What is generative AI?

Updated: January 2026

How generative AI helps your company? Learn about its real applications in marketing, HR, and development

Generative artificial intelligence is currently a tool used by companies, professionals, and organizations in almost any industry. In just a few years, it has gone from being just a theoretical concept to a help mechanism in marketing, development, human resources, customer service, and content creation, but it is not always clear what it is, how it works, or why it has so much impact today.

Today at Xamai we will explain it to you and we will also learn about its real applications, benefits, risks, and what elements companies should consider to use it correctly.

Generative artificial intelligence what is it?

It is a branch of artificial intelligence that focuses on generating new content once it has acquired learning from a large data source; it differs from other AI systems that only analyze or classify information because generative AI has the ability to create.

When we talk about creating, we mean text, images, music, code, natural language responses, and other formats, but always based on patterns learned during its training.

To summarize the definition, we can say that it is a technology that learns by observing data and then uses it to produce new, useful results, always with coherence according to the context and the requested task.

And how does generative AI work?

You will be able to dimension its true potential when you truly understand how generative AI works: we start from the point that these systems are built from machine learning, automated learning, and advanced models capable of processing enormous volumes of information.

During training, models analyze data and learn patterns, relationships, and structures, over and over again, and over time, that ability allows them to generate content with total coherence and adapted to the required needs.

Generative AI models and foundational models

The generative AI models that exist today are based on models that have been trained with massive amounts of data. This gives them a great ability to adapt and also, they can perform multiple tasks without having to be trained from scratch each time.
These models function as a base upon which applications are built for different sectors, services, and business processes.

Machine learning, transformers, and neural networks

We cannot talk about generative AI without mentioning machine learning, transformers, and neural networks because their advancement is mainly due to these 3 elements.

In fact, models based on transformers have been very important for natural language processing and text generation with greater coherence and context. Approaches such as autoencoders are also used, which allow complex information to be represented and the quality of the generated content to be improved.

Evolution and history of generative artificial intelligence

Generative AI didn't appear out of nowhere. Its early versions were limited and depended on simple rules, but over time, the growth in computing power, data collection, and advances in computer science drove increasingly sophisticated and powerful models, until transformers and foundation models elevated system performance and expanded their applications.

Today we can say that generative artificial intelligence continues to evolve at a rapid pace and is receiving constant improvements in efficiency, context, and reasoning ability.
Applications and use cases of generative AI

Applications of generative AI are already part of the daily routine of many companies that have solutions already in use and provide undeniable benefits.

1.- Generative AI in companies and organizations

Companies use generative AI to automate tasks, improve the time between each process and thereby reduce times, making work more effective. Generative AI can perfectly help in generating reports or performing analysis of unstructured data; it is a fast and precise technology as long as it has the necessary data. The overall efficiency of the company increases significantly when using this tool.

2.- Generative AI in marketing, content, and customer service

In marketing, generative AI is used to generate content, create articles, personalize messages, and produce images adapted to different audiences, which considerably improves interactions, customer service, allows for conversational assistants that understand context and natural language, and thus maintains more agile and consistent communication.

3.- Generative AI in development, operations, and human resources

In technical areas, generative AI helps developers with code generation, documentation, and testing. It is also very efficient in areas such as human resources, which is used for training, profile analysis, and support for internal processes.
You will see that productivity increases for workers and improves the use of available resources.

Prompt creation and the best way to ask generative AI

To correctly use generative AI, you also need to know how to ask and interact with it. Orders or prompts are all the instructions we give to systems to generate content, and this must be clear, specific, and contextualized.

The more relevant information is provided, the better the results will be. It is very important to know how to formulate prompts, to the point that it has become a skill for professionals, marketing teams, and development areas.

 

Advantages and benefits of generative AI

Without a doubt, one of the greatest benefits of generative artificial intelligence is its direct impact on efficiency by reducing times and scaling operations without proportionally increasing costs.

Furthermore, it improves the quality of decisions by allowing for greater innovation using real data and patterns identified by the models. This is synonymous with modernization and having a better response to market needs.

But just as it has its positive side, there is also a limiting side that could have effects.
Challenges, risks, and limitations of generative AI

Despite its advantages, generative AI also presents risks and limitations. These include data privacy, information security, and the potential for generating disinformation. A lot of sensitive information is handled in this database, so it is important to maintain security. In addition, if models are trained on biased data, they can reproduce errors or incorrect representations.

Let's remember that AI doesn't always understand context as people do, so human oversight remains necessary.

Recommended practices and ethical considerations

For responsible use of generative artificial intelligence, it is essential to establish good practices such as protecting intellectual property, protecting user privacy, and ensuring transparency in processes.

Companies should define clear policies, train their teams, and ensure that AI is used as a support tool, not as a replacement for human judgment.

Generative AI as a competitive advantage in digital transformation

 

Generative artificial intelligence has become a key driver of digital transformation. Its ability to generate content, optimize processes, and improve productivity positions it as a real competitive advantage.

At Xamai, generative AI is understood as a strategic tool integrated into our SAP products intelligently in order to improve business processes. Beyond the technology, the focus is always on generating value, improving decision-making, and supporting organizations on their journey towards sustainable and results-oriented digitalization.

Generative AI is already here. The difference is how it is used.