IT Knowledge in a Nutshell
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While the EU aims to ensure safety and transparency for its citizens through the AI Act, companies are increasingly uncertain about the legal obligations the AI Act imposes on them. One thing is certain: AI has long since become part of everyday business life. We’ll explain how you can use AI in your company in a legally compliant manner, in accordance with the EU AI Act!
An employee asks the internal AI assistant about the current vacation policy. The answer sounds convincing—but it’s still wrong. The language model isn’t familiar with the actual company policy; instead, it generates a plausible-sounding response based on its training data. You may be familiar with this exact problem from your own experience.
AI governance establishes a binding framework for the secure, transparent, and legally compliant use of artificial intelligence. It integrates corporate governance, risk management, data protection, IT security, and technical development. As a result, governance becomes a central component of any viable AI strategy.
Companies are constantly storing vast amounts of data. Sensors, ERP systems, CRM solutions, cloud applications, and IoT devices deliver new information every second. Real value is only created when this data is accurately consolidated, processed, and made available for later analysis.
The open standard makes it possible to connect AI applications to enterprise systems, data sources, APIs, and external tools in a standardized way. This gives language models access to up-to-date information and enables them not only to retrieve data but also to perform actions in various applications.
Just a few years ago, the path to information almost always went through traditional search engines like Google. Users would enter a search term, click on various websites, and compare the information provided there on their own. This search behavior is currently undergoing a fundamental change...
In this article, you will find out what AI means in the office, what advantages it offers and in which areas it can already be used today. We also show you what you should look out for when using AI and how you can get started working with AI-supported tools step by step.
Do you want to know whether Scrum or Kanban is the right choice for you? Both agile methods are among the best-known project management methods - and both can help you to deal with complex requirements more clearly.
Applications such as ChatGPT, Gemini or other AI-supported systems deliver texts, evaluations, concepts or code within a few seconds. But in practice, it quickly becomes clear that the quality of the results is no coincidence. It largely depends on how precise and structured you formulate your query.
Artificial intelligence has long since arrived in everyday business life. Whether automated processes, intelligent analyses or generative AI - the potential applications are growing rapidly. At the same time, however, the requirements for data protection, transparency and legal security are also increasing. This is precisely where the topic of AI compliance comes into play.
Artificial intelligence is changing the way companies work. AI systems support the analysis of large amounts of data, automate recurring tasks and enable new digital business models. However, the use of AI not only brings efficiency gains, but also new security-related challenges.
Have you ever wondered why smartphones now recognize faces, why cars can drive themselves or why chatbots seem more and more natural? Behind many of these small technical wonders is deep learning - a kind of "brain" for modern AI systems.
Perhaps you know the feeling: you open Netflix and immediately find a series that suits your taste perfectly. Or your smartphone sorts your photos so cleverly that you can find certain moments with just one tap. You might be thinking: "That's pretty handy." And that's exactly what machine learning is all about.
Have you ever wondered how large companies manage to bring together mountains of data from different sources, cleanse it and then use it to make important decisions? It's not rocket science, but a meticulous method that forms the backbone of any modern data analysis.
The demands placed on modern data landscapes are increasing rapidly. More and more systems, higher data volumes, stricter compliance requirements and the pressure to use data intelligently are presenting companies with new challenges.
The term Continuous Integration - CI for short - is not really a big secret: you regularly upload your code to the shared repository and a pipeline automatically checks whether everything fits together.
If you develop software, want to introduce it or are responsible for it in your company, you probably know this feeling: The new application is supposed to simplify processes, save time and make your life easier - but what if errors in the system have exactly the opposite effect?
Imagine you start your computer in the morning, open your software - and everything runs smoothly. No error messages, no unexpected failures, no "We have to update the system first" blockers. Sounds good, doesn't it? Unfortunately, everyday life often looks different