The enterprise allows you to focus on basic tasks by appreciating the ability to recover employees from routine. For example, the automatic transcription of video conferences saves 2-3 hours of working at each meeting. According to Perov, if there are many meetings, it publishes hundreds of hours that can be directed to priority.
The ideal scenario is a single assistant who creates a meeting, sends invitations, squeezes, arranges tasks in the calendar and appoints responsible ones. Perov said that the success of Microsoft Copilot was explained by the presence of a product ecosystem in which AI is integrated without any problems. There is no such system in Russia, and developers have to build it from scratch. You need to select a productive model, collect data for training, and adapt it to the infrastructure of any company requiring time, servers and experts.
Another task is AI’s integration with different services: messengers, video conferences, CRM and mail. Business needs a single assistant working in all systems, not an individual AI for each service.
Safety is another obstacle. Large companies require data stored locally or in a special cloud. It is impossible to connect popular models such as chatgpt due to risks of information leakage.
The solution is to expand the neural network within the company. However, the latest GPT versions are not yet available for local distribution. Alternatively, some of them think of Deepseek, the open-source model, which is close to the GPT-4 comparisons. But in practice it needs time to prove stability.
It takes time to create an ecosystem. After the separation of Western services, Russian developers focused on replacing usual products. The integration between the services began to be actively developed in 2025. Finally, there is a completely practical problem between Russian developers – limited resources.
Source: Ferra
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