World Model Readiness Whether AI features ship honest

Module · Whether AI features ship honest

The Product Team AI Check

Shipping an AI feature is easy; shipping one that keeps working is the hard part. A model that demoed beautifully can degrade in the wild, delight the wrong users, or quietly optimise for a number that does not matter. This module checks the five disciplines that separate a durable AI feature from a lucky demo: evals before launch, real feedback loops, success metrics beyond engagement, degradation monitoring, and a roadmap that tells the truth about what the model cannot do.

Question 1 of 5 · Evals before launch

Does your team run an eval suite before an AI feature ships?

A model that looks great in a handful of hand-picked demos can fail on the long tail nobody tried. An eval suite is a repeatable test set with a known answer key, so you measure quality before users do. Vibes are not a launch gate.

Question 2 of 5 · Feedback loops close

Does real user feedback actually make it back into the model or prompts?

A thumbs-down button that nobody reads is theatre. A closed loop means the signal from users, corrections, complaints, abandoned sessions, reaches the people who can change the prompt, the retrieval, or the model, and does. Otherwise the same failure ships forever.

Question 3 of 5 · Metrics beyond engagement

Do you measure whether the AI feature actually helps, not just whether people use it?

Engagement is easy to grow and easy to fake: a confusing feature drives clicks too. The metrics that matter are whether users got the outcome they came for, task completion, resolution, time saved, trust. Optimising engagement alone can make a feature more addictive and less useful at once.

Question 4 of 5 · Degradation is watched

Would your team notice if the AI feature quietly got worse in production?

Models drift: inputs shift, a provider updates a version, retrieval goes stale, and quality slides without a single error in the logs. Without monitoring on output quality, the first person to notice degradation is an angry user, months in.

Question 5 of 5 · Roadmap is honest

Is your roadmap honest about what the AI cannot reliably do yet?

Pressure to promise AI magic is intense, and a roadmap that oversells sets the team up to ship something that cannot deliver. Honesty means the limits are named in planning: what the model gets wrong, where it needs a human, what is genuinely not ready. Silence about limits becomes a launch commitment.

For the statistics · one click each

Three questions for the public picture

These do not affect your score. They feed the anonymised, aggregated statistics; groups under 8 respondents are never shown.

How many AI features has your team shipped to users?

None yet
One
A handful
Many, across the product

How does your team decide an AI feature is good enough to ship?

Gut feel and demos
Manual spot checks
A repeatable eval set
An eval that gates launch
Not shipped one yet

What does your team mainly measure to judge an AI feature's success?

Usage and engagement
Usage plus some outcomes
User outcomes primarily
Nothing formal yet
Not shipped one yet

Your context

Used to calibrate the report. Company size and sector remain in the anonymized dataset; your email does not.