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“Wait, that was AI?” is the moment behind demo by doing: let someone experience useful AI-supported work in an ordinary interaction, then explain the tool when it naturally comes up. Thanh Pham describes this approach in Asian Efficiency. His examples make the idea concrete, but they are anecdotes—not evidence that it reliably improves sales or adoption.
What “demo by doing” means
Instead of opening with a prepared presentation, use AI to help complete a real task the other person already needs. They encounter the result as part of the work, rather than watching a staged demonstration. If the tool becomes relevant, explain what it did and where human judgment fit.
The distinction is about context, not concealment. The point is to make the work useful first, not to mislead someone about how it was produced. In situations involving sensitive information or recorded conversations, consider privacy, consent, and the expectations of everyone involved before using AI.
Two examples Thanh Pham describes
Scheduling a discovery call
Pham says an AI scheduling agent built on Lindy handled email replies and coordinated a discovery-call appointment. He says he disclosed the agent during the call, after the other participant had already experienced the scheduling exchange. The article reports no independently verified performance measurements for this example.
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Arriving with organized call notes
In another anecdote, Pham says a planning call had been transcribed and organized from the start. A client reached for a notepad, then realized notes were already being captured. The article does not identify the transcription service.
Why the approach may feel different from a prepared demo
Pham’s argument is that a person watching a presentation evaluates a capability from the outside, while someone who has already received useful work can judge an experience they actually had. He also suggests that using AI for routine work can make it feel like a normal part of a workflow rather than a special performance.
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Those are explanations for the approach, not established outcomes. The article presents no controlled comparison, survey, or conversion data showing that “demo by doing” works better than a prepared presentation. Its closing line captures the idea—“The best demo you can give someone is the one where they don’t realize they’re being demoed until it’s already over”—but should be read as Pham’s view, not a measured result.
How to try it in a real workflow
- Choose a routine task with a useful output. Follow-up drafts, research, summaries, and meeting coordination are examples Pham recommends. Pick work that genuinely helps the other person; do not create a task just to showcase AI.
- Use AI where a person can review the result. Check factual claims, names, dates, tone, and any commitments before sending or sharing the work. Decide in advance what needs human approval.
- Deliver the work in its normal context. Share the follow-up, summary, or coordinated meeting as part of the interaction. Avoid presenting the output as entirely human-produced if that would give the other person a misleading impression.
- Explain the AI’s role when it is relevant. Be specific about what the tool handled and what you checked or changed. Treat the explanation as context, not a claim that the tool is flawless.
When a prepared demo is still the better choice
Every workflow does not naturally produce a safe, visible example. A prepared demo may be more appropriate when people need a clear, repeatable view of a process, when the live task involves confidential information, or when the AI output cannot be reviewed before it affects someone else. The goal is to choose a demonstration format that fits the task and the audience—not to avoid explaining the technology.
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What the examples do—and do not—establish
Pham’s stories show what the approach can look like: an AI agent coordinating email and a call, and a conversation prepared with transcription and organized notes. They do not establish that a particular service is best, that the workflows are available at specific prices or feature levels today, or that the method produces better business results. The source names Lindy only in the scheduling anecdote and does not name the transcription tool.
A lived example can make an AI capability easier to picture than a slide or scripted pitch. Whether it is more persuasive depends on the task, the audience, the quality of the work, and how transparently the AI’s role is handled.
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