As noted in the study, the real estate development and property market remains one of the largest sectors of the Russian economy. In 2025, the annual market volume amounted to RUB 15–23 trillion. At the same time, the sector is facing increasing pressure: developers’ average net profit margin declined from 9–11% in 2022 to 3–5% in 2025, while construction productivity in Russia remains below the economy-wide average — RUB 1.4 million of gross value added per employee versus RUB 1.8 million in most other sectors. A further constraint is the labor shortage. Already in 2025, developers report a shortage of 17%, or 1.2 million people, and by 2030 the shortage in construction and real estate could reach around 10%, or 800,000 people. Under these conditions, AI is no longer an experimental technology but is becoming a tool for reducing manual labor, accelerating processes, and improving project manageability.The impact from AI implementation does not arise automatically: the main challenge lies not only in choosing technologies, but also in selecting truly applicable use cases, assessing their economic potential, and evaluating the organization’s readiness for scaling. As part of the study, we analyzed real Russian and international cases, validated them with experts in Russia and abroad, filtered out irrelevant solutions, and thereby shortened the path from searching for ideas to practical implementation
Anna Danchenok, Partner at Yakov & Partners. and Head of the Real Estate and Territorial Development Practice
From pilots to impact
Most Russian developers are already testing AI, but large-scale industrial implementation has not yet taken place, the experts acknowledge. According to the study, 64% of companies are experimenting with AI in an unstructured mode, 27% are at the active development stage, and only 9% are at the stage of scaling AI solutions.As part of the study, the experts reviewed 175 AI use cases in real estate development, of which 80 were assessed in detail in terms of potential impact and practical feasibility. The experts identify solutions in sales and marketing, project scenario development, construction and installation works, and site selection as the most promising areas for the first wave of implementation. As part of the study, the experts reviewed 175 AI use cases in real estate development, of which 80 were assessed in detail in terms of potential impact and practical feasibility. The experts identify solutions in sales and marketing, project scenario development, construction and installation works, and site selection as the most promising areas for the first wave of implementation. Priority use cases include AI-based forecasting of prices and sales rates, monitoring of competitors’ offerings, dynamic apartment pricing, end-to-end analytics of marketing funnels, as well as the generation of personalized content and lead scoring. In individual pilot and vendor cases, such solutions demonstrated up to 7% growth in monthly revenue, up to 3% revenue growth from dynamic pricing, and up to a 20% reduction in advertising costs through budget reallocation. A second important area is generative design of master plans and parametric optimization of development projects, the experts note. These solutions make it possible, at an early stage, to compare building placement options, site constraints, and project economics more quickly. According to pilot and vendor cases, an AI platform for parametric optimization can generate 2.5 times more design options and reduce design time by up to 75%. However, such effects depend on the implementation context and the quality of the input data, the study indicates. The main barriers to scaling AI are related not so much to technology availability as to management. Among the key limitations, the experts point to poor data quality, information security risks, the absence of an implementation strategy, blurred roles between IT, business, and legal teams, underestimation of change management, and weak linkage of AI initiatives to KPIs. For example, 64% of developers acknowledge that they underestimate the importance of appointing a business owner for use cases and allocating a budget for data quality and integrations. Another 55% underestimate change management and user training, as well as the need for a unified portfolio of AI use cases and its prioritization. At the same time, among companies with standardized architecture, the share of processes at the implementation or scaling stage reaches 36%, whereas with fragmented architecture it is only 2%.At the same time, around 90% of developers report a positive effect from pilot solutions, but only one in five companies is able to quantify the result. Even among advanced players, the contribution of a single AI solution to EBITDA usually does not yet exceed 0.5–1%. This demonstrates the gap between the mere launch of a pilot and the ability to extract sustainable business value from AI
Natalia Kuvaeva, Project Leader and expert at Yakov & Partners.
Looking to the future
International experience shows that AI is adopted more quickly wh ere the state creates digital infrastructure for the industry: BIM standards, digital permitting, data platforms, and machine-readable requirements. For example, in China, the program covering 24 pilot cities in the field of “smart construction” enabled the launch of 758 demonstration projects and the creation of 39 innovation platforms. In Singapore, the CORENET X digital platform for approvals in construction and real estate, mandatory use of building information modeling, and tax incentives have created conditions for the application of artificial intelligence and robotic solutions. In the United Kingdom, digital planning helps process urban planning documents in minutes instead of hours, while in the United States, in New York, AI pilot projects in permitting, zoning checks, and construction supervision can save experts up to 25% of their time. In Russia, this foundation is also being formed. Since July 1, 2024, the industry has transitioned to mandatory use of BIM at the design stage, and since January 1, 2025, at the construction and installation works stage.Under these conditions, the competitive advantage will go not to the companies that are first to purchase AI tools, but to those that can embed them into project economics, the data system, and KPIs. For the market, this means a transition from fragmented experiments to an industrial implementation framework in which AI affects margins, timelines, risks, and the speed of decision-making
Anna Danchenok, Partner at Yakov & Partners. and Head of the Real Estate and Territorial Development Practice.
