Shape the future instead of just watching: Why 360VIER is a member of the AI Federal Association

A commitment to co-creation – and what practical experience we bring to the table.

360VIER is a member of the AI Federal Association. For us, this is a statement of position. In our daily work, AI is the material we use to create – and the deeper we delve, the clearer it becomes: Many of the questions AI will raise in the coming years are being decided right now. We want to have a say in precisely these questions, from the workshop, with the perspective of people who develop such systems day in and day out.

Germany Discusses AI from the Sidelines

The German AI debate is thorough. It discusses the EU AI Act, weighs liability and data protection issues, measures productivity gains, and compares models and tools. These are important questions, and it is good that they are being addressed. However, one perspective is often overlooked: the internal view of the organization – and it is precisely there that the success of AI is decided. The concrete questions then are: Is the content machine-readable or only made for the human eye? Is the data modeled or distributed in silos? Are processes documented or do they exist as implicit knowledge in individual minds? Such questions show that AI readiness is primarily a matter of the underlying structure.

Our Perspective at 360VIER

360VIER has been developing digital platforms since 2012, with over 500 projects to date. AI has long been part of our craft. We first implement it in-house and then transfer everything that proves successful to our work with clients.

Three examples from our “AI Enablement” competence area illustrate what this means in practice. “AI Visibility” measures how present a brand is in the responses of ChatGPT, Claude, Gemini, and Perplexity – a visibility that is becoming increasingly important as many people now draw their answers directly from these systems. “DialogHub” works with RAG (Retrieval Augmented Generation): a knowledge layer that, in customer use, backs up every answer with its source, thus ensuring reliability. And our AI-First platforms consider machine readability from the very first structural decision, so that AI systems understand the content cleanly from the outset. In addition, there is the agentic level: We enable companies to use open agent frameworks like “OpenClaw” productively and securely – on their own infrastructure, with clear authorization boundaries.

What Practice Teaches

One lesson runs through all these projects: The model is rarely the decisive factor. The difference is made by context, clean data, clarified rights and responsibilities – and an organization that structures its knowledge in such a way that machines can work with it. This is precisely where the real engineering achievement lies. An agent becomes reliable when the underlying data is correct; a chatbot convinces when a clean knowledge layer supports it. Therefore, anyone who wants to make AI productive starts with the organization – and that is the work that excites us most.

Why This Association

The AI Federal Association brings together what belongs together: practice and politics, technology and regulation, SMEs and standardization. Good standards emerge precisely from this exchange, when people who themselves work on AI systems and know firsthand where aspiration and implementation meet are at the table. We are happy to contribute this perspective.

What We Want to Contribute

What we bring is concrete. We contribute experience from real AI projects, with all the details that only become visible during implementation. We know from practice what truly makes a machine-readable company – from the structure of content to data modeling and process documentation.

We have a well-founded view on “Agentic AI in Organizations.” And we know the Mittelstand: companies with 50 to 500 employees who achieve a lot with smart, lean solutions and need AI that integrates into their existing systems.

Building Together

For us, this membership is a true commitment. It relates to the question that currently occupies us most: How must an organization be structured so that AI works productively, securely, and meaningfully within it? This answer emerges in practice – in systems, projects, and teams, and in the standards that grow from them. That is precisely where we want to get involved, together with others who are working on the same questions. We are building together.