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Labeling AI content in accordance with the EU AI Act starting in 2026

New transparency rules under Article 50 of the EU AI Act have been in effect since August 2, 2026. But that doesn’t mean you’ll have to label every text, image, video, or social media post with a big  “Made with AI” label from now on. The rules are much more specific. They primarily apply to chatbots, deepfakes, and certain texts on topics of public interest. And when it comes to texts, human editorial oversight plays a particularly important role.

Better prompts in 10 steps [cheat sheet]

Want to work smarter with AI? Discover how to write the perfect prompt using our 10-step plan and get AI to do exactly what you want!

AI policy and literacy in organizations: what to regulate next?

AI is already in your organization. Even if you think it isn't. Since 2024, there has been the AI Act. European legislation that determines how organizations must deal with AI responsibly. This does not require thick policy folders, but clear choices. Because whoever uses AI must be able to explain what is happening, what risks are involved and who is responsible for what.

Which AI should you choose?

Everyone knows ChatGPT. But it’s no longer the only serious AI tool out there. Claude, Gemini, and Mistral are popping up more and more often in conversations, demos, and software choices. But for many people, one question remains: what’s really the difference? We’ll show you that it’s no longer just about which AI is “the smartest.” The real question is simpler and more useful: which AI is the best fit for what you want to do?

AI has a shorter attention span than you think (and what you can do about it)

You’re having a great time using ChatGPT, Claude, Gemini, or Sterc.ONE, everything’s running like a charm, and then something strange happens. The answers start to get vaguer. The model forgets what you agreed on a few messages ago. Sometimes it even makes up things that never happened. You think: has this thing suddenly gotten dumber? No. It’s just that the workspace is full. In this article, I’ll explain what’s happening behind the scenes, why every AI has this problem, and most importantly: what you can do about it yourself.

From standalone systems to an agentic AI platform

Many organizations still rely on a complex array of systems: ERP for orders and inventory, PIM for product data, CRM for customer relationships, and on top of that, various integrations with suppliers, carriers, and marketplaces. But standalone systems and databases alone are no longer enough. The next step is to make AI a structural part of your organization, not as a standalone chatbot or experiment, but as a fundamental layer underlying your processes.

This is why AI takes off for some and fails for others

Every organization wants to do something with AI: chatbots, copilots, agents, content, analytics, automation. The promise is great, but the reality is challenging. Many AI initiatives deliver less than hoped for. Sometimes, not at all. That’s why we don’t view AI as a tool, but as a system. And that system always follows the same formula: Prompt × Data × Model = AI Result. Together, these three form a triangle. They are inextricably linked. If one component is off, the entire structure collapses.

Think before you use AI

AI has quickly evolved from a tool to the norm, in education, but just as much in organizations. But how do you ensure that you continue to think for yourself? Our vision is clear: embrace AI, but do so responsibly. Not as a shortcut, but as a thinking partner. It is precisely when validating information, choosing the right model, and formulating the right task that humans remain crucial. Those who use AI wisely become faster and better. Those who use AI blindly become dependent and vulnerable.

In-depth research with AI: choose the right tool for your purpose

In-depth research using AI has come of age. However, many teams still use it as if it were just “smarter Googling,” and it isn’t. It’s like a researcher who creates a plan, consults sources iteratively, combines internal and external information, and turns it into a verifiable report. But every tool does that just a little differently. So, which one do you choose and when?

AI drivers: how to build AI that actually works

AI isn’t just a little tool you can grab and use on the fly. Nor is it a model you simply turn on and call it a day. AI only works when everything around it is in place: technology, data, people, and agreements. That’s why we work with 7 AI drivers. Think of them as the building blocks of any serious AI approach.

Mistral AI: Europe's AI leader in privacy and control

From nothing to a value of €11.7 billion in two years. Backed by investments from companies including NVIDIA and ASML. Built by former Google and Meta researchers who returned to Paris to show that things can be done differently. Mistral AI isn’t just another European counterpart to American AI giants, it’s a fundamentally different story. This is your chance to use AI on your own terms.

ChatGPT Prompt Packs: AI hacks that make your work smarter

AI is everywhere, but how do you use it in such a way that it really gets you time and results? OpenAI comes up with a smart solution: Prompt Packs. Bundles of ready-to-use commands (prompts) that help you deploy ChatGPT smartly, without having to take a Prompt Engineering course first. From marketing to HR, finance to development: there's a pack to fit virtually every department.