You have an idea for an AI agent that analyzes reports and suggests actions, but before investing four weeks of development, you need to know if users really want it.
A fake door is a tool to test the question before building. It consists in showing a realistic entry point to the function and measuring interest, explaining immediately that the function is coming or being explored. If done well, it helps to learn; if done badly, it risks to deceive.
Fake door must not deceive
Ethics is clear: you can probe interest, not fool or waste time. A correct example is an “Generate Auto Analysis” button that, if clicked, shows a transparent message: “We are evaluating this feature. Do you want to be notified or participate in beta?” Don’t promise non-existent results or simulate critical actions.
What to measure
It’s not enough to count clicks.
- how many people see the entry point;, how many people click;, what segments they come from;, what problems they say;, how many people ask for access to beta;, how many accept a short interview.
Open answers are valuable to understand which agent to build really, not just what you imagined.
Where it works best
Fake door is useful when development is expensive and uncertainty about demand is high. For example:
- agent who prepares campaigns;, agent who explains dashboard;, agent who creates automatic segments;, agent who analyzes customer feedback;, agent who suggests pricing.
If the risk is low and you can prototype in one day, better build a manual or semi-automatic version and observe.
From door to product
After the test, do not just look at the click rate. Ask yourself if the problem is urgent enough, if users would trust the agent, what data they need and what actions require human approval. A well done fake door indicates not only whether to build, but how to start.
How to apply it without complicated work
Not starting with the newest tool, but from the point where the team is wasting time or making decisions without data. An agent is not a brilliant chat: it must have clear inputs, limited tools, controlled memory and an explicit rule to pass the decision on to a person when the risk rises.
A useful sequence:
- Define which data the agent can read and which not; 2. Write the expected result in verifiable form; 3. Decide when human revision is needed before sending or saving output; 4. Measure time saved, avoided errors and cases where the agent stops.
What to measure to see if it works
The right question is not “have we used AI?” or “have we added a dashboard?” It is: what decision has become faster, clearer or safer? If it does not change a decision, the project remains technical decoration.
It measures at least three levels: spared operating time, quality of the result and confidence of the team. Time alone can deceive: a faster but less controllable flow is not an improvement. Quality alone can deceive: a perfect but slow system does not enter everyday work.