1.2.3 - The Risks You’re Avoiding Naming

Named risk

Are the key risks named before they hit users?

The call

Name the risks early. Otherwise AI helps you move faster toward a failure you have not prepared for.

Why it matters

The risks you avoid naming will shape delivery whether you name them or not. AI can help reveal assumptions quickly, but human judgement decides which risks deserve action before implementation expands. The difference is between controlled learning and late-stage rework that harms user trust.

Explainer

A named risk is not general caution. It is a specific failure that could happen, who it would affect and what would make it visible. Until you can say what could fail, where it would show up and how you would respond, the plan is too soft. AI can help surface options, but it cannot own risk judgement.

Make the named risk concrete

Compare the broad version with a version you can actually test.

• Too vague: There are some risks around whether the AI search results will be useful.
• Concrete enough to test: If a content creator’s saved context is too narrow, AI search returns the same results every time and the tool feels broken. The user stops trusting the results before they have tested enough to see the value.

The second version lets two people prepare for the same failure from it.

Check the named risk

• Pass: You can say what might fail, who or what it would affect and what action will reduce the damage.
• Fail: If the risk still sounds like a vague concern instead of a concrete failure, it is not named well enough yet.

Do not move into build, launch or rollout work until this passes.

How to use AI for the named risk

• AI chat: Rewrite the named risk until you can state all three parts clearly.
• vibeCoding: Build the thinnest flow that tests this named risk in practice before broader build work.
• AI-assisted coding: Carry the same named risk into implementation and review so the live system keeps the same decision.

Sharpen the named risk

Copy this prompt into AI chat, replace the bracketed lines with your real named risk and keep the instruction exactly as visible here.

You are checking whether this named risk is clear enough before you move forward.

Constraint:
The named risk must be specific enough that two people would prepare for the same failure from it.

Working draft:
Likely failure: [what might fail]
Affected user or workflow: [who or what it would affect]
Mitigation: [what action reduces the damage]

Task:
Decide whether this named risk is specific enough to guide the next decision. If it is vague, rewrite it so two people would make the same decision from this named risk.

Check:
- Would two people interpret this the same way?
- Does it stay concrete enough to guide the next step?
- Does it meet this bar: You can say what might fail, who or what it would affect and what action will reduce the damage.

Return:
- A corrected named risk
- A short explanation of what was vague

Evaluation

Before accepting the result, check whether two people would prepare for the same failure from it.

Example

To help you work through this, here is a real example. StartWithYourContext is an AI search tool built as part of the vibe2value project. Here is how its named risk was written using the three parts:

• Likely failure: If a content creator’s saved context is too narrow, AI search returns repetitive results and the tool feels broken.
• Affected user: The content creator stops trusting the results before they have tested enough to see the value.
• Mitigation: Show the user how their context shapes results and prompt them to broaden it when results become repetitive.

That risk is specific enough that two people would prepare for the same failure from it.

When there is more than one side

Not every product has a single set of risks. When a system serves more than one side, each side faces different failures and a risk named for one may be invisible to the other.

Multi-sided worked example

For example, StartWithYourContext has two different risk profiles:

• Content creator: Narrow context produces repetitive results. The user loses trust before seeing value.
• Developer: A breaking change in one layer of the stack silently affects another. The integration looks fine until edge cases surface in production.

Both risks are real, but they live in different parts of the system. If only one is named, the other fails silently.

Risk and mitigation

• Risk: Allowing unnamed risks to survive planning, which leads to weak decisions and rushed fixes after users are affected.
• Mitigation: Require one named risk, one assumption and one user-impact statement before each major build decision.

Key takeaway

Do not move forward until you can say what might fail, who or what it would affect and what action will reduce the damage.

Work through this in a workshop

If your risks are still unnamed, bring them to a free weekly workshop. Bring the messy part of your AI-assisted build and leave with a clearer next step. In some sessions, we walk through practical examples on the Cloudflare Workers stack to show how a rough idea turns into something that actually runs.

What do you think?

How are you identifying the risks you avoid naming and how is AI helping you make those risks explicit before they become user issues?