Causal AI: Another New Trend, or a Way to Smarter Agentic Workflows?

Causal AI has quickly become a highly talked about concept in the AI community. Its promise: Equip systems with a deeper understanding of how the world actually works through cause-and-effect reasoning. Advocates claim it’s the next big leap for decision-making, generalization, and safety in AI.
But how much of this is hype and how much is real? And more importantly: what role could Causal AI actually play in improving today’s generative AI and agentic systems?
While large language models and agent-based architectures have shown impressive capabilities in generation, planning, and execution, they still struggle with grounding, consistency, and strategic foresight. Causal AI offers a potential solution but it also brings new complexity, strong assumptions, and practical limitations.
In this session, we’ll critically explore how these two paradigms:“Causal AI“ and „Generative AI“ differ, where they struggle, and how they might work together to enable more intelligent, autonomous, and trustworthy agentic workflows.

💡 You’ll Learn:

• The core differences between Causal AI and Generative AI: capabilities, strengths, and limitations
• Why generative systems struggle with reasoning, and where causal thinking can fill the gap
• How Causal AI can improve planning, grounding, and reliability in Agentic systems
• When generative models outperform causal ones and when they fail
• Strategic considerations for integrating causal reasoning into generative AI workflows

🧠 Who Should Attend?

• C-level executives and VPs exploring advanced AI strategies
• Product Leaders and Innovation Executives shaping AI capabilities
• Data and Analytics Leaders responsible for delivering trustworthy insights
• Teams already using RAG who want to push performance to the next level
• Anyone responsible for critical decision-making powered by AI

The conversation will be high-level and jargon-free, with plenty of time for Q&A.