‘To Keep Pace with Technology, Foresight Itself Must Become Faster’

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The Innovation and Technology Management Conference (InnoTech 2026), organised by Elsevier publishing house in partnership with Tsinghua University, has taken place in Beijing. The event focused on the challenges of managing technological development in the age of artificial intelligence. Olesia Maibakh and Danil Yatskin, researchers from the International Research and Educational Foresight Centre at the HSE Institute for Statistical Studies and Economics of Knowledge (ISSEK), presented the findings of the large-scale foresight study ‘IT in Russia: Development Scenarios’ conducted with the support of Yandex.

InnoTech 2026 took place on May 22–24, 2026, at the Renaissance Beijing Haidian Hotel. Leading international scholars and experts gathered to discuss how technology can be managed effectively amid accelerating digital transformation. The liveliest discussions centred on several key questions: how artificial intelligence is reshaping decision-making in innovation, why the platformisation of industry inevitably leads to a reconfiguration of values, and whether supranational institutions can steer innovation trajectories through strategic investment and regulation.

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During the AI Governance and Regulation session, Olesia Maibakh, Head of the Foresight Research Methodology and Organisation Unit at the ISSEK International Research and Educational Foresight Centre, delivered a presentation entitled ‘Research Priorities in AI.’ She examined the shortcomings of existing institutional approaches to governing and funding fundamental AI research.

Olesia Maibakh identified three key mismatches that hinder effective coordination between fundamental science and the public institutions responsible for supporting it.

First, emerging technologies and governance institutions operate on different timescales. The pace of AI research and deployment significantly outstrips the speed of decision-making on research funding. While the global body of AI knowledge doubles approximately every 23 months, national science and technology programmes may take as long as 48 months to be launched. This misalignment of institutional and technological cycles reduces the effectiveness of even substantial public investment.

Olesia Maibakh

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The second challenge, Olesia Maibakh argued, is the granularity gap between research objectives and government priorities. Public strategies typically focus on broad challenges—such as the ethical use of AI, labour market disruption, and AI governance—whereas fundamental research requires highly specific and narrowly defined decisions. Development institutions often struggle to respond quickly when proposed research falls outside these overarching strategic priorities. As a result, a gap emerges between high-level policy ambitions and the practical outcomes of AI research.

Finally, Olesia Maibakh highlighted what she described as a signal-type mismatch between science and development institutions. The effectiveness of public support is usually assessed through market-based indicators such as patents, investment, or commercial activity. Fundamental research, however, exists upstream of these market signals. At this stage, neither markets nor commercial indicators yet exist—and in some cases they may never emerge. Consequently, market signals are of little value when deciding which fundamental research projects deserve funding. Instead, policymakers must identify alternative, less obvious indicators, which will require a fundamental rethink of how research outcomes are evaluated.

‘Contemporary research in artificial intelligence and information technology faces a fundamental paradox: the subject of research evolves faster than new research and development cycles can be planned and funded. Foresight velocity is becoming a critical meta-capability for any organisation seeking to shape its technological future,’ said Olesia Maibakh.

To address the mismatches identified in their research, HSE University scholars proposed incorporating regular revisions of forecasts into technology foresight exercises. ‘To keep pace with technology, foresight itself must become faster,’ Olesia Maibakh commented.

Accelerated foresight would make it possible to adapt this approach to any highly dynamic field of science where knowledge expands more rapidly than planning cycles, and where innovation is primarily driven by technology push originating in research laboratories rather than by market demand.

Danil Yatskin

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Following the presentations, conference participants gathered in the exhibition hall for a poster session where they exchanged views and explored the research displays in greater detail. The poster presenting the findings of the foresight study ‘IT in Russia: Development Scenarios’ was prepared by Danil Yatskin, Leading Expert at the ISSEK International Research and Educational Foresight Centre. It attracted considerable interest from participants, whose questions focused primarily on two issues: to what extent the IT development scenarios and their underlying drivers could be applied to other countries and where Chinese and American IT developers would be positioned within the study’s scenario matrix.

The technology foresight study ‘IT in Russia: Development Scenarios’ was conducted in 2025 by the ISSEK International Research and Educational Foresight Centre with the support of Yandex. The research identified 55 factors with a critical impact on the current state and future development of the IT sector. Using two key uncertainties—the potential for international cooperation and the effectiveness of government support—the researchers developed four scenarios for the development of seven IT technology areas, ranging from artificial intelligence and the Internet of Things to cybersecurity and robotics. For each field, they outlined likely development pathways under different scenarios. The findings provide a valuable tool for shaping Russia’s long-term IT development strategy.

According to the study, artificial intelligence technologies are expected to develop actively in Russia under all projected scenarios for the IT sector. Their capabilities, realised through a wide range of digital applications and solutions, are likely to be integrated across healthcare, education, finance, and other sectors, leading to significant improvements in the accuracy and efficiency of diagnosis, forecasting, and recommendations.

The HSE researchers argue that although restrictions on access to computing resources and data significantly affect the pace and scale of AI development, there is no need for government support in most cases. Private investment and innovative business models help keep the AI sector flexible and less dependent on external conditions or public policy.

At the same time, issues relating to data protection, privacy, and the potential misuse of AI are expected to become increasingly important. The researchers believe that maintaining public trust in artificial intelligence will require the introduction of clear standards and regulatory frameworks based on appropriate legislative initiatives.

The study also found that information systems and digital applications continue to transform business by making it more efficient and transparent. However, their successful implementation and widespread adoption will depend on addressing a range of technological, ethical, and regulatory challenges.

Researchers from the HSE Graduate School of Business also participated in InnoTech 2026. At the conference, organised by Elsevier publishing house and Tsinghua University, Anja Tekic, assistant professor at the Department of Strategic and International Management, together with Zeljko Tekic and Mikhail Komarov, professors at the Department of Business Informatics, presented the findings of their research and discussed contemporary approaches to innovation and technology management in the digital and intelligent era. More information about their presentations is available via the link.