Shipping AI features without the hype

The failure mode we see most often is an AI feature scoped as a demo, not as operations. No owner for prompts, no evaluation set, no plan for wrong answers, and no integration back to the system of record.

Useful AI projects start with a bounded job: reduce support triage time, draft responses from approved docs, extract fields from uploads, or route tasks—not 'add ChatGPT to the product.'

Retrieval-augmented generation (RAG) works when your knowledge base is curated and access-controlled. It fails when teams point a model at a folder of stale PDFs and expect production accuracy.

Human review remains part of many workflows—especially where money, compliance, or customer trust is involved. Architecture should make review easy, not bolt it on after launch.

We treat AI as a product capability with metrics: latency, cost per task, escalation rate, and user acceptance. If those are not tracked, the feature is still a prototype.