AI Agents Put to the Test: From Hype to Real Value
On-Demand Webinar
Data & Cloud Services
How to Realistically Categorize and Effectively Use AI Agents
AI agents are currently considered the next big efficiency lever—but there is often a huge gap between vision, demo, and productive use. Many companies are faced with the question of whether agents really deliver genuine added value or simply create new complexity.
In our on-demand webinar, our AI experts therefore provide a pragmatic framework that clearly defines AI agents, distinguishes them from existing automation approaches, and provides concrete decision-making aids for corporate use.
The webinar will focus on:
- A Clear Overview: Understand what AI agents really are in a business context—and how they differ from chatbots, workflows, and traditional automation.
- Informed Decisions: Learn to evaluate agents based on realistic business value rather than technical feasibility.
- Specific Use Cases: Gain insights into real-world scenarios that are already in productive use today and deliver measurable benefits.
- Roadmap for Executives: A Structured Approach to Introducing Agents into Existing Data and Process Landscapes—Controlled, Scalable, and Low-Risk.
- Clear Prerequisites: Understand the organizational, technical, and procedural requirements that must be met for agents to be successful.
Take this opportunity to find out under what conditions AI agents can deliver real added value for you—or whether traditional automation is (still) the better choice. Sound exciting? Then watch the webinar now:
Webinar Participants
Julian Schütt has been advising our clients for over 15 years on everything from the design to the implementation of smart data architectures. As head of the Data & Cloud Services business unit, he focuses on the use of innovative technologies, ranging from agile cloud environments to the efficient use of artificial intelligence.
Florian helped build celver AG’s Smart Data division from the ground up, driving progress in the areas of cloud, data science, and AI in particular. As the Data Science Team Leader, he is responsible for projects related to building modern, cloud-based data platforms and developing machine learning and AI solutions. His focus is on implementing scalable architectures and data-driven applications that create measurable value for customers.
