Most AI automation projects do

Most AI automation projects do not fail because the model is weak. They fail because the operating system around the model is missing.

Before I let an agent touch a live workflow, I define four things:

01 / The source of truth. 02 / The decisions the model may make. 03 / The actions a deterministic gate must approve. 04 / The evidence that proves the result.

That pattern now runs across three public builds:

BUILD 01 / Organizational AI Memory with source-backed retrieval. BUILD 02 / Monday + Zoom + RTMP meeting automation with transcription and AI scoring. BUILD 03 / Browser-first Telegram operations with Postgres truth, Qdrant retrieval, and deterministic gates.

I have packaged the entry point as a focused AI Workflow Audit: one process, one success metric, a risk map, and a 30-day implementation roadmap.

What is the first workflow in your business that is still held together by copying, checking, and chasing?