integrations but a pragmatic solution: essentially a large but well-structured prompt that turns an LLM into a product sidekick.

Such a helper takes on a significant share of the text and analytical routine. You can delegate to it the first draft of documentation, the sorting of incoming inputs, the wording of requirements, draft prioritization, backlog structuring and the preparation of materials for discussing hypotheses or strategy. What used to take several rounds of manual assembly can now be obtained faster as a working draft, which the PM then brings to final quality.

The strength of this approach is that it does not try to replace the human where accountability and product judgment are needed. The article does not sell the image of a magical AI that understands the customer better than the team. The assistant is described as a tool alongside the person: it speeds up preparation, helps structure chaos, suggests wording and removes repetitive operations, but does not take the final decision.

For product work this is critical. A product manager's value rarely comes down to writing yet another document; it lies in noticing a conflict of interests, choosing a compromise, asking the right question and testing the decision with users in time. An LLM is useful precisely where you need to quickly work through a large volume of text, assemble a rough logic and remove mechanical intermediate steps.

It is also telling that the solution rests not on a separate corporate product but on a well-built scenario for interacting with a model. This is an important signal for teams that expect AI to require complex infrastructure. In many cases, tangible benefit appears sooner: when an organization clearly describes recurring tasks, fixes the desired output format and gives the model a role with clear constraints. Then AI works not as a technology demo but as an applied tool inside a specific function.

The result reported in the article is a 50% reduction in the product team's routine work. For product managers this means more than saved hours on the calendar. In effect, the team wins back resources for what brings real value to the business: talking to customers, testing hypotheses, analyzing user behavior and making product decisions.

The Sovcombank story also shows a broader trend: the fastest effect from AI in corporations often comes not from ambitious autonomous agents but from helpers built for a specific role. If you describe recurring tasks correctly and embed an LLM in the daily workflow, even one strong prompt can noticeably relieve a team and remove a bottleneck long considered unavoidable.