Written for AI and engineering leaders already past the investment decision, this guide addresses the persistent and costly gap between AI pilots that perform well in controlled environments and systems that actually work reliably in production.
This guide is written for AI leaders who are past the question of whether to invest in AI. You have already answered that.
The question you are now trying to answer is harder: why is the gap between what AI can do in a controlled environment and what it actually does in production so wide -- and what needs to change architecturally to close it. We will name the specific technical failure modes, the architectural patterns that address them, and the decisions that determine whether an AI programme compounds in value or plateaus at pilot stage.
This is written from direct production experience -- not from analyst frameworks or vendor positioning.
