GenAI is more than just a technology; it’s a powerful tool already reshaping the rules of the market. Industry leaders are leveraging GenAI to enhance efficiency, streamline processes, and solve complex business challenges. It is becoming one of the key strategic areas for market development and must be made accessible to all companies in Russia.

• What benefits can industrial enterprises gain from adopting GenAI today?
• How do you implement GenAI correctly and avoid mistakes? We have the answers to all your questions!
• Why is it important to act now?

We will guide you through key global robotics trends and show where Russia stands on this map. You’ll learn about the major challenges facing the domestic robotics sector and how to overcome them.
Through real-world cases, we’ll demonstrate how the integration of robots is already transforming business processes in Russia. Currently, there are 19 robots per 10,000 industry workers—the goal is to increase this tenfold by 2030.
The main focus: the revolution AI is bringing to robotics. Why is now the critical time to begin implementing these technologies?

As AI services become more popular and embedded in daily life, supply in this area has naturally increased. Neural networks are used in diverse domains—from routine text and image tasks to advanced B2B and B2G solutions. However, this also brings new challenges: many similar products with unclear competitive advantages and the difficulty of turning original ideas into viable commercial products. We will explore how to identify new, commercially successful niches in AI, avoid functional duplication, and understand the key issues that AI services should address for consumers, businesses, and the state in the near future.

The day is not far when robots become an integral part of daily life—not just as factory assistants or smart home speakers, but as companions for the elderly and autonomous systems making decisions in critical situations. By 2030, the global service robotics market is projected to reach $170 billion (Boston Consulting Group), and in Russia, robotic processes in industry could grow to 25% by 2025 (NAURR).
But their introduction raises profound societal questions:

•  How do we balance automation and job preservation?
•  How do we prepare people for new professions—60% of which don’t yet exist but will require AI interaction (WEF)?
•  How can humans and robots complement—not compete with—each other?
•  What about those unable to adapt? How do we avoid digital inequality?
•  And how do we retain our humanity in a world where robots become increasingly humanlike?
•  How can humans and robots co-author the next chapter of history?

The concept of Industry 6.0 relies on widespread use of large language models (LLMs) like ChatGPT, Qwen, DeepSeek, GigaChat, and visual-language models (VLMs). All stages of production—including code generation—are executed without human involvement. A text or voice command initiates an autonomous production line. Cloud-based AI controls the process while robots equipped with GenAI independently perform complex actions. Other AI robots design, produce, assess quality, service, and repair equipment. A heterogeneous swarm of cobots, anthropomorphic, mobile, industrial robots, and drones manages all production stages without human input.

The process begins with product description in natural language, outputting files ready for 3D printing. LLMs then define parts, primitives, and connections needed for functionality, generate Python code for 2D geometry, and finally issue assembly instructions. The resulting 3D models are exported to STL files and used for printing. A robotic arm transfers the printed components to a drone for delivery to the next production stage.

Even in its experimental phase, this system showed over 4x productivity gains—legitimately calling it a prototype of Industry 6.0. Speakers will discuss Industry 6.0 methodologies and GenAI implementation prospects, share use cases, and outline steps for establishing AI Centers in enterprises.

Artificial intelligence is rapidly transforming the world around us. Generative AI technologies are reshaping business, the economy, and society. Ahead lies the era of multi-agent systems and the emergence of AGI—Artificial General Intelligence—comparable in capability to humans. This new level of technological development will enable the resolution of complex problems, the creation of unique products, and the optimization of decision-making processes. As part of the AI Journey track, leading researchers from across the country will discuss technological progress, share their vision for AI’s future trajectory, and explore the prospects for AI development in Russia and globally.

This panel discussion focuses on the practical implementation of generative AI in government and business. It will address infrastructure challenges and opportunities introduced by generative AI technologies and explore democratizing access to GenAI through cloud and hybrid technologies to solve business tasks. The session will also examine the boundaries of AI use in digital business, transportation, and government, including aspects and case studies related to critical information infrastructure (CII).
● Technological challenges for GenAI in government and business
● Hybrid cloud solutions for delivering AI to enterprise infrastructure
● The level of digital maturity in government and business; experience from digital enterprises
● Rising demand for AI-as-a-service models in government and business
● AI beyond CII restrictions
● AI in government and business: pilot and implemented projects, features and results

Representatives of Russian IT companies will discuss the barriers to scalable AI monetization, present use cases, and share practical approaches to selecting tools—AI agents, model aggregators, or alternative solutions.
Key topics include:
● Promising formats: AI agents, model aggregators, virtual assistants
● Top 3 scenarios for using large language models to generate economic value
● What determines the cost of a generative AI project
● How to select the right model
● How much it costs to develop a large language model—and whether it’s worth doing in-house

The digital transformation of the state is no longer possible without large-scale implementation of AI. Are Russian manufacturers ready to provide hardware infrastructure for AI—and which graphic accelerators can they offer? The practical use of quantum computing is becoming a reality. Quantum architecture imposes specific requirements on hardware, which in turn requires specialized manufacturing technologies. Will quantum computers become mainstream products in computing hardware portfolios? Is the industry prepared to meet this challenge?

AI in industry enables a new level of decision-making by automating routine processes, responding quickly to changes, and improving decision quality. This increases productivity, shortens commissioning and delivery times, and creates greater economic impact.
Human decisions are based on facts, external influences, goal-setting, and accumulated experience and knowledge. The volume of accumulated experience helps accelerate and improve decision-making.
Can the decision-making process be digitized through the use of digital twins of enterprises, sectors, and the broader economy?
Data used in decisions—worth billions—may pertain to specific business processes or impact entire production chains. In the case of digital twins, built on mathematics, data, and computing power, decision quality depends on data quality.
What should be done to ensure industrial digital twins improve the quality of decision-making and deliver added value to the Russian economy?

In the digital age, as the line between reality and simulation blurs, the media industry faces new opportunities and threats. On one hand, AI and personalization algorithms bring unprecedented transformation potential; on the other, they heighten the risk of misinformation, deepfakes, and manipulation. Media of the future may broaden human perception but also challenge traditional mechanisms of information control. What will the future of media look like? Which information channels will maintain public trust, and which ones will collapse?
● What principles should guide media in maintaining audience trust amid rapid technological change?
● How are AI and machine learning altering content creation and moderation?
● Cybersecurity and ethics—where is the line between innovation and destabilization of the media landscape?
● What happens when reality and virtuality blur, making perception highly manipulable?
● How can we preserve critical thinking and promote digital literacy to avoid the “synthetic reality” trap?

Artificial intelligence has become an integral part of filmmaking, transforming production, editing, and even screenwriting. Today, AI helps create visual effects, speeds up image processing, and generates complex animations and visualizations that once required immense effort. AI software is also used to analyze scripts and predict audience reactions, helping producers make better decisions. Furthermore, AI can generate digital actor images—or entirely new characters—unlocking fresh creative possibilities.
While production is optimized and analytics improved, concerns persist. AI adoption may reduce creative jobs and raises legal issues around using deceased actors’ likenesses. Who holds the rights? Who gets paid? These questions remain unresolved.
● Can AI truly replace human intuition and creativity in script selection and adaptation?
● What are the downsides of overly automating film production? Will it stifle creativity?
● What is the future of acting when AI becomes deeply integrated?
● Is AI a creative partner or just a technical tool? Can it co-create cinema, or will its role remain purely auxiliary?

As increasingly complex high-tech industries emerge and workforce shortages grow, the need to improve access to industrial autonomy technologies becomes critical. These technologies can boost productivity, enhance safety, increase automation, and foster new workforce skills.
Autonomy is a holistic industrial state achievable through several interconnected factors:

1.     Digital technologies (digital twins, AI, robotics) as part of a comprehensive approach to production management;

2.     Personnel competencies for managing high-tech assets;

3.     Government standards on industrial safety and equipment operation.

● Raising the level of autonomy requires collaboration among industry, government, academia, and IT players.