Before you ship an AI feature to SaaS users, the 5-question checklist I wish I'd had

After working with multiple SaaS teams shipping AI features for the first time, here are the 5 questions that consistently reveal whether a feature is demo-ready vs production-ready. The gap between the two is almost always one of these five things.

1. "Can you run the demo on 20 inputs I select, including our messiest real-world examples?"

Not curated examples. Not the clean inputs that made the demo impressive. Twenty inputs from the actual tail of your user base, the ones with typos, incomplete context, unusual phrasing, edge cases your team didn't anticipate.

If the performance drops significantly, you've learned something important before it costs you a user relationship. If it holds up: good signal that the system is more robust than a typical demo.

2. "What does the user see when the AI returns a low-confidence or incorrect output?"

If the answer is vague or "we'll handle that later," the failure paths haven't been designed. Users will encounter AI failures. The question is whether those failures are handled gracefully or whether they produce confusing, broken experiences.

Every failure mode needs a designed user experience: a shorter response, an "I'm not sure about this one" signal, a fallback to a human, a specific error message. These decisions should be made before launch, not discovered in support tickets.

3. "How do you know if a change you make improves or degrades quality overall?"

If the answer involves manually testing on a small set of examples, the team is iterating without systematic measurement. This works for the first few months. It breaks at scale when you have hundreds of edge cases, and every change is a guess about net quality impact.

A labelled evaluation set of 200+ representative real-world examples, runnable in 10 minutes, answers this question precisely for every change.

4. "What specifically are you measuring as a production quality metric?"

"The AI seems to be working" is not a quality metric. Specific, measurable answers: hallucination rate on a sampled eval set, retrieval precision@k, format validation pass rate, user negative feedback rate. These should be tracked from launch day, not added as an afterthought.

5. "What's your alerting threshold - at what point does AI quality degradation trigger an investigation?"

If there's no defined threshold, degradation will be discovered when users report it, which is always later than when it started. Production AI quality can degrade silently. You need a tripwire that alerts before users notice.

All five of these cost time to implement. Together, they probably add 2–3 weeks to the production timeline. They reliably prevent the "works in demo, breaks for users" pattern that costs much more than that to fix after launch.

What checks does your team run before shipping AI features to production? Please compile a more comprehensive list from people who've shipped multiple AI features.