In an era of rapid technological change, HSE goes beyond traditional briefings and types of supervision. The application of digital technologies and artificial intelligence is gradually transforming the approach to safety — from control and reporting to forecasting, prevention, and systemic management.
In this article, we will discuss exactly how technologies, such as chat models and low-code platforms, are being integrated into the practices of HSE departments, what opportunities they unlock, and why the key to successful implementation lies not in replacing the specialist, but in enhancing their role.
Traditionally, HSE specialists face the reality that up to 60% of their working time is spent on paperwork, reporting, and record-keeping. Today, however, the situation is changing. The use of systems capable of collecting and analyzing large datasets reveals a new paradigm: not just recording violations and incidents, but predicting their occurrence and taking proactive measures.
For example, research shows that digitalization and AI solutions are becoming a logical stage in the evolution of HSE, encompassing process automation, the transition of analog processes to digital formats, and the implementation of data collection and analysis systems.
Thus, the HSE department gets a new chance to transform — from a reporting unit into a strategic business partner focused on risk management and injury reduction.
The introduction of chat models (such as ChatGPT) and low-code/no-code application development platforms opens up new tools for HSE specialists. They do not replace the expert, but rather expand their capabilities:
The expert can formulate tasks in a chat model, for example: "Analyze injury data over the past five years and suggest 5 key risk factors."
The chat model generates various scenarios, templates, reports, and prompts.
On a low-code platform (with minimal coding or no programming at all), a prototype dashboard is created: metric visualization, monitoring, and preventive tasks.
The resulting solution is tested, refined, and implemented — the specialist remains a coach and coordinator, rather than just a document administrator.
This approach increases the speed of solution development and reduces the burden of routine operations. AI solutions in HSE help detect potential hazards, reduce human error, and improve departmental efficiency.
The transition to digital solutions is often complicated by the fact that HSE departments lack in-house developers and are reluctant to invest in large IT projects. This is where low-code/no-code platforms come to the rescue:
The HSE specialist works with a chat model, formulates tasks, and receives a proposed structure for an application or dashboard.
A prototype is created on a platform (e.g., Replit, Bolt, Glide, Bubble) — featuring data uploads, filters, and visualization.
Testing on real cases and data, followed by further adjustments.
Implementation — either as a standalone application or migrated to the company's internal servers.
Advantages: rapid launch, lower costs, the ability for the HSE department to participate in solution creation without full dependence on the IT department, and the capability to prepare clear and transparent technical specifications with interface visualization.
Following the implementation of AI projects, the following positive effects are already being noted:
Reduction in manual operations: less time spent on data entry, reports, and spreadsheets.
Faster response times to new risks.
Increased employee engagement: when they see an interactive dashboard and receive recommendations, they feel like active participants in the process.
Shorter development cycles for new tools: the time from idea to MVP has decreased.
Resource savings: less dependence on external contractors, greater departmental autonomy.
However, the realities of technology implementation are not always rosy. Here are a few key challenges:
Regulatory and legal framework: For example, the use of AI and robotics in HSE requires appropriate legal support.
Risk of overwork: Studies show that with the introduction of AI, the workweek sometimes gets longer because technologies increase productivity, but employers may also increase the workload.
Specialist training: Skills in data handling, prompt engineering, and understanding algorithms are needed; HSE specialists must evolve.
Data quality and infrastructure: Systems require reliable input data and proper architecture. Without this, the impact may be minimal.
Safety culture: Technology cannot replace culture. If employees are not engaged and do not understand the goals, the results will be weak.
Ahead lies not just individual applications, but an entire occupational safety ecosystem built on AI and digital services. It may include:
Chatbot assistants for HSE specialists, ready to answer questions, generate templates, or create scenarios.
Modules for the automatic uploading of regulatory documents and corporate requirements, with the ability to generate "guides" for different departments.
Interactive dashboards that collect data from various sources (incidents, medical records, training, business trips) and predict risk zones.
Archive analytics: not just data storage, but trend identification, incident forecasting, and early warning.
Mobile applications for employees: risk notifications, instructions, data uploads, and training participation.
Today, HSE departments stand on the threshold of a new era. If the implementation of technologies is accompanied by a change in roles, approaches, and safety culture, we will see a significant shift: a reduction in injuries, fewer incidents, and improved working conditions. But it is important to remember that technologies will not replace humans; they will enhance them. Success depends on how ready the department is to become a digital partner rather than just a service unit.
📌 Actionable Recommendations:
Start with a small pilot: a chat model + a low-code application in one specific area (e.g., high-risk work types).
Train HSE specialists in basic skills: formulating tasks in chat models, working with data, and managing prototypes.
Integrate the new solution with existing processes so it doesn't become just an isolated gimmick.
Monitor the results: time spent on reports, number of incidents, and employee engagement.
Maintain a safety culture: without it, technologies will remain nothing more than a beautiful interface.
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Kirill Leonidovich Tomashevsky, Dinara Ilyasovna Kiyamova. "Application of Artificial Intelligence and Related Robotics in the Field of Occupational Safety." Journal "Labor Law in Russia and Abroad," No. 01/2025. YURIST Publishing Group
ISTC "CIS" Blog: Neural Networks and Work Optimization in the Field of Occupational Safety in Russia. August 6, 2025. sngrf.ru
Article "How Artificial Intelligence Helps in Occupational Safety." "Komsomolskaya Pravda," April 29, 2025. samara.kp.ru