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We constantly talk about it but what is Agentic AI actually? 👀 The terms Generative AI, AI agents and Agentic AI are often used interchangeably. One useful and simplified distinction is:   Generative AI creates content. It responds to a prompt with text, images, code or other outputs.   An AI agent goes one step further and performs a defined task. It can access information, use tools and take several steps towards a specific goal.   Agentic AI refers to systems of multiple AI agents collaborating to achieve complex goals. Specialised agents can divide a broader goal into subtasks, exchange information and coordinate their actions across a workflow.   Take a customer service case: one agent classifies the request, another retrieves relevant customer information and another checks which resolution is permitted. A coordinating layer connects the steps and determines whether the case can continue or should be escalated. Humans still define the goals, permissions, tools and boundaries.   There is no single universally accepted definition of Agentic AI yet. But this simplified distinction helps explain what separates it from a chatbot or a single task-specific agent. In simple words:   Generative AI creates. AI agents act. Agentic AI orchestrates.

Recently, we shared five signs your data isn’t AI-ready yet. Now, let’s turn the question around: What does it take to be ready for a first, focused AI use case? 👇   ✅ It starts with a real pain point and a clear benefit for a customer, team or role. ✅ It requires metrics that show whether the solution creates real value. ✅ And it needs a trusted data source, sufficient data quality and an AI approach that fits the work, shaped with the people who know the process best.   That doesn’t mean you’re ready for every possible AI initiative. It means you’re ready to start with one use case that matters. 🤝   Swipe through the three questions.   Which one would be hardest for your organization to answer today?   #AIReadiness #EnterpriseAI #DataStrategy Michael Baumann

The conversation around Agentic AI often starts with investment. 💰   More budget. More tools. More pilots.   But that is rarely where the real bottleneck is. Cloudflight's agentic AI Gap study shows that only 14% of companies named are missing budget as their main blocker. More often, the challenge sits much closer to the business:   Who takes ownership? Where does AI create real value? Which outcomes are trusted enough to become part of actual processes?   These questions are becoming increasingly important for European companies.Because productivity will not improve by simply adding more parallel work. It improves when knowledge, processes and decisions are better connected. ⚙️   Agentic AI can be a powerful lever here but only when companies intentionally build the foundation for it. More on the Cloudflight AI Starter Workshop in the comments. 👇

AI rarely fails because of the model. It fails because of the data underneath. Before starting an AI project, it is worth taking an honest look at your data foundation. We see these five signs again and again.... and each one can slow AI down before it even gets started. You will find the five warning signs in the carousel. Save it for your next data project. 👇 Which of the five feels most familiar to you? 👀 Blend Mexhuani

The data scientist isn't disappearing. The role is growing up. 🙌 The data scientist's craft hasn't changed as much as the hype suggests. Choosing the right approach and framing the problem correctly still takes the same expertise as ever. What's changed is the starting point: with powerful pre-trained models available off the shelf, much of the model-building work falls away, so you reach the result far faster. Skills like MLOps and data engineering now matter as much as the modeling itself, paving the path to a new role: the AI Engineer, who turns models into production-ready systems.   For companies, that's a double win: more depth and more speed. 💯   But technology alone doesn't deliver it. Too many AI pilots never reach production, rarely for technical reasons, but because of missing strategy, weak data infrastructure, and insufficient governance.   That's where the real value lies. Not in building models, but in building the foundations that make AI work: clean data pipelines, sharp questions, measurable success criteria, and ethically sound systems.   Those who build the bridge between data expertise, business strategy, and AI architecture are more valuable than ever. 💡 Which shift are you seeing most – the move toward production, or toward strategy? 👇

A lot of Agentic AI conversations start with tools. ⚙️   Which platform? Which model? Which pilot? All valid questions. But they are not always the best place to start. 😶🌫️   The more important question is whether your AI has the right context to work with.   Because Agentic AI does not create value in isolation. It needs access to the right data, clear processes, shared business language and workflows it can actually support. 🤝 That is where strategy makes the difference. Not as a long document that sits in a folder, but as a clear direction for where AI should create value, how it fits into the organization and what needs to be in place for it to scale.   With that foundation, Agentic AI becomes much easier to move from concept to real business impact. And once the direction is clear, the technology can do what it is meant to do: support better decisions, better processes and better ways of working. Want to identify where Agentic AI can create real value in your organization? 👉 Let’s start with the strategy behind it.

👇 3 things companies won’t regret in 12 months: ✅ Connecting their knowledge. ✅ Structuring their context. ✅ Giving AI agents the information they need to support real processes.   Because Agentic AI becomes much more powerful when agents understand the environment they operate in. 👀 Curious what else companies won’t regret 12 months from now? Let’s talk.

GenAI macht KI greifbarer. Aber ersetzt es Data Science? 😮 Die kurze Antwort: nein.   Denn produktive KI braucht mehr als gute Prompts oder starke Modelle.   Unser Ask an Expert #4 wurde zu einem ausführlicheren Bloginterview. Mit Dr. Timo Klerx sprechen wir darüber, was Data Science im Zeitalter von GenAI bedeutet und wie Unternehmen KI sinnvoll in die Anwendung bringen.   👉 Den Link zum vollständigen Interview findet ihr im ersten Kommentar.

Data Point Prague recap 🙌 Smart questions, great conversations and a really well-organized event from start to finish. 🚀   Gerhard Brueckl and Blend Mexhuani were on stage talking about modern data platforms, architecture choices and the eternal question: Fabric, Databricks or both? The short answer: it depends. The better answer: reach out to Gerhard or Blend and discuss your specific situation. 💯   Thanks to everyone who joined the session and to the Data Point Prague team for creating such a welcoming atmosphere. ✨   #DataPointPrague 

GenAI hat die Berührungsangst vor KI deutlich reduziert. Das ist ein großer Fortschritt. 💡   Viele Menschen erleben zum ersten Mal selbst, was KI leisten kann: Texte strukturieren, Code generieren, Ideen entwickeln, Inhalte zusammenfassen. Dadurch wird KI greifbarer und rückt näher an den Arbeitsalltag.   In der Praxis zeigt sich aber auch: Der eigentliche Mehrwert entsteht nicht durch ein Tool allein. 👀 Besonders spannend wird es dort, wo KI-Anwendungen nicht nur unterstützen, sondern konkrete Entscheidungen verbessern sollen: Welche Daten sind dafür relevant? Sind sie verlässlich genug? Welches fachliche Wissen braucht es, um Muster richtig einzuordnen? Und wie kommen die Ergebnisse am Ende in bestehende Prozesse?   Erst wenn Daten, Domänenwissen und Prozesse zusammenkommen, wird aus einem Modell eine Lösung, die im Alltag wirklich unterstützt. 🤝