The AI Feature Scorecard: Which Idea Should Your SaaS Build First?

The list usually has an "ask anything" assistant at the top, because it plays well in a board meeting. Somewhere in the middle sits a smaller idea: draft the update users write by hand every week, or sort the requests someone sorts by hand every morning. Nobody gets excited about it. It's often the one that should ship first.

Ideas aren't the scarce part. The trouble is that they get compared on excitement, and excitement doesn't predict which feature customers will still use in month three.

So here's the scorecard we use instead: five lines scored 1 to 5, plus two gates that can veto an idea regardless of its total. Fill it in with the people who know the product and the data. It's meant to be copied.

The five lines

Score every idea from 1 to 5 on each line, and be stingy with fives.

1. Frequency: how often does the job happen?

A job that happens many times a day gives the feature a chance to become a habit, and gives you enough usage to learn from. A job that happens once a quarter won't, however clever the output is.

• 5: many times a day per user
• 3: about once a week
• 1: a few times a year

2. Data readiness: does the context exist, and can you reach it?

The model is only as good as what you hand it. Score whether the data the feature needs already exists, in a shape you can query, with permissions you can enforce.

• 5: in your database, structured, reachable through your own API as the user
• 3: it exists, but it's scattered or buried in free text
• 1: it lives in people's heads, or you'd have to start collecting it

A low score here isn't always final. When we built Irresistible Bot for Vrinda Normand, each user's copy had to sound like their own brand, and no system held that brand until the product asked for it. Structured onboarding collects it before the first generation. Designing that step was part of the feature, and the whole build took one month for $5,000.

3. Verifiability: can someone tell a good output from a bad one?

This is the line most backlogs skip. If a user can check the output at a glance, because it's a draft they'll edit anyway or a category they can see is wrong, mistakes stay cheap and trust builds. If nobody can tell whether the answer was right, you can't write evals, and users stop trusting the feature after its first confident mistake.

• 5: correctness can be checked automatically, or by the user in seconds
• 3: an expert can check it, with some effort
• 1: nobody can tell until much later, if ever

4. Risk: what does a wrong output cost?

Score this one inverted, so 5 means safe.

• 5: a mild annoyance, easy to undo
• 3: a support ticket or an awkward email
• 1: it moves money or reaches a customer without review

5. Measurable value: which number moves?

Name the metric before you build: time per task, tickets per week, trial conversion, workflows completed. If the best you can write is "customers will love it", that's a 1.

• 5: moves a number you already track, from a known baseline
• 3: moves a number you could start tracking
• 1: no number, only enthusiasm

The two gates

Add the five scores for a total out of 25. Check the gates before you look at the total.

Data readiness of 2 or lower. The feature can't ship yet, however well it scores elsewhere. Either the first project is getting the data in order, or you pick another idea.

Verifiability of 2 or lower. You won't be able to tell whether it works, so you can't improve it or defend it to a customer. Park it until you can describe what a correct output looks like.

I'd rather ship a 17 that passes both gates than a 21 that fails one.

A worked example

Here's an illustrative backlog for a B2B SaaS that manages client projects. The scores are invented for the example. The reasoning is the point.

The assistant fails both gates. Its data is everything in the product, with every permission rule attached, and nobody can say what a correct answer to "anything" looks like. The churn model fails verifiability, since you find out whether it was right months later.

The weekly update wins. The project activity is already in the database, and the account manager reads every draft before it goes out. Time spent writing updates is easy to measure, too. Auto-sorting is a close second and a good next feature.

What our own builds had in common

The AI work we've shipped fits the same shape. Coaching Genie fully automates helping coaches write course content: a job coaches come back to again and again, where the coach can judge a draft by reading it. For Xperiencify and Coaching Genie we also fully automated content-idea research, a manual workflow that someone used to do by hand. Neither started as an "ask anything" box.

The scorecard, ready to copy

One block per idea:

Idea: ________________________________________

Frequency          1  2  3  4  5    (5 = many times a day per user)
Data readiness     1  2  3  4  5    (gate: 2 or lower means not yet)
Verifiability      1  2  3  4  5    (gate: 2 or lower means not yet)
Risk (5 = safe)    1  2  3  4  5
Measurable value   1  2  3  4  5    (metric: ______________________)

Total: ____ / 25          Both gates passed:  yes / no

Then two rules:

1. Build the highest total that passes both gates. Break ties with the clearer metric.
2. Write the acceptance test before the first line of code: what counts as a correct output, and what the feature does when it isn't sure. A fixed-price AI project needs a narrow acceptance test covers how.

Rescore the backlog after the first feature ships. Real usage changes the numbers, and it's cheaper to learn that before feature two than after it.

Related reading

• Your AI prototype works. Here is why it will fail in production: what the winning feature needs before it reaches every customer
• The $5K AI agent: what fits in 30 days: what a first build looks like on a fixed budget
• RAG is not your product: when "ask anything" is the wrong first feature

The service: Fractional CTO. For SaaS teams: B2B SaaS founders. Ready to build the winner: AI Product Development.