How to plan an AI automation project that actually ships

Start from the workflow, not the model

The most common way AI projects stall is starting from "we should use AI" instead of a specific, painful workflow. Pick one process with a clear before/after — a support queue, a document review step, a repetitive data-entry task — and design the automation around it.

Ground it in your real data

An agent or assistant is only as useful as the data and tools it can reach. Plan for retrieval and tool access to your actual systems early, not as an afterthought — this is usually the difference between a demo and a production tool.

Decide what 'good enough' means before you build

Define accuracy and safety expectations up front: what happens when the AI is unsure, what a human review step looks like, and how you'll monitor quality after launch. Guardrails aren't a final step — they're part of the design.

Ship something narrow first

A narrow automation that reliably handles one real workflow builds trust — and a foundation — for expanding scope, faster than a broad assistant that tries to do everything from day one.