AI that suggests the work vs AI that does the work, where does your team draw the line?
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There is a category distinction that does not get talked about enough.
Most AI tools in the GTM space are suggestion engines. They produce a draft, a recommendation, a report, a list of next actions. The work still lands on a person to execute.
A smaller category of tools actually attempt to execute: send the email, publish the post, update the CRM record, spend the budget.
For teams using AI in GTM right now, where do you draw the line between:
- AI I trust to run autonomously
- AI that needs to queue for my approval before any external action
- AI I only use for drafts and suggestions
And is the line in the same place for all actions, or does it shift depending on what the action is and who it touches?
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I run this exact split in a health app I'm building. Voice meal logging goes straight through a cloud function that returns structured items, no draft step, no confirm screen, it writes directly to the log. What I didn't expect is that the real judgment call moved earlier than the AI trust question. Before any transcript reaches the model at all, I reject it if it's only filler words like um, yeah, testing, so the model never even gets asked to interpret garbage. My approval gate ended up being about whether to generate an output at all, not whether to trust the one it gave me.
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