The difference between an AI assistant and an AI coworker
Assistants answer questions. Employees do work. The distinction sounds semantic until you realize how differently they change your day. Here's what it actually means in practice.
The word 'AI' is doing too much work right now. It's used to describe everything from an autocomplete suggestion to a system that autonomously manages your ad spend. The category is so broad it's become nearly meaningless.
Here's a distinction that actually matters: AI assistant vs. AI coworker. They're not just different tools. They're different relationships, different workflows, and they change your job in fundamentally different ways.
What an AI assistant does
An AI assistant answers questions. You bring context to it, you ask it something, it responds. The interaction is stateless — it doesn't know what happened yesterday, it doesn't have access to your accounts, it doesn't take action in the world.
An AI assistant makes you better at thinking. It doesn't do the work.
What an AI coworker does
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An AI coworker executes. It has authenticated connections to your actual systems. It can pull data from Meta Ads, cross-reference it against Stripe revenue, and give you a reconciled table — without you providing any of that data manually. The same idea answers your phone — Merit's AI receptionist, mavrick.pro/missed-calls.
- →Connected to your real accounts and tools
- →Can take action (with your approval) — pause campaigns, generate reports, pull data
- →Has context about your business from prior interactions and connected data sources
- →Lives where your team works (Slack) rather than in a separate app
- →Runs missions on your behalf, not just responses to your queries
The workflow difference
With an AI assistant, your workflow looks like this: you do the work, you hit a thinking problem, you consult the assistant, it helps you think, you go back to the work.
With an AI coworker, the workflow inverts. You define what needs to happen, the employee executes it, you review and approve, it ships. You're in command, not in the weeds.
The practical implication
If you're still using an AI assistant to help you write the report you spent 90 minutes pulling the data for, you've automated the thinking but not the work. That's a partial solution.
The next step is to stop pulling the data at all. Let the employee do it. Your job becomes reviewing, approving, and deciding — not collecting.
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Brian MacDonald
Brian MacDonald is the founder of Merit, the AI coworker for marketing teams. Previously ran SetupClaw.tech, an AI deployment service for SMBs. Read more about Brian and the mission.
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