the economic potential from the introduction of artificial intelligence in Russia by 2028 could amount to 4.2–6.9 trillion rubles, or up to 4% of GDP

Moreover, the total economic potential (increasing revenue or reducing company costs) from the introduction of artificial intelligence (AI) in the Russian Federation by 2028 could amount to 22–36 trillion rubles. in nominal prices, a joint study by the consulting company Yakov and Partners and Yandex estimates the real effect by this date at 4.2–6.9 trillion rubles, or up to 4% of GDP, of which a fifth (0.8– 1.3 trillion rubles) – from the use of generative AI (creates texts, videos, images based on queries). At the same time, in absolute terms, about 70% of the potential falls on six key industries: transport and logistics, banks, retail, mining, consumer goods production and the IT industry.

According to a survey of technical directors of 100 largest companies in the Russian Federation in 15 industries conducted by Yakov and Partners, 20% of companies are already using generative AI, of which 12% have identified priority functions and scenarios for implementing the technology, about a quarter (27%) are experimenting on a targeted basis with her. All of them use solutions from OpenAI (GPT-4, GPT-3.5), more than 90% also use Russian models (YandexGPT and Kandinsky from Sber).

Almost two-thirds of respondents (68%) said that the implementation of AI-based solutions over the past year influenced 1-5% of the company’s EBITDA (mainly in the areas of e-commerce, banking, insurance, media and IT), a third (32%) organizations did not notice the changes. However, at the same time, almost every second company invests about 1–5% of its budget in IT and digitalization in AI, and individual AI adherents in telecom and IT companies even invest over 5%. Almost all companies (94%) call cost reduction the key effect of introducing AI; about a third of those working in the consumer sector (banking, retail, media and others) see AI as a way to increase revenue (including through new business areas), product value for customers and customer loyalty.

However, the implementation of this technology faces difficulties. The key one is the shortage of specialists in data science, data engineers, machine learning engineers, AI trainers and data analysts – 61% of employers mention this. Other barriers include access to hardware and the high cost of developing AI tools.

Venera Petrova