A comprehensive analysis of the technical, organisational, and human infrastructure required to deploy AI effectively in enterprise environments.
Everyone talks about AI strategy. Few talk about AI infrastructure. Not the servers and GPUs — the organisational infrastructure. The data pipelines, the governance frameworks, the feedback loops, the human oversight systems.
Building AI that works in a lab is engineering. Building AI that works in an organisation is architecture. The difference is everything that surrounds the model: the data it consumes, the humans it serves, the systems it integrates with, and the governance that ensures it behaves.
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The End of Prompt Engineering
As models reason better, the need for complex prompt chains diminishes.
Human-in-the-Loop, Properly Defined
Human-in-the-loop was never about having a person nearby. It was about designing exactly where judgment enters a system — and most companies that claim to have it have only the nearby part.
Complementarity-Aware Collaboration
Most AI training teaches people to use the tool. Almost none teaches the harder, rarer skill: knowing, in real time, whether your judgment or the system's is more likely to be right.