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82 Articles
Digital pioneers in the AI race: Why scalable operationalization is still the key to success
Digital pioneers in the AI race: Why scalable operationalization is still the key to success
AI in practice: Why digital pioneers still have some catching up to do when it comes to scalable AI The integration of artificial intelligence into companies is one of the central challenges of today's economy. A new international study by the Economist on the topic “Making AI deliver: A benchmarking framework on how leading companies operationalize AI for impact” offers exciting insights: In particular, digital […]
05 May 2026 · 4 min read
Plain text on AI scaling: Why traditional companies are ahead of digital natives when it comes to operationalization
Plain text on AI scaling: Why digital natives are ambitious, but traditional companies are ahead when it comes to operationalization Artificial intelligence (AI) and data science are no longer a dream of the future - they now shape numerous business models. Digital pioneering companies in particular, the so-called “digital natives”, are setting ambitious goals for the use of AI. But a current, cross-industry study by the Economist shows: Although […]
05 May 2026 · 3 min read
How digital pioneers scale AI - and why traditional industries are often more successful when it comes to sustainable operationalization
How digital pioneers scale AI - and why traditional industries are often further ahead. As AI transformation accelerates, the question for many companies is no longer whether, but how artificial intelligence can be anchored in their own company in an efficient and scalable manner. A current, cross-industry survey of more than 1,200 international managers shows excitingly: While digital […]
05 May 2026 · 4 min read
Microsoft Fabric 2026: Innovations, self-service and maps – This is how the platform revolutionizes your data strategy
Microsoft Fabric 2026: The future of data analysis and integration for companies Author: Ailio GmbH - your expert for data science, AI and modern data platform As part of FabCon and SQLCon 2026, Microsoft presented the next major evolutionary step for the Fabric platform. These innovations promise far-reaching benefits for companies that value innovative data analysis, flexible [...]
20 March 2026 · 3 min read
Microsoft Fabric Maps: Revolutionary cloud solutions for geospatial analysis, AI and real-time analytics
Microsoft Fabric Maps: New standards for geospatial data analysis in the cloud The possibilities for analyzing and visualizing geospatial data have undergone enormous innovations in recent years. With the general availability of Maps in Microsoft Fabric, Microsoft is taking a significant step toward making it easier for businesses of all sizes to access powerful geospatial analytics - without […]
20 March 2026 · 4 min read
What's new in Databricks in 2026?
Innovations at Azure Databricks: The journey to the intelligent lakehouse for the era of AI agents Databricks is solidifying its position as a leading platform through a series of strategic innovations aimed at even more closely integrating data engineering, analytics and AI. The focus is on the democratization of data recording, a new operational database for AI applications and deep integrations […]
20 March 2026 · 4 min read
Serverless Workspaces in Azure Databricks: Revolutionizing modern data engineering and AI
Serverless Workspaces in Azure Databricks: A milestone for modern data engineering and AI With the widespread availability of serverless workspaces on Azure Databricks, the platform sets a new standard in data processing, analytics and the use of artificial intelligence in companies. As a specialized data science and AI service provider with a focus on Databricks and Microsoft Azure, we analyze […]
13 March 2026 · 3 min read
Data Science Project Planning: Why “tests” determine success or failure
You can have the best idea, the cleanest model and a motivated team. If you plan data science and data engineering projects without a clear testing strategy, you will pay twice later: with unstable deployments, results that are difficult to reproduce and endless discussions about whether a bug is “in the code” or “in the data”. In practice, this is exactly where the playing field separates from [...]
12 March 2026 · 6 min read
Data Science Switching from science to industry: The one skill that determines success
You can build the best model, explain the cleanest statistics, and recite any optimizer in your sleep. And yet you will fail in industry as a data scientist if you don't master one thing: effective communication under real conditions. Because in companies, the most elegant paper rarely wins. The solution that is understood, accepted, implemented and operated […] wins.
12 March 2026 · 6 min read
Building a data platform successfully: Why “use case first” almost always leads to value faster
You want to build a data platform and are faced with what seems like a big fundamental question: first infrastructure, then use cases. Or first use cases, then infrastructure. Many teams reflexively choose “infrastructure first” and months later wonder why the platform exists but no one is actually using it. The hard truth: A data platform is not an end in itself. She is a […]
12 March 2026 · 7 min read
Own GPT platform in the company: This is how “real AI” becomes secure, usable and scalable
Many companies want to use “real AI” and still end up with pilot projects that fizzle out in everyday life. Not because Large Language Models (LLMs) are too complicated, but because the crucial part is missing: a controlled, repeatable way in which people in the company can, can and should use these models sensibly. This is exactly where it will be decided whether AI will become a productivity lever […]
12 March 2026 · 7 min read
Feature Engineering in Data Science: How to build models that really work in the engine room
Your model looks strong in the notebook, but performance drops in real use. Not because your algorithm is “too simple”, but because the features don’t work. This is exactly where it is decided whether data science delivers results in practice or just nice metrics in a test environment. Feature engineering is the engine room: unspectacular but crucial. […]
12 March 2026 · 7 min read
Cancel AI PoC or continue: The quickest way to make a decision is a clean baseline
You can spend weeks on an AI PoC and still not know at the end whether it was “good”. Not because your team works poorly, but because you work without a starting point. Without a baseline, every number seems arbitrary, every optimization seems like activism, every discussion like gut feeling against gut feeling. This is exactly where the lever lies: If you create a baseline early, [...]
12 March 2026 · 10 min read