Generative artificial intelligence (GenAI) is becoming one of the most important tools today for organizations seeking to automate processes, make better use of data, and create personalized customer experiences. However, the real value comes not from the technology itself, but from its application in specific business processes.
“Generative AI isn’t about text generation; it’s about the ability to effectively leverage an organization’s knowledge,“ says Aistis Raudys.
Key Terms
Generative AI is a type of artificial intelligence capable of creating new content—such as text, code, images, or other data—based on learned patterns. One of the most important forms of this technology is large language models (LLMs), such as ChatGPT or Gemini, which can understand and generate text that is human-like.
Chatbots are also widely used in business (interfaces that enable communication with customers or employees, as well as API integrations that ensure seamless communication between different systems). At the technical level, solutions are often deployed using containerization platforms such as Docker, which ensure stable operation across different environments.
- Generative AI is a type of artificial intelligence capable of creating new data, such as text, code, images, or music. It does this by learning patterns from existing data and then using those patterns to generate new but similar content.
- LLM (Large Language Model) is a type of generative artificial intelligence trained on a massive amount of text data. This allows LLMs to communicate and generate human-like text in response to various prompts and questions. Examples: Gemini, ChatGPT, etc.
- Chatbots are computer programs or interfaces that simulate a conversation with users. Chatbots are often used for customer service applications or to provide information on a website.
- An API is a set of instructions and standards that allows programs to communicate with one another. Think of it like a waiter taking your order at a restaurant—the waiter doesn’t prepare the food, but they facilitate communication between you and the kitchen. Similarly, an API facilitates communication between different software components. Chatbots can use APIs to access information or services from other programs.
- Docker is a platform for building, deploying, and running applications. It allows developers to package their code and all its dependencies into standardized units called containers. These containers can be run on any computer with Docker installed, ensuring consistent behavior regardless of the underlying environment. This is useful when deploying LLMs and chatbots, as it makes them easier to share and run across different systems.
What does generative AI do?
When properly implemented, generative AI enables the automation of repetitive tasks, the summarization of complex information, and the dissemination of organizational knowledge through simple interfaces.
“The greatest value is created when employees no longer have to search for information—the system provides it immediately,” emphasizes A. Raudys.
This makes it possible to:
- reduce the amount of manual work;
- accelerate decision-making;
- ensure a consistent customer experience.
Types of Solutions – From Single Functions to Complete Systems
Generative AI solutions are typically divided into two types. The first are narrowly specialized models (TSM) designed for specific tasks, such as customer service, document analysis, or content generation. The second type consists of general-purpose models (GPM), which combine several functions into a single system and act as universal business assistants capable of adapting to the diverse needs of an organization.
“The biggest mistake is trying to create a universal solution right away. Value usually starts with a single, clearly defined function,” shares Aistis Raudys.
In practice, these solutions take the form of very specific tools that solve everyday business problems. For example, FAQ engines allow users to instantly retrieve summarized answers from complex documents, such as financial reports or legal texts. Term sheet generators can automatically generate structured documents from short summaries of client conversations, reducing manual work.
Chatbots are widely used in customer service to provide instant, personalized responses, while internal assistants help employees quickly find the information they need. In e-commerce, value is created by product description generators and shopping assistants that help customers make decisions in real time.
In more complex fields, research assistants capable of selecting reliable scientific sources are used, as well as insurance policy analysis tools that summarize complex documents. In medicine, generative AI is used as virtual assistants that help answer patients’ questions based on structured knowledge.
Another important area is content creation: from writing assistants that turn notes into structured text to Google Ads tools that generate conversion-optimized ads in line with a brand’s tone.
“Generative AI becomes valuable when it doesn’t just create content, but solves a specific problem faster than a human,” emphasizes A. Raudys.
In this way, generative AI transforms from a theoretical technology into a practical tool that actually changes everyday business processes.
Personalised Health Recommendations
The capabilities of general-purpose models are well illustrated by personalised health recommendation generation, where the model must not only understand user data but also provide reliable, evidence-based insights. We built an LLM trained on WHO recommendations and medical research that calculates health indicators and delivers personalised recommendations based on user-inputted data (physical activity, diet, sleep, and environmental factors). Result: health indicator consistency accuracy improved by over 20%.
Automated Sports Journalism Content
One example of a narrowly specialised model is a content generation system built for tennis journalism, an area where generative AI can deliver clear, quickly measurable value. The system automatically generates tennis articles from real-time match data and player statistics, supports multiple content formats, and produces multilingual output via DeepL integration. Content creation time was reduced by 70%, manual effort decreased, and reader engagement metrics improved. The solution is designed to be easily adapted to other sports (badminton, padel, and more).
How does the implementation process work?
The development of the solution begins with the data: an analysis of the organization’s documents, chat history, or internal processes. Based on this information, the model is trained, tested, and integrated into existing systems. The end result is a fully functional solution with API access, documentation, and the ability to expand it further.
“The most important thing isn’t the model itself, but how it integrates into daily operations,” emphasizes A. Raudys.
Long-term benefits for business
Generative AI not only automates processes but also builds long-term organizational expertise. It helps reduce costs, minimizes the likelihood of errors, and enables faster responses to changes. Over time, it evolves from a technological experiment into an essential part of the business infrastructure that creates a competitive advantage.
FAQ
What is generative artificial intelligence?
Generative artificial intelligence is a technology capable of creating content, such as text, images, code, or even analyses, based on available data and models.
How is generative AI applied in business?
It is most commonly used for content creation, customer service (chatbots), email generation, product descriptions, data analysis, and the automation of internal processes.
How does generative AI differ from traditional AI?
Conventional AI analyzes and predicts, while generative AI creates new content based on available information.
Can generative AI boost sales?
Yes. It helps create marketing content faster, personalize offers, and improve the customer experience, all of which directly impact sales.
Is it safe to use generative AI in business?
Yes, but it’s important to ensure data protection, avoid using sensitive information in public tools, and choose reliable solutions.
What are the most common mistakes when using generative AI?
Over-reliance without verification, poor-quality prompts, and a lack of a clear strategy.