If You Can't Decide After Verification — The One Line That Separates GO/NO-GO

Surveys have been conducted. Interviews have been done. Results are out.

Yet, whether to proceed or not remains undecided. Even after reviewing the numbers, the situation is the same. It looks promising, yet insufficient.

Many people stop at verification. However, the stopping point is often not because verification wasn't done, but because decisions can't be made based on the verification results. The results are in hand, but moving to the next step is impossible.

The verification wasn't done incorrectly. Often, it's just a matter of sequence.

It's because the passing criteria are set after the results are out

When you look at results without any criteria, any number can be interpreted as you wish.

• If 6 out of 10 people say it's good → "More than half are positive."
• If 4 out of 10 people say it's good → "The initial target is usually a minority."

Both interpretations make sense. That's why decisions aren't made. It's not that the numbers are lacking, but rather how to interpret those numbers isn't predetermined.

This problem disappears if you change the sequence. Write down the passing criteria before starting the verification.

One line to write before verification

It looks like this.

"If ○○ people out of ○○ do ○○, proceed. Otherwise, stop."

Here's an example.

• This is insufficient — Check if there is demand
• This is how it should be written — If 8 out of 20 interviewees have spent money or time on this issue in the past 3 months, proceed

Though it's one line, it contains three elements.

1. Target — Not just anyone, but who to ask
2. Number — How many out of how many (it's better to write in absolute numbers rather than ratios)
3. Action — Not just those who said it's good, but those who actually did something

Once this line is written, there's no need to interpret the results when they come out. It's either countable or not countable.

Four designs that make results unusable

Even if the passing criteria are set, if the questions are poorly designed, the resulting numbers can't be used. There are recurring issues.

1. Structure that only finds reasons for success

Verification is the process of identifying reasons for failure. However, many questions are structured to gather reasons for success, like "Would this service be beneficial to you?" This is natural since you're asking about something you want to create.

If you include positive questions, it's better to also include negative questions of equal weight. "In what situations do you think you wouldn't use it?" should be placed at the beginning. If placed at the end, it's less likely to be mentioned after leaning towards positivity.

2. Questions already contain the answer

"Would you use a tool that automatically organizes for busy entrepreneurs?"

This isn't a question but a description. Since my hypothesis is all in the sentence, the respondent only chooses yes or no.

It's better to ask without the hypothesis. "How did you handle that task last month?" reveals much more.

3. Asking about intentions

"Would you use it?" is about intention. Intentions are generous, especially regarding price. The amount answered to "How much would you pay?" often doesn't match the actual payment.

It's better to ask about actions.

• This is insufficient — Would you use it for 30,000 won a month?
• This is how it should be asked — Are you currently spending money on this task? Where and how much are you spending?

Money already being spent is a fact, not an opinion.

4. Trying to prove everything at once

If you ask about demand, price, and necessary features all in one survey, nothing will come out. When feature questions are scattered, people generally answer "good" to everything. Each one seems good.

One verification should have one question. If it's time to look at demand, postpone the price for later.

There are not two but three answers

Once the passing criteria are set, the results fall into one of three categories.

• Proceed — The criteria have been met. Move to the next stage.
• Stop — The criteria were not met. Abandon this direction.
• Narrow the scope and try again — It didn't pass overall, but it did in a specific group.

The third option appears often. If full-time and part-time, beginners and experienced people are mixed in one sample, the average comes out ambiguous. In this case, dividing the mixed group and recounting often shows that only one side meets the criteria.

That's the initial target. The verification didn't fail; rather, the target wasn't narrowed down yet.

Stopping is not a failure

When results show that the criteria weren't met, there's a tendency to want to verify again. This time, with slightly different questions. The more preparation involved, the stronger this tendency.

GO obtained this way often turns out to be much costlier later. Because it's realized after creation.

If 2 weeks spent on verification prevents 3 months of development, that's an achievement. Documenting a decision to stop is also a result. When a similar idea arises later, being able to say "This was shelved with these criteria back then" prevents going through the same cycle twice.

When delegating to AI

It's common to draft verification designs with AI. If you simply ask "Create survey questions," questions that gather reasons for success are likely to come out. Most existing surveys in the world are structured that way.

Requesting like this changes the outcome.

"First write down what answers would come out if this hypothesis is wrong, and create questions that can capture those answers. Ask about actions in the past 3 months instead of intentions."

The same goes for passing criteria. AI is good at creating questions, but it can't determine how many people need to do something for it to proceed. That's for the person spending the money and time to decide.

If you do just one thing today

If you're currently in the middle of verification or holding verification results, write down one line on paper.

"If ____ out of ____ people do ____, proceed."

It's okay even if the results are already out. However, it must be the number you would have written without seeing the results. If you can't write it, the verification isn't complete yet; there's no criteria.

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If you're holding verification results but can't move to the next step
With a 2-minute diagnosis, you can see one task to address first, a 2-week pilot start plan, and the risks that must be checked by a person. Feel free to answer as much as you're comfortable with.

STAR-T AI Business Operation Diagnosis →

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Footnotes

① Reference Materials & Verification Date
This article does not cite external statistics or research. The basis is patterns repeatedly confirmed in lectures and consulting sessions (over 500 individual consulting sessions for prospective entrepreneurs accumulated, based on SSOT history). The numbers in the text — "6 out of 10", "4 out of 10", "8 out of 20", "30,000 won a month", "past 3 months", "2 weeks"/"3 months" comparison — are all examples to show how to write, not actual survey results or measurements. Verification date: 2026-08-06.

② Unused Figures
Widely cited figures like "what percentage of startups fail due to lack of demand" were not used. This is because the original source and reference year could not be verified. Figures from internal surveys or CS responses were also not used.

③ Creation Method
The draft was created with AI and verified and edited by a human. It underwent three checks for facts, tone, and legal compliance before publication.