On April 7, RIGF 2026 hosted a foresight session titled AI as a Catalyst for Disruption, focusing on how artificial intelligence is already reshaping governance, the economy, and social processes – and how these transformations may evolve through 2035.
The session was moderated by Konstantin Vishnevsky, Director of the Center for Strategic Analytics and Big Data at the Institute for Statistical Studies and Economics of Knowledge, Higher School of Economics. The discussion centered on findings from the iFORA big data analytics system, developed by Institute and powered by advanced artificial intelligence technologies, which analyzed more than 850 million documents. As a result, the experts produced a semantic map highlighting the processes already being transformed by AI today.
The analysis highlighted both opportunities and potential risks associated with AI development across three key domains: the economy, governance, and society. This framework shaped the subsequent discussion, as experts compared the identified trends with their own practical experience in developing and implementing AI solutions.
In his presentation, Konstantin Vishnevsky noted: “Within the management framework, key opportunities include data-driven decision-making, faster management cycles, and greater precision in process control. At the same time, risks involve increased dependence on data quality, shifts in the balance between human and machine roles, and heightened requirements for the security of managerial decisions.”
Senior executives from leading Russian media organizations shared their views on the opportunities and risks AI presents for content creation, distribution, and communication.
“We use AI to generate product hypotheses that reflect audience needs and make user interactions with our services more intuitive and valuable,” said Veronika Kolodko, Managing Director of Rambler.
According to her, adopting such technologies requires a comprehensive overhaul of internal production processes. For instance, rapid testing on “synthetic” users enables editorial teams to quickly assess how effectively a text meets reader expectations. This approach significantly reduces both time and costs, as insights for further product development can be obtained within a single day.
This level of technological adoption enables businesses to better define the objectives of content creation. According to Veronika Kolodko, long-term strategic planning using neural networks – spanning several years ahead – is an ambitious goal that is likely to become achievable in the near future.
Kirill Sidorov, Director of Digital Development at TASS, suggested that some media outlets risk disappearing as search engines and AI models increasingly bypass them, reducing traffic, citations, and audience engagement.
“Search engines now aggregate and summarize the news, presenting a unified picture of the day, but we receive no citations, no clicks – nothing. At some point, many media outlets may simply cease to exist because no one will know about them. Either developers of AI systems must compensate content creators for the news they use, or those creators will gradually disappear,” Kirill Sidorov said.
According to him, TASS is experimenting with its own AI model, drawing on one of the oldest news archives, but the agency is still far from entrusting news production to neural networks. Kirill Sidorov noted that TASS correspondents produce content at a pace comparable to AI.
Assessing the broader media landscape, he noted that a significant share of news content is already being generated by AI through the rewriting of news feeds. At the same time, aggregators are attempting to push back against this wave.
“They are trying to create services where audiences will actually read news produced by humans rather than artificial intelligence,” Sidorov said.
Vyacheslav Bersenev (NEUROLAB, Association of Artificial Intelligence Laboratories), Yevgeny Osadchuk (Skolkovo), and Mikhail Skvirsky (Sberbank) also took part in the discussion.
The conversation on artificial intelligence continued on the second day of RIGF 2026 during the session, How Nations Are Regulating Generative AI: The Evolution of International Approaches. Experts explored global regulatory models for generative AI, the impact of regulation on competition and market entry for new players, as well as potential requirements, priorities, and legal frameworks for AI development in Russia.
As session moderator Karen Kazaryan (Digital Economy) noted, the draft law “On the Fundamentals of Artificial Intelligence Regulation” has been actively discussed in Russia over the past few months, making it one of the most widely debated legislative initiatives in recent years.
Anastasia Kabayeva (TeDo) presented the findings of a study on regulation in the technology sector. The key takeaway is that 70% of the jurisdictions analyzed are opting for a soft regulatory approach, based on self-regulation at the level of institutions, industries, and professional communities. Notably, even countries that initially pursued stricter frameworks, such as the European Union and China, are now beginning to ease certain regulatory requirements.
Damir Salikhov (Yandex) provided an overview of current global trends in artificial intelligence regulation.
Anna Malinovskaya (Rostelecom) highlighted the need to combine soft and hard regulatory approaches in the AI field, emphasizing the importance of the Code of Ethics adopted by the AI Alliance and supported by thousands of companies.
Yevgeny Osadchuk (Skolkovo) addressed the challenge of balancing technological sovereignty with the pursuit of maximum performance in AI solutions.
Marat Takhaviev (Big Data Association) addressed the issue of data availability for training domestic AI models. He outlined two key approaches: generating synthetic data for safe modeling, and using AI to anonymize real datasets while preserving their analytical and training value.
Participants also stressed the importance of drawing on international experience to develop a balanced regulatory framework for AI in Russia.