When migrating data warehouses, AI agents reduce labor costs by 15% and pay for themselves in up to a year / News of the economy of Krasnoyarsk and the Krasnoyarsk Territory / Newslab.Ru

AI agents in corporate data warehouse migration projects significantly speed up work and reduce dependence on the shortage of the most sought-after specialists.

Tatyana Sezemina, head of the “AI Agent Platform” of the T1 AI direction (T1 IT holding), spoke about this at the session “How AI changes the rules of the game in the migration of data storage and analytical reporting” at the “Russian Retail Show 2026”.

According to the speaker, the introduction of AI is especially relevant for retail, where the volume of data grows annually by an average of 30%, and most companies face a shortage of qualified personnel and a high level of outdated systems. In such conditions, storage migration becomes a lengthy and resource-intensive process.

IT holding T1 implemented a pilot project for a large Russian retailer, in which, with the help of AI agents, it automated key stages of working with data – from structuring in the warehouse to generating database queries and analytics. The solution covers the full cycle: building a logical data model, restoring connections between data, reverse engineering queries, generating requirements and monitoring data quality.

“We applied an agent approach, in which AI plays the roles of a system analyst, developer, tester and data management specialist,” said Tatyana Sezemina.

Thus, according to the results of the pilot project, time savings on individual operations reached 80%, the average for an operation was about 40%, and the overall reduction in labor costs was 15%, taking into account all processes. At the same time, the speaker noted that the payback period for the solution varies from nine months to one year, depending on the scale of the project.

Separately, the expert drew attention to accelerating the development and launch of new reports by optimizing work using AI agents.

AI agents can also be used to improve data quality and architectural management by automatically checking that objects comply with established standards, identifying outdated storefronts, identifying data owners, and analyzing storage usage using transaction logs.

Sezemina also emphasized that the implementation of such solutions is advisable not only for large businesses. The main factor when making a decision is the scale and complexity of the data warehouse. For small teams, it is possible to launch projects using cloud models, which reduces the initial investment.

“In the future, the development of AI agents will lead to the emergence of autonomous data warehouses, where most of the operations – from development to analytics – are performed automatically, and interaction with the system occurs in natural language,” noted Tatyana Sezemina.