Sberbank reduced Process Mining time by 4x: how an AI agent analyzes millions of events and generates reports

Previously, Sberbank analysts spent tens of hours manually verifying data to identify inefficiencies in business processes. Today, an AI agent performs this work for them, autonomously processing hundreds of millions of events per month, formulating hypotheses, and issuing ready-made recommendations. As a result, the expert analyst's time spent on data analysis has been reduced by four times.

In the context of a large bank, any process inefficiency isn't just lost hours; it translates into multi-million dollar losses, risks, and missed opportunities. Searching for these bottlenecks is like sifting through mountains of sand for specks of gold: it's time-consuming, labor-intensive, and requires deep expertise. But even the most experienced analyst cannot process terabytes of data in real-time. Fortunately, this burden can now be lifted by an AI agent.

The Pain of Process Mining: How analysis looked before the AI agent

Process Mining is a powerful tool for identifying bottlenecks and optimizing business processes. However, in a bank of Sberbank's scale, data volumes amount to hundreds of millions of events per month. Every click, every transaction, every interaction leaves a digital footprint. Analyzing such a massive array of data manually, or even with traditional BI systems, became a colossal task.

Analysts spent the lion's share of their time on routine operations: collecting, cleaning, aggregating data, building basic reports, and verifying obvious hypotheses. This consumed hours, sometimes days, leaving little time for the most important part — the intellectual work of interpreting findings, identifying root causes, and developing strategic solutions. Before the AI agent was implemented, each analyst spent on average four times more time on data analysis.

Why traditional methods fell short

Traditional approaches to Process Mining, though effective, required analysts to have a deep understanding of methodology and significant manual effort. Existing platforms allowed for process visualization and report generation but could not independently generate hypotheses, uncover hidden relationships, or propose specific recommendations. This still required a human expert to sift through vast amounts of information.

Sberbank realized that to scale Process Mining and enhance its effectiveness, a new approach was needed. This approach would automate the most labor-intensive and routine analysis steps and enable work with data without deep technical immersion. This led to the idea of creating an AI agent capable of taking on these tasks.

How the AI agent was designed

The AI agent for Process Mining was designed as an intelligent system capable of independently performing a full cycle of business process analysis. The key requirements for the agent included:

• Processing large volumes of data. The agent had to handle hundreds of millions of events per month, allowing for analysis at any time and frequency.
• Automated hypothesis generation. Instead of analysts manually checking dozens of hypotheses, the agent was to identify potential inefficiencies and their root causes itself.
• Generating ready-made reports with recommendations. The agent's output was to be not just a set of data, but a structured report with specific conclusions and optimization proposals.
• Ease of use. Working with the AI agent required no complex configurations or deep methodological knowledge. It was enough to upload data and formulate the task in natural language.

Thus, the AI agent became not a replacement for a specialist, but a powerful assistant, freeing up time for more complex and creative tasks.

Implementation and scaling

In 2025, Sberbank launched the AI agent within its ecosystem. The implementation was phased, starting with the most critical and high-volume processes. Gradually, the agent integrated into the daily work of analysts, becoming an indispensable part of their toolkit. This allowed Sberbank to verify its effectiveness and reliability in real-world conditions.

By 2026, when the AI agent was presented at the international AI Journey conference and announced for market release, it was already successfully processing millions of events, helping Sberbank optimize its processes. This case demonstrated that an AI agent not only increases productivity but also makes process analytics accessible to companies of any size, even those without a staff of highly qualified Process Mining analysts.

Results

The implementation of the AI agent allowed Sberbank not only to significantly reduce the time spent on data analysis but also to improve the quality of decisions made. Analysts can now focus on strategic tasks, using ready-made insights from the agent. This accelerates the entire work cycle—from research to implementation of changes—ultimately leading to increased efficiency of business processes and improved customer service quality.

How to implement this in your company

Sberbank's case demonstrates that AI agents can radically change the approach to process analytics, making it accessible and effective for companies of any size and industry. To replicate this success, start with:

• Identifying the most labor-intensive processes. Pinpoint the business processes where manual data analysis consumes the most time and requires high expertise.
• Data collection. Ensure you have access to sufficient event data in your systems for the AI agent to analyze.
• Phased implementation. Begin with a pilot project on one or more processes to evaluate the AI agent's effectiveness and gradually scale its application.
• Team training. Show analysts how the AI agent can become their assistant, freeing them from routine tasks and allowing them to focus on more intellectual challenges.

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