From Demo to Production: Where Should AI Reasoning Happen?

<p>Gartner expects about 40% of agentic AI projects to be canceled by 2027. Only around 17% of organizations have actually deployed agents, even though more than 60% plan to.</p><p>This episode covers the first three parts of my series, Enterprise AI in Production, and looks at why a great agentic demo so often stalls before it reaches production.</p><p>We cover:</p><ul><li><strong>Predictability.</strong> Can we audit it, reproduce it, and put an SLA on it? These are enterprise software questions, not AI questions, and they decide what goes live.</li><li><strong>Cost.</strong> An agent that keeps reasoning keeps spending tokens. Why cost per decision matters, and why paying to re-solve problems you already solved is the hidden waste.</li><li><strong>Trust.</strong> Enterprises buy confidence, not capability. We walk through the four questions that decide whether something ships: explainability, reproducibility, auditability and accountability.</li></ul><p>The pattern that connects all three: AI should discover, and deterministic systems should execute. Once AI reliably solves a class of problems, turn that into a rule or workflow. It then runs cheaply, the same way every time, and a regulator can inspect it.</p><p>Capability gets you the meeting. Confidence gets you the deployment.</p><p>Where have you drawn the line between reasoning and execution in your systems? I&#39;d like to hear.</p><p>#EnterpriseAI #AgenticAI #AIinProduction #AIGovernance</p>