My Chatbot and I Have Trust Issues: Building Reliable AI

We are building AI systems everywhere—from customer service to clinical decision support—while not even fully trusting the chatbots. And we are wise not to trust them. The truth is that when we talk about "human-in-the-loop", we are giving our AI an F, and we are losing out on most of the automation and scalability. So if we can't trust a simple chatbot, how can we design enterprise-wide autonomous AI?

This talk dives into the root cause of those trust issues: the gap between probabilistic-based prediction and fact-based understanding, and why modern AI needs more than just bigger models doing the RAG time shake to become reliable.

I will dig into the concept of World Models: internal representations of reality. Every intelligent system—whether a worm, a human, or an AI—must build a small world inside itself. This internal model is what allows it to predict rather than just react. It’s how the brain, the worm, and the algorithm navigate and find their way in a messy world.

Through examples from various industries (including modelling global supply chains with millions of graph relationships), we’ll explore how grounding AI in structured knowledge dramatically improves its ability to reason, stay consistent, and avoid hallucination.

Attendees will learn how knowledge graphs, ontologies, and hybrid neuro-symbolic architectures create AI systems that can explain their answers, keep track of facts, and align with organisational logic. Finally, we’ll look at practical patterns for bringing reliability into real-world AI setups—from digital twins and decision support to enterprise agents that can be trusted with actual work.

If your chatbot also has “trust issues,” this talk will help you understand why and what it takes to fix the relationship.