What happens when enterprises spend trillions of dollars on AI but the systems underneath it still cannot provide the context those models need to make reliable decisions? In this episode of Tech Talks Daily, I reconnect with Christian Monberg, CTO at Zeta Global, to examine what separates AI experimentation from production systems that organizations can actually trust. Our previous conversation focused on how businesses could use AI to scale marketing without losing the human connection with customers. This time, we move deeper into the technology underneath those experiences. Christian explains why disconnected tools and fragmented data remain barriers to AI adoption, and why Zeta rebuilt its data architecture using Palantir Foundry. We discuss the role of context graphs in connecting customer identity, business objectives, previous decisions, campaign history and outcomes so AI systems can understand more than isolated pieces of information. We also examine one of the biggest questions surrounding agentic AI: when should businesses allow an AI agent to take action? Christian shares what enterprises need around explainability, permissions, observability and learning loops before AI systems can safely move from recommendation to execution. With global AI spending expected to reach $2.59 trillion in 2026, the conversation ultimately comes back to a simple question: how can technology leaders prove that their AI investments are producing measurable business value?

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