Shipping AI Automation Without Infrastructure Overload

AI projects often start with unnecessary technical complexity. A reverse order usually works better.

1. Define one concrete scenario

Not “adopt AI” but something like “reduce first response time for inbound messages”.

2. Lock one measurable metric

A single metric is enough at first: speed, accuracy, or automation coverage.

3. Build a minimal working pipeline

API -> processor -> delivery channel -> logging. That is enough for early iterations.

4. Connect LLM only to repetitive workflows

This gives better control over output quality and cost.

5. Scale architecture after evidence appears

When load grows, you can clearly see where queues, caching, and orchestration are actually needed.