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Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →In a CIO demonstration published April 1, 2026, Zapier’s Copilot turns a spoken instruction into a visible workflow that drafts and sends a personalized follow-up email. The example connects Google Forms, AI by Zapier using the episode’s named ChatGPT 5 mini model, a five-minute delay, and Gmail. It also shows why generating a workflow is not the same as proving it is correct: Zapier’s Director of AI Transformation, Philip Lakin, reviews test records, fixes a field mapping, and previews the email before testing delivery.
What the Zapier demo builds
In CIO’s 24-minute DEMO episode, host Keith Shaw speaks with Philip Lakin, Zapier’s Director of AI Transformation. Lakin asks Copilot to create a lead follow-up process from a spoken instruction. Copilot presents the result as a visual sequence of steps rather than leaving the instruction as a chat response.
- Trigger: A new sales-interest submission in Google Forms starts the workflow.
- Draft: The form data goes to AI by Zapier, with ChatGPT 5 mini named as the model in the episode, to generate a personalized subject line and email.
- Wait: The workflow pauses for five minutes.
- Send: Gmail sends the drafted message.
The example illustrates a practical use for AI inside connected software: information enters through one app, an AI step creates content from it, and another app carries out an action. The model name reflects the episode’s April 2026 demonstration; the episode does not establish current model availability.
How the prompt becomes a workflow—and where review fits
Copilot automatically maps form data into later steps in the demonstrated build, but the mapping is not treated as infallible. While checking the workflow, Lakin notices and corrects a field mapping. He then tests with records, previews the generated email, removes a signature so Gmail can handle it, and tests delivery.
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That sequence is the useful operational lesson: a natural-language instruction can get the workflow started, but the resulting steps and output still need checking. Lakin describes the approach this way: “This is human-in-the-loop. AI gives a strong first draft, but it’s still my job to review it.” The episode demonstrates his review process; it does not show that every generated workflow or email will be accurate without supervision.
Is it an AI agent or an automation with an AI step?
Lakin explicitly characterizes the example as “a deterministic workflow with an AI step, not a full AI agent.” In this build, the form trigger, five-minute delay, and Gmail action are laid out as steps. AI generates the email content within that structure.
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| Concept | How the episode describes it | What that means for this example |
|---|---|---|
| Structured workflow with an AI step | Steps and actions are explicitly arranged; AI handles a bounded part of the process. | The form trigger, delay, and email action surround AI-generated copy. |
| AI agent | Lakin contrasts agents as more inference-driven and less controlled. | The episode does not build or test an agent, so it offers no direct performance comparison. |
This is Lakin’s distinction and reliability rationale, not a universal finding established by a head-to-head test. He also advises assigning agents smaller, focused tasks, comparing editing five pages with editing 100; that is practitioner guidance from the interview, not a measured result.
What problem is Zapier trying to solve?
The interview’s practical contrast is between connecting tools into a workflow, waiting for software vendors to add the AI features a company wants, and doing the work manually. In the demonstrated case, a form submission can lead to an AI-assisted email without a person copying the details between separate apps. Lakin says Zapier connects more than 8,000 apps; that is his statement in the 2026 interview, not an independently verified current app count.
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What the demonstration does—and does not—show
The episode is a product demonstration by a Zapier executive, not an independent evaluation. It shows one workflow being built, corrected, reviewed, and tested. It does not establish a general success rate, quantified time savings, comparative reliability, or that companies broadly use such workflows successfully. For a real deployment, the demonstrated mapping and message checks are relevant safeguards, not proof that oversight can be removed.
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