How to Scope an AI Feature

AI work is harder to scope than conventional development because "does it work" is a spectrum rather than a yes or no. This is the process we use to turn that spectrum into something estimable.

Step 1 — State the job in one sentence

Not the technology, the job. "Turn a customer email into a structured support ticket." "Suggest three tags for an uploaded photo." If it takes a paragraph, it is more than one feature and should be split.

Vague scope is the root cause of most AI projects that never finish. "Add AI to our app" is not a scope; it is an aspiration.

Step 2 — Define wrong, and what it costs

Write down what a bad output looks like and what happens to the user when it occurs. Then classify:

Cheap failure — user ignores a suggestion and moves on. Proceed.
Annoying failure — user must undo something. Proceed with an easy undo.
Expensive failure — wrong data saved, wrong message sent, wrong charge applied. Redesign so the user confirms first.

This single step eliminates more bad AI features than anything else, and it costs an afternoon.

Step 3 — Build the evaluation set before building anything

Collect 50–100 real inputs, ideally sampled from actual usage, and write the expected output for each. Include the awkward ones: empty, very long, wrong language, deliberately confusing.

This set is the specification. It defines what "working" means, lets you compare approaches objectively, and tells you when a change has made things worse. Without it you are making decisions on impressions.

Step 4 — Set the accuracy bar

What percentage correct makes the feature worth shipping? This is a business decision and must be made before building, because it determines how much engineering is justified.

Be realistic. Perfect accuracy is not available. If the feature only works at near-perfect accuracy, revisit step 2 — it probably needs a confirmation step rather than better models.

Step 5 — Decide the fallback

What happens when the model is unavailable, too slow, or returns something unusable? Options include falling back to manual entry, a cached previous result, or a simpler rule-based path. Whichever you choose, it is part of the feature and part of the estimate.

Step 6 — Set a cost ceiling

For API-based features, calculate cost per use, multiply by realistic monthly volume, and check the result against what the feature earns or saves. Do this at current volume and at ten times current volume.

If the arithmetic does not work at scale, decide now whether to use a smaller model, cache aggressively, run on-device, or not build it.

Step 7 — Prototype against the evaluation set

Now build the smallest thing that produces output, and run it against the set from step 3. You will learn quickly whether the accuracy bar is reachable. This is deliberately before building any interface — if the core does not work, the interface is wasted.

What this produces

A one-sentence job, a defined failure cost, an evaluation set, an accuracy bar, a fallback path, and a cost ceiling. That is an estimable piece of work, and it is also enough information to decide not to build it — which is a good outcome when the arithmetic says so.

This slots into a normal technical discovery phase, and pairs with AI features that actually ship.

Frequently asked questions

How do you scope an AI feature?

State the job in one sentence, define what a wrong output costs the user, build an evaluation set of 50-100 real inputs with expected outputs, set an accuracy bar, decide the fallback when the model fails, set a cost ceiling, then prototype against the evaluation set.

What is an evaluation set and why does it matter?

A collection of real inputs with expected outputs, built before the feature. It defines what working means, lets you compare approaches objectively, and tells you when a change has made results worse. Without one you are judging on impressions.

How accurate does an AI feature need to be?

It depends on what a wrong answer costs. If failure is cheap and easily ignored, moderate accuracy is fine. If failure is expensive, the feature needs a confirmation step rather than a better model.

How do you budget for AI running costs?

Calculate cost per use, multiply by realistic monthly volume, and check it at both current scale and ten times current scale. If the arithmetic fails at scale, consider a smaller model, caching, on-device processing, or not building it.

Should you build the interface or the AI first?

The AI core first, tested against your evaluation set. If the accuracy bar is not reachable, any interface work would have been wasted.

Talk to us about your build

KIDA Studios builds custom software, apps, games, AR and XR across Apple platforms, Windows, Android, web, and embedded. If you have a project in mind, a short discovery call is the fastest way to get a realistic scope and number.

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