Artificial Intelligence (AI) in occupational health and safety management. Moving to predictive analytics. Cases on neural networks analyzing Big Data, forecasting incidents, and tailoring training.
The application of autonomous AI agents and vibecoding technology to automate routine processes in industrial safety. The practice includes using neural networks to independently identify hazards from photos, fill out risk registers, calculate budgets, and generate local regulatory acts without involving IT specialists. Implementing this approach transforms the role of the HSE specialist into a "knowledge broker," radically reducing the time spent on analyzing regulatory frameworks and investigating incidents.
Phased implementation of artificial intelligence tools in occupational health and safety processes to automate routine tasks and data management. The practice includes preparing internal "knowledge brokers," launching a secure corporate analog of generative neural networks, and integrating AI with accounting systems. Implementing this approach in a mining company reduced labor costs for intellectual tasks by 20%.
Implementing artificial intelligence tools to automate routine HSE processes. Practices include using AI bots to collect Near Miss data, hybrid search systems (RAG) for regulatory databases, software robots (RPA) for reporting, and generating engaging content for briefings.
Integration of artificial intelligence into the daily tasks of an HSE specialist through mastering prompting skills and using no-code platforms. Practice includes automating routine processes, delegating analytical tasks to AI assistants, and transitioning to the Prompt First paradigm to improve management system efficiency.
Phased implementation of AI tools in the HSE processes of a large industrial company. The initiative began with creating simple chatbots for risk assessment and PPE without programmers, which engaged management and led to deploying an in-house local LLM within a closed security perimeter.
Implementation of a machine vision system based on the Open Source MoonDream model to monitor PPE usage and sanitary compliance in food production. The system analyzes real-time video streams, sends violation alerts to a Telegram bot for responsible managers within 15 seconds, and is accompanied by a positive employee motivation program without fines.
The use of generative neural networks to create training materials and visualize HSE risks. The practice includes using text, graphic, and video AI models to accelerate course development, translate into foreign languages, and create realistic briefings without involving third-party contractors.
Implementation of an end-to-end contractor risk management system focusing on maintenance work. Using digital tools (Power BI dashboards, electronic permits-to-work, heat maps) for online monitoring and management decision-making.
Integrating safety culture markers into management system documentation and using unconventional youth formats (rap compositions) to engage Gen Z in eco-safety and HSE issues. Using AI bots to automate routine tasks for HSE specialists, such as writing plans and initial assessment of non-conformities.
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A comprehensive approach to employee health management, including the use of anonymized VHI data to form risk groups, organizing "Health Schools," and nutrition control at remote sites. The practice demonstrates the importance of management engagement and adapting programs to the real needs of the staff, including the provision of medications on foreign projects.