Deploying Local AI For Defined SOPs

# Deploying Local AI For Defined SOPs

In February 2026 I gave a workshop at Chiang Mai University on deploying local AI for defined SOPs. The useful point was not that local models should replace frontier systems. It was that local models need a narrower job description.

Frontier systems are strongest when the work is vague and the search space is wide. Local systems become interesting when the work is bounded, private, repeated, and checkable. The operating question is not which model is best in the abstract. The operating question is which step in the workflow should belong to a model at all.

If a step is deterministic, script it. If a step has fuzzy human input but a bounded output, a local model can help. SOPs are what make that boundary visible.

Bias is part of the design. A local model should be pushed toward the task through post-training, adapters, and harnesses. The point is not to build a tiny universal mind. The point is to make a useful specialist that can act safely inside a controlled system.