TL;DR
Choose local or cloud AI by data, control, operating capacity, and consequence—not fashion.
What the paper develops
Local and cloud AI are often framed as a technology preference, but the meaningful differences are operational: data boundaries, required control, consequence of failure, support capacity, update model, and recovery. This paper gives leaders a governance frame for comparing those tradeoffs before architecture becomes a sunk decision, including cases where a hybrid or deliberately limited solution is the better answer.
The operating move
Choose local or cloud deployment from the data boundary, consequence of failure, required controls, support capacity, and recovery model. Architecture follows the operating decision.
WORKFLOWCONTROL EVIDENCEHUMAN OWNER
Inside the white paper
- Data, control, consequence, performance, and support tradeoffs
- Operating economics, lifecycle capacity, and recovery obligations
- A decision path for local, cloud, hybrid, or bounded non-adoption