📋 Tender Overview
Organizers of the TNF Industrial Energy Forum and the Platform Social Design Center, supported by Sber experts, released a study examining the adoption of Generative Artificial Intelligence (GenAI) in Russias oil and gas industry. Entitled A Business Paradigm Shift or Just Another Technology?, the report analyzes different aspects of implementing GenAI in the field. The study notes that while local pockets of GenAI adoption exist in Russias oil and gas sector, traditional AI remains widely utilized in production processes. Further growth holds considerable promise. Factors influencing the pace of GenAI integration include companies digital culture, employee qualifications, data quality, and regulatory frameworks. Case studies of successful GenAI implementations are presented, covering both Russian and global contexts. Experts identify exploration and extraction as the primary priority for GenAI deployment, endorsed by 66% of survey respondents. Other key areas include backend task-specific services (45%) and refining operations (37%). Their rationale reflects both economic returns and availability of robust datasets. However, barriers persist, hampering widespread GenAI uptake. Leading administrative obstacles cited in the survey include lengthy internal approval processes (47%), lack of skilled personnel (47%), and conservative leadership attitudes toward GenAI (39%). On the technical front, concerns revolve around retooling production lines (46%), inadequate adaptation of AI models to specific industry requirements (45%), and low automation and digital maturity levels (43%). Gradual GenAI rollouts yield benefits, boosting operational efficiency, safety, and reducing running costs. Maximized gains hinge on establishing industry standards, nurturing competence centers, hiring experienced specialists, hybrid modeling, and fostering a culture of deliberate GenAI utilization in industrial settings. Successful transformation depends on collaborative efforts between the government, businesses, and academia. Implementing AI solutions raises critical managerial decisions, each carrying distinct implications. Key choices include treating AI as a mere tool versus a deep-rooted business transformation, centralized versus decentralized deployment strategies, cloud versus on-premise architecture, and prioritizing short-term wins versus long-term strategic goals. The study summarizes Sbers experience transforming itself with GenAI and highlights joint initiatives with leading oil and gas players. Essential to such transformations is a comprehensive platform that empowers the creation of AI agents, integrates organizational data, and retrains models. Central to this approach is reliance on domestic foundational models developed exclusively in Russia. As illustrated by Sbers GigaChat, the bank maintains control over all phases of its evolution, tailoring it with proprietary data--crucial for industrial-scale deployments. The full report is available on the website.
