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.

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.

We cover:

• Predictability. 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.
• Cost. 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.
• Trust. Enterprises buy confidence, not capability. We walk through the four questions that decide whether something ships: explainability, reproducibility, auditability and accountability.

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.

Capability gets you the meeting. Confidence gets you the deployment.

Where have you drawn the line between reasoning and execution in your systems? I'd like to hear.

#EnterpriseAI #AgenticAI #AIinProduction #AIGovernance