AI agents in a reality check: From hype to real added value
Webinar on demand
Data & Cloud Services
How to realistically classify AI agents and use them effectively
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 focus of the webinar:
- Clear classification: 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.
- Specificuse cases: Gain insights into practical scenarios that are already in productive use today and deliver measurable benefits.
- Action plan for executives: A structured approach to introducing agents into existing data and process landscapes—controlled, scalable , and low-risk.
- Clear requirements: Understand what organizational, technical, and procedural foundations must be in place 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 contributors
Julian Schütt has been advising our customers for over 15 years, from the conception to the implementation of smart data architectures. As head of the Data & Cloud Services business unit, he is involved in the use of innovative technologies, from agile cloud environments to the efficient use of artificial intelligence.
Florian helped build up the smart data division at celver AG from the very beginning, focusing in particular on cloud, data science, and AI. As team leader for data science, he is responsible for projects related to the development of modern, cloud-based data platforms and the development of machine learning and AI solutions. His focus is on implementing scalable architectures and data-driven applications that create measurable added value for customers.
