AI could bring trillions of rubles to the Russian economy

Today, there is very real money behind it. According to estimates, by 2030 the annual economic effect from внедрения AI in Russia could amount to 7.9 to 12.8 trillion rubles. However, Russia’s share of the global market still remains at around 1%.

The gap between current positions and the potential outcome shows how large the reserve for growth still is. The Russian AI market is estimated at about 2 trillion rubles and grew by roughly 25% over the year. AI solutions are used by 60–70% of companies, but in many industries the potential of their use is far from fully realized.

One such area is construction and real estate development. Here, the potential annual effect from using AI is estimated at about 900 billion rubles. At the same time, construction process management remains one of the least automated areas. Technologies are used in only 1.5–2% of cases. For an industry where developers’ margins have shrunk from 9–11% in 2022 to 3–5% in 2025, this opportunity is especially important.

That is why today the discussion about AI can no longer be reduced to the question of whether a company needs a neural network. Much more important is something else—where the technology is truly capable of increasing efficiency and profit, and where its implementation may lead to new managerial problems.

Just a few years ago, artificial intelligence was perceived mainly as a technical tool. Now, what comes to the fore is the speed with which a business can integrate it into real processes and obtain measurable results.

The scale of the global race is clearly visible in investment figures. In 2025, corporate investment in AI reached $581.7 billion. At the same time, private investment in the U.S. amounted to $285.9 billion, and in China to $12.4 billion. However, such a significant gap in investment no longer implies the same gap in the quality of technologies. The difference between the best American and Chinese models has narrowed to 2.7 percentage points versus 17.5–31.6 points three years earlier.

This changes the very logic of technological competition. Investment alone is becoming insufficient, as the quality of data, understanding the specifics of a particular industry, and the ability to quickly turn developments into effective solutions are gaining increasing importance.

For Russia, its own window of opportunity emerges here. The country is among the global leaders in total AI computing capacity, and domestic companies are developing their own models and solutions. At the same time, constraints remain—from access to certain components of computing infrastructure to a shortage of specialists and problems with international cooperation.

A separate question is what will happen to human capital. A study conducted under RSF grant No. 25-28-01469 identified an effect that may go unnoticed by management: professional skills begin to weaken 12–18 months before the problem becomes obvious. In other words, automation is capable not only of relieving employees’ workload but also of reducing opportunities to accumulate professional experience.

Therefore, businesses face a task more complex than ordinary automation. They need to catch the moment when it is still more beneficial to retrain an employee than to replace them, and at the same time integrate AI into the management system so that the technology strengthens rather than destroys human competencies.

By 2030, the advantage will likely not go to those companies that simply accumulate the maximum number of neural networks. The key will be the ability to combine technology with data, industry expertise, security requirements, and human capital. It is precisely this combination that can turn AI from a standalone digital project into a full-fledged tool for business management.

Olga Lebedeva-Yergunova is a PhD in Economics, Associate Professor at the Higher School of Industrial Management (IPMEiT, Peter the Great St. Petersburg Polytechnic University), Executive Director of the BRICS+ Women Scientists Association (BWSA), and principal investigator of RSF grant No. 25-28-01469.