According to analysts’ forecasts, by 2030, the generative artificial intelligence (GenAI) industry will reach a capitalisation of $356 billion. This is a clear signal: AI is no longer just an experiment with chatbots, but a key element of the operational efficiency of modern business.
❓ Why do businesses choose GenAI?
Generative AI works at the intersection of NLP (natural language processing) and machine learning, allowing you to create unique content: from code and SEO texts to complex designs and synthetic data.
🔑 Key areas of implementation:
- content generation: automation of product descriptions and marketing strategies
- product development: use of neural networks to improve code and analyse market trends
- customer service: implementation of intelligent assistants that respond to queries 24/7
🚀 How to start implementation?
The process of creating your own AI business model includes stages that are critical for cyberneticists:
- Task prioritisation: start with low-risk routine automation (planning, data entry).
- Working with data: this is the foundation. This stage includes collecting, cleaning, labelling, and distributing data into training, verification, and test sets.
- Choosing algorithms: using transformer architectures, GANs (generative adversarial networks), or variational autoencoders (VAEs) depending on the type of task.
- Deployment: integration of the trained model into the production environment via cloud computing.
For students and specialists in economic cybernetics, the implementation of generative AI is a striking example of how modern data, algorithms, and management decisions are combined in real business cases and shape the skills of the future.
Information prepared based on materials from ChatGPT Academy: https://www.chatgptacademy.online/sfery-vykorystannya-ai/biznes/efektyvni-strategiyi-vprovadzhennya-generatyvnogo-shi-v-biznes/