Sber Bank Reduced Data Analysis Time by 50%: How AI Agents Transform Corporate Processes

In large companies, thousands of hours are spent daily on routine operations that, though critically important, do not create direct value. Sber, a leader in the Russian financial sector, addressed this issue by launching the GigaCowork platform, which delegates routine tasks to AI agents. This led to a 30-50% reduction in information analysis time, an 80% acceleration in document processing, and savings of up to 83% of employee work time.

In large corporations, routine isn't just an inconvenience; it's a huge expense. Hundreds of thousands of hours spent searching for data, reconciling documents, and preparing standard reports distract highly qualified specialists from strategic tasks. This leads to slower decision-making, reduced motivation, and colossal financial losses. Many companies try to solve this problem with point automation, but it often leaves employees to manually transfer data between systems. Today, there's a solution that allows managing the entire process chain, not just isolated fragments.

Where time was lost in corporate processes

In a structure as large as Sber, millions of documents and requests are processed daily. Back-office, legal, and accounting department employees constantly face tasks requiring data collection and analysis from various corporate systems. For instance, changing payment terms for a client contract could take 2-3 hours, including finding the contract, downloading its history, cross-referencing with credit policy, assessing risks, and drafting an addendum.

Lawyers spent 1-2 hours on initial contract review for compliance with templates, while accountants daily collected data from "1C" and CRM, reconciled primary documents, and identified discrepancies to prepare a report on problematic areas by the start of the week. These tasks, though important, were monotonous, consumed valuable specialist time, and were prone to human error.

Why point automation didn't solve the problem

Existing automation systems often focused on individual process stages, leaving out the need for manual data transfer and coordination between systems. For example, a system might automatically generate part of a document, but collecting source data and final verification still fell to a human. This meant employees continued to spend time "stitching together" automated fragments, and the overall impact of automation was far from desired.

The company needed a comprehensive solution that would allow describing work logic in business regulation language, without developer involvement or IT system re-engineering, while also enabling AI agents to interact with each other and with corporate systems to complete tasks end-to-end.

How the GigaCowork platform was designed

GigaCowork was conceived as a unified environment for managing AI agents, capable of translating company regulations and practices into managed automation. The key idea was to empower employees to describe processes in natural language, using regulations, instructions, or checklists, and turn them into skills for AI agents.

The platform includes several core components:

• Workspaces. Isolated environments for departments and teams, where each can create their own agents, manage document access, and set rules.
• Skills. Instructions described in plain language that define how an agent should perform specific tasks. These skills can be used by other employees and departments, scaling the effect across the entire company.
• Connectors. Connections to corporate systems (CRM, ERP, email, file storage) via the Model Context Protocol (MCP), allowing agents to retrieve and transmit data.
• Access Control. All agent actions are performed on behalf of and with the credentials of the employee who initiated the task, ensuring compliance with corporate security policies and transparent logging.

The agents' task is not just to automate, but to fully take over routine steps of business processes, interacting with each other, but always leaving the final decision to a human.

Implementation and scaling

Sber opened access to GigaCowork for testing, which allowed for gradual implementation of the platform across various departments. Agent setup did not require technical skills, significantly simplifying the process and enabling business users to create and adapt agents to their needs independently. The agents' logic was defined in plain language in a chat interface, making the process intuitively understandable.

For example, in the back office, an agent, upon receiving a task to change payment terms for a contract, independently found the contract, downloaded its history, cross-referenced credit policy, assessed risks, and prepared an addendum in just 10 minutes, whereas manually this would take 2-3 hours. Lawyers gained the ability to review contracts for template compliance in minutes, and accountants could automatically collect data and identify discrepancies.

Implementation results

The implementation of the GigaCowork platform allowed Sber to significantly increase the efficiency of corporate processes. This is most clearly demonstrated in the reduction of time spent on routine operations, which frees up employees for more complex and creative tasks. Agents help accelerate strategic decision-making by reducing the time for information and context analysis by 30-50%.

How to apply this experience to your business

Sber's experience shows that AI agents can be a powerful tool for optimizing business processes in any company with many routine tasks. Here's where to start:

• Identify routine processes. Find tasks that consume significant employee time but do not require deep human judgment. This could include data collection, preparing standard reports, or initial document review.
• Describe the logic of operation. Use natural language to create instructions and regulations for AI agents. It's crucial that these instructions are clear and unambiguous.
• Integrate agents into existing systems. Connect AI agents to your corporate CRMs, ERPs, and file storage so they can seamlessly exchange data.
• Start small. Implement agents in stages, beginning with the least critical but most resource-intensive tasks. This allows for quick results and gains support from the team.

If this case sounds like what's happening in your company, our manager can help: he'll analyze your business and niche for free and point out where an AI agent would bring a real result in your case. Message the manager