Sber cut data analysis time by 4x: how an AI agent transformed Process Mining

Business process analysis, or Process Mining, traditionally demands deep data immersion from analysts, consuming tens of hours to identify patterns and form hypotheses. Sberbank tackled this challenge by deploying an AI agent capable of processing up to hundreds of millions of events per month. This has reduced the time analysts spend on data by four times, transforming routine searching into immediate insights.

In the context of a large company like a bank, business processes generate colossal volumes of data. Hidden within this mass are bottlenecks, inefficiencies, and growth points, but finding them is like searching for a needle in a haystack for a human. Analysts spend hundreds of hours on manual dissection, which slows down decision-making and makes the process costly. This is not just a waste of time; it's missed opportunities and direct losses. But today, this problem is solvable, and the solution lies in automating analysis.

Challenges of Traditional Process Mining

Process Mining is a powerful tool for optimizing operations, but its effectiveness directly depends on the analyst's skill and time. For a large bank, where the number of operations runs into millions and events into hundreds of millions, manual analysis becomes almost impossible. Analysts face the need to:

• Process vast amounts of data. A typical process can contain millions of records, each describing a distinct event.
• Identify hidden relationships. Non-obvious dependencies between process steps that affect its efficiency.
• Formulate sound hypotheses. Based on identified patterns, specific improvements must be proposed.
• Spend tens of hours on manual verification. Each analysis stage requires attention and time, which slows down the entire cycle.

Why an AI Agent Was Needed

Existing Process Mining tools automate data collection and visualization but not their interpretation. They show "what" is happening but don't answer "why" and "what to do." Sber needed a tool that not only creates beautiful graphs but also identifies anomalies, offers explanations, and points to specific actions. This prompted the development of an AI agent capable of taking on the intellectual part of the analysis.

The goal was to create a system that autonomously performs the full analysis cycle, from processing raw data to generating a report with hypotheses and recommendations, freeing the analyst for more strategic tasks.

How the AI Agent for Process Mining Was Designed

The AI agent was designed as an autonomous system capable of executing the entire process analysis cycle. Its key functional blocks included:

• Data collection and normalization. The agent automatically integrates with various data sources and brings them to a unified format required for analysis.
• Pattern identification. Using advanced machine learning algorithms, the agent finds hidden patterns, bottlenecks, and inefficient routes in business processes.
• Hypothesis generation. Based on identified patterns, the agent proposes justified hypotheses about the causes of problems and possible optimization paths.
• Recommendation formulation. The agent not only points out problems but also suggests concrete steps for their resolution, backing them with data.
• Natural language interface. For user convenience, the agent allows tasks to be set and reports to be received using ordinary speech, without the need for deep technical knowledge.

Implementation and Scaling

The implementation of the AI agent began with pilot projects on Sberbank's internal processes. This allowed for refining the functionality and confirming its effectiveness on real data. Gradually, the agent started to be applied to analyze more complex and critical processes. A key moment was training employees on how to use the new tool. Thanks to an intuitive interface and the ability to set tasks in natural language, the entry barrier for analysts was significantly lowered.

Ultimately, the AI agent became an integral part of Sber's analytical pipeline, processing up to hundreds of millions of events per month and providing ready-made insights. The goal is to scale the solution and offer it to the market so that other companies can leverage similar benefits.

Results

The implementation results were impressive. The 4x reduction in analyst time means not only resource savings but also a significant acceleration of the decision-making cycle. Thanks to the AI agent, Sberbank gained the ability to understand its processes more deeply, quickly identify inefficiencies, and implement improvements, which directly impacts competitiveness and customer service.

How to Implement This in Your Company

Sberbank's case demonstrates that intelligent Process Mining automation is accessible and can bring significant benefits to any company working with big data. Here's how to start:

• Identify the most labor-intensive process. Choose a process where analysts spend the most time on manual analysis and problem identification.
• Estimate data volume. Understand how many events this process generates per month to assess the scale of the task for the AI agent.
• Formulate key questions. What hypotheses do you want to test, what inefficiencies are you looking for, what recommendations do you expect from the agent.
• Integrate data. Prepare data for analysis, ensure its accessibility and quality.
• Start with a pilot. Launch the AI agent on a limited data set or a segment of the process to test and fine-tune its operation.

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