AI as a Content Factory

The implementation of artificial intelligence is no longer just a technological trend; it is becoming a necessity established at the state level. Given the growing volume of tasks and the acceleration of work processes, HSE specialists need new tools to optimize routine tasks. Vera Konshina, representing Yandex, analyzes practical experience in using generative neural networks to transform corporate communications and create training materials on industrial safety.

Risk Visualization and Content Adaptation

A huge percentage of an HSE specialist's daily work involves communication and training. Traditional text instructions and regulations often fail to resonate with employees, especially the younger generation, who absorb visual information better. The presentation details the transition from dry documents to AI-generated videos.

• Process Acceleration: Using AI allows training courses to be translated and adapted into dozens of languages in a matter of weeks, which would normally take months of work.
• Safe Visualization: Neural networks make it possible to create realistic images of hazardous situations and risks without the need for staged photoshoots at real production sites, which could violate ethical or safety standards.
• Resource Savings: Generating images and videos reduces dependence on designers and expensive filming equipment, allowing the HSE specialist to independently meet visual content needs.

From Text Prompts to Video Creation

The speaker demonstrates by example how to build a step-by-step workflow with neural networks, gradually increasing task complexity. It is recommended to start with basic text models (such as YandexGPT, GigaChat, or Deepseek) for drafting short incident reports. The next step is generating images for internal presentations, taking corporate colors into account.

The most complex but effective stage is working with video and audio. Instead of trying to generate a complex video sequence from scratch, which often leads to visual distortions (artifacts), a combined approach is proposed: first, a high-quality static image is created, which is then "brought to life." For voicing briefings, speech synthesis services are used, providing realistic voices without the need for studio recording.

Prompt Engineering and AI Logic Verification

The quality of the result directly depends on how the task is formulated. The speaker analyzes common generation errors when the neural network adds unnecessary details (for example, distorted proportions or extra fingers). To avoid this, a strict prompt structure must be applied:

• Set a specific role and target audience.
• Explicitly prohibit the generation of unwanted elements.
• Fix the style (realism, comics, corporate colors).
• Decompose complex videos into multiple short scenes.

Before starting serious work, it is recommended to test the selected model's ability to build logical connections by asking it an absurd question (for example, about heating an object in a freezer). This allows you to assess the adequacy of the algorithm.

Information Security When Working with AI

One of the key barriers to implementing AI in a corporate environment is the risk of data leakage. Using open cloud models means that uploaded information can be used for further algorithm training. To protect confidential enterprise information, it is necessary either to conduct a strict data anonymization procedure (removing legal details and personal data from risk assessment cards) or to use closed corporate AI perimeters operating under secure B2B contracts.

What you will learn from this webinar:

• How to reduce the time spent on developing and localizing HSE training courses using neural networks?
• What tools to use for free voiceovers and animation of briefings without involving contractors?
• How to properly compose a text prompt to avoid visual distortions in generated illustrations?
• How to safely use cloud AI services to analyze incident statistics without the risk of corporate data leakage?
• How to test the selected neural network for logic and professional suitability before starting work?