On April 8, 2026, a case study session titled “The Economics of AI: Does It Add Up or Not?” was held as part of the Data Fusion conference on data analysis and artificial intelligence technologies.
Undoubtedly, AI has the potential to dramatically reduce labor costs and help save time. However, the question of converting this time into real money remains a matter of economic art. The obvious cost reduction through staff reductions often fails to recoup even the company’s investment in developing and owning professional AI. Therefore, the focus is on AI application models that transform the labor process itself and allow for quantifiable impact assessments, taking into account specific costs and risks, such as those associated with AI errors.
The session discussed:
- Successful, non-trivial cases of turning “time into money” through AI in companies.
- The economic aspects of implementing AI in highly specialized production processes with a significant share of labor.
- Quantitative assessment and forecasting of the economic effects of AI implementation: approaches, models, and results. Do modern economic theories work well with AI?
- “Reconnaissance in force”: how to assess the economics of ai in the early stages of development and implementation? What signs can be considered as harbingers of inefficiency?
- Practical aspects of developing the cost of AI ownership: how to consider the risks associated with inevitable errors? insurance for AI-related risks.
- Profound Transformation of the AI economics in industries: how will the costs of individual work be redistributed once all market participants successfully implement AI? Could this lead to a situation where implementation costs won’t pay off? What should we do: be first to implement or wait for the market to restructure?
The session was moderated by Alexander Bukhanovsky, ITMO University, Head of the “”Strong AI in Industry” Research Center.
The session program included the following presentations:
- Elena Belobrova, Yandex Cloud, Head of ML&AI Business Development
AI: Where is the Money, and Where is an Expensive Experiment - Ekaterina Kanunnikova, VK Tech, Director of Product, Data Services
Before and After the Model: How to Calculate an AI Project Fairly - Yuri Hohlov, Institute of the Information Society, Chairman of the Board of Directors, full member of the Russian Engineering Academy
The Economics of AI: From Creation to Impact through Use - Dmitry Ruzanov, Alfa-Bank, Director of the Model Development Department
How AI Turns Time into Money: A Model Approach and New Pricing at the Bank - Yuri Borodachev, National Research Nuclear University MEPhI, Deputy Director of the Artificial Intelligence Center
The Economics of AI: The Real Sector. Is it Aligned or Not? The Problems of Expectations and Effects from Hardware Consumers
