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Most organizations are rich in data but poor in meaning. Knowledge lives in people’s heads, scattered documents, and disconnected systems — and every new AI initiative hits the same wall: missing context, answers no one can verify, and results that can’t be audited or explained. Garret makes the case for Neurosymbolic AI as the practical path forward: a deliberate pairing of machine learning and generative AI with the rigor of explicit knowledge and rules. The organizations getting real value from AI today aren’t choosing between traditional ML, LLMs, and symbolic methods — they’re combining them. ML and generative models do what they do best: finding patterns, structuring messy inputs, forecasting, and powering natural language interaction. A semantic layer captures what the business actually knows, and decision logic guarantees the steps that have to be right every time. Leave with a clear understanding of what Neurosymbolic AI is and why it matters, a framework for thinking about where ML and GenAI belong in a solution and where deterministic logic belongs, and a practical starting point for building hybrid AI systems in their own organization that are accurate, explainable, and worthy of trust.
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