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How to Make an AI Demo Feel Like Real Work

“Demo by doing” lets people experience AI-supported work before you explain the tool. Here’s how Thanh Pham’s examples work—and what they don’t prove.
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“Wait, that was AI?” is the moment Thanh Pham wants an AI demonstration to create—not through a staged presentation, but by letting someone experience useful work first. His “demo by doing” approach means using AI in an ordinary professional task, then explaining its role when the conversation naturally turns to it.

What “demo by doing” means

In a conventional demo, a presenter explains a tool while an audience watches. Pham’s alternative is to let the work itself make the capability visible: use AI to handle a real, appropriate task, deliver the result, and disclose the tool when there is a natural opening.

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The distinction is about the order of the experience, not hiding AI. The person should understand what happened and have a chance to ask questions; the approach is not a reason to mislead anyone about how work was produced.

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Two examples from Thanh Pham

Scheduling a discovery call

Pham describes an AI scheduling agent built on Lindy that replied to emails and coordinated a discovery-call appointment. He says he explained the agent’s role during the call, after the other participant had already experienced the scheduling exchange. This is a personal anecdote; no independently verified performance measurements are provided. Pham’s account at Asian Efficiency does not establish Lindy’s current features or pricing.

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Organizing notes during a planning call

In a second anecdote, Pham says a call was being transcribed and organized from the start. When the client reached for a notepad, they realized notes were already being captured. The account does not name the transcription service, and it does not establish how accurately the notes captured the conversation.

Why the approach may feel more convincing

Pham’s explanation is that people watching a prepared presentation evaluate a tool from the outside, while someone who has received useful work can judge an actual experience. He also argues that using AI for routine tasks can make it seem like a normal part of work rather than a novelty introduced for a pitch.

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Those are the author’s reasons for recommending the method, not demonstrated sales or adoption outcomes. The article reports no controlled comparison, survey, conversion data, or measured productivity gains. A good experience may make an AI capability more concrete, but it does not prove the tool is suitable for every task or audience.

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How to try “demo by doing” responsibly

  1. Choose a real, low-risk task. Consider a follow-up draft, a research summary, meeting coordination, or call notes—routine work where an imperfect first output will not create serious consequences.
  2. Keep the result reviewable. Check AI-generated replies, summaries, and notes before they are sent or relied on. Correct errors and handle sensitive decisions yourself.
  3. Fit the tool to the workflow. For any AI service you consider, assess whether it performs the task adequately, produces reliable output, protects information appropriately, works with your existing tools, and leaves a manageable amount of human review.
  4. Be thoughtful about information and consent. Before using AI with someone else’s messages or a meeting, consider what information the service will receive, who can access it, and whether participants should be told or asked before recording or transcription begins. The cited account does not explain its privacy or consent practices, so it cannot answer those questions for your situation.
  5. Explain the AI when relevant. Once the work has a natural result to discuss, say what the tool did and what you reviewed. Invite questions rather than turning the explanation into a sales pitch.
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What the examples do—and do not—show

The scheduling and transcription stories illustrate how AI-supported work might be encountered in an ordinary interaction. They do not identify the transcription product, establish current product capabilities, or show that an unannounced workflow will improve trust, sales, or adoption. Treat “demo by doing” as a practical way to make a capability tangible, not a proven formula or a substitute for transparency.

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