AI agents show superhuman skill yet still fail in simple ways — a paradox known as the “Swiss cheese problem.” The solution lies in neuro-symbolic integration: combining neural networks’ creativity with the rigour of symbolic logic. Knowledge Graphs provide the missing backbone enterprises need for reliable, trustworthy AI.

95% of enterprise GenAI pilots fail—but it’s not the models’ fault. The winners connect data, enforce clear semantics, and wrap LLMs in formal ontologies for trustworthy, validated AI.

Large language models excel at transduction — drawing analogies across cases — and hint at induction, learning patterns from data. But true reasoning demands abduction: generating structured explanations. By pairing LLMs with ontologies and symbolic logic, organisations can move beyond fuzzy resemblance toward grounded, conceptual intelligence.