Artificial Intelligence advancements are picking up momentum.
Sergey Dutov, Director of Corporate Innovations at the Skolkovo Foundation, has outlined several key use cases and strategies for the application of Generative AI (GenAI) in Russian industry.
Use Cases of GenAI in Russian Industry
GenAI is set to revolutionise various sectors of the Russian industry. Here are some of the main use cases:
- Product Design and Prototyping: GenAI can accelerate the design process by generating multiple design variations and optimising prototypes using AI-driven simulations.
- Predictive Maintenance and Operations Optimization: Leveraging GenAI to analyse equipment data can predict failures and optimise industrial processes, thereby reducing downtime and costs.
- Automation of Routine Tasks: GenAI can be used to automate repetitive and data-intensive tasks, such as report generation, documentation, and compliance checks.
- Enhanced Decision Making: Integrating GenAI to synthesise large datasets and provide actionable insights enables better strategic planning and operational decisions.
- Training and Skill Development: Utilising AI-generated training materials and simulations can efficiently upskill the workforce.
Strategies for Implementing GenAI in Industry
Implementing GenAI in industry requires a thoughtful approach. Here are some strategies Dutov highlighted:
- Collaboration Between Corporates and Startups: Encouraging partnerships between large industrial companies and innovative startups, particularly those within the Skolkovo ecosystem, to co-develop AI solutions tailored to specific industrial challenges.
- Focus on Data Infrastructure: Emphasising the importance of building robust data collection and management systems to feed accurate and high-quality data into AI models.
- Incremental Integration: Advocating for a step-by-step approach to AI adoption, starting with pilot projects before scaling up across operations.
- Regulatory and Ethical Frameworks: Considering the importance of developing regulations and ethical guidelines to ensure responsible AI use.
- Investment in Talent and Innovation: Prioritising the development of AI expertise within companies and fostering an innovation-friendly environment.
Dutov emphasised that GenAI holds transformative potential for Russian industry by enhancing efficiency, innovation, and competitiveness when implemented with careful strategy and collaboration. However, it's important to address employee resistance to change, with staff understanding the general principles of solution operation and applying them regularly.
The relatively high cost of infrastructure and specialists can be a barrier, but these issues are being addressed through collaborative efforts. Breakthrough projects are undertaken by companies with a high level of GenAI maturity and include generative design and the use of GenAI in R&D.
In the production sphere, generative AI demonstrates its effectiveness in various auxiliary solutions, including co-pilot tools for technologists, metallurgists, and engineers, and integrating with computer vision systems that monitor compliance with technological processes and product quality.
Leading production companies are open to various collaborations and flexible in project implementation, using domestic vendors' equipment, involving specialized system integrators, seeking expertise and resources from scientific labs, and providing their facilities as a test zone for startups, sharing risks and pilot project costs.
Innovative projects that fall under the category of "low-hanging fruits" include solutions that support current business and production processes, such as intelligent search, automatic meeting protocol generation, and optimization of procurement processes.
Two main scenarios for GenAI application in industry are "breakthrough" and "supporting". The development of proprietary SLM (Small Language Models) for IIoT (Industrial Internet of Things) devices is promising, particularly for models that operate on data from specific equipment within a single workshop.
The country is adopting the RAG (Retrieval Augmented Generation) concept as a base solution for improving response accuracy. GenAI penetration in Russian industry, which accounts for over 30% of the country's GDP, is expected to have a significant impact.
Large investment projects, high levels of digitalization, science-intensive production processes, and R&D create a favourable environment for GenAI adoption. By integrating GenAI with predictive analytics and planning systems, equipment downtime can be reduced by up to 50%. Advanced companies are testing generative AI technologies, with a mass transition to industrial implementation expected within the next one to two years.
Artificial-intelligence, with its applications in Generative AI (GenAI), can significantly contribute to the optimization of Russian industry by enhancing predictive maintenance, product design, routine task automation, and decision-making (Use Cases of GenAI in Russian Industry). To effectively implement GenAI, strategies like collaboration between corporates and startups, focus on data infrastructure, incremental integration, development of regulatory and ethical frameworks, and investment in talent and innovation are crucial (Strategies for Implementing GenAI in Industry).
