Build AI for an Ordinary Tuesday

An AI demo can summarize a document, draft an email, or answer a question from the wiki and impress everyone in the room. Getting it into daily use takes a different kind of work.

The demo usually runs on a clean input with someone steering. A production system has to handle the inputs people send it, including the ones nobody thought to put in the presentation.

What the demo skips

• Failure handling. What happens when a document is malformed, an API is down, the model returns unusable output, or a request is out of scope?
• Evaluation. Keep a fixed set of real cases so you can measure changes and catch regressions before a release.
• Permissions and review. Decide what the system can do on its own and where it must stop for a person.
• Cost and response time. Check both at the volume you expect people to use. A feature that works for one request may struggle with many at once.

It’s easy to leave these out of an early demo. They still need answers before people depend on the system.

Build for the ordinary day

I want the system to survive an ordinary Tuesday: the inputs are messy, the person who built it is busy, and someone needs to get their work done.

That means making room for types, tests, evaluations, and logs from the start of the production build. It also means deciding who gets told when something fails and what they can do about it. A prototype can help you learn quickly, but hardening it needs its own time in the plan.

Before handing it over, I want to be able to answer a practical question: how will someone know whether it’s working without asking the person who built it? They should have a way to see completed work, inspect failures, and take over when the system needs help.