An AI coworker is AI that takes on part of your team's work the way a team member does. It owns specific steps, works on them when they're due, reads the same goals and decisions as the people around it, and hands what it produced to a person for review.
The term is new, and vendors use it loosely, often for any chatbot with a name. This guide explains what makes AI a coworker rather than a tool, how it differs from AI assistants and AI agents, what it should and shouldn't do, and how to start working with one.
What is an AI coworker?
An AI coworker has the same four things a new team member needs before they can be useful:
- A role. It owns defined parts of the work, such as drafting the weekly client report or summarizing interview notes, not "whatever someone asks."
- Context. It reads the project's goal, the earlier steps, the files and the decisions already made, instead of only what one person types into a prompt.
- A schedule. It works when its step is due, without someone remembering to ask.
- A reviewer. A named person checks its output before anyone builds on it.
Take any of those away and you're back to an assistant: useful for the person using it, invisible to everyone else.
AI coworker vs AI assistant vs AI agent
| AI assistant | AI agent | AI coworker | |
|---|---|---|---|
| Who starts the work | You, with a prompt | A trigger, such as a new form entry | The plan: when its step is due |
| What it knows | What you paste into the chat | The data its task is connected to | The shared project: goals, earlier steps, files, decisions |
| Where the output goes | Back to you, to copy somewhere | Into another system, automatically | Into the team's work, marked as AI work |
| Who checks it | You, if you remember | Often nobody, until something breaks | A named reviewer, in the next step |
| Best for | One person's drafting and research | Repeatable tasks with fixed rules | Recurring team work with handoffs |
Most teams end up using all three. The difference that matters is where the work stalls: if it stalls while one person writes, an assistant helps. If it stalls between people, waiting for a draft, a summary or a decision, that's the gap an AI coworker fills.
What can an AI coworker do?
Good AI coworker work is preparation: the work that has to exist before a person can decide or act.
- First drafts of briefs, plans, reports, release notes and emails
- Summaries of feedback, survey answers, interviews and long threads
- Research and comparison tables, with sources
- Preparation for meetings: an agenda from open items, a recap of what changed since last time
- Files, such as a slide outline, a spreadsheet or a checklist
Keep these with people, even when AI could produce an answer:
- Approvals and go or no-go decisions
- Priorities, budgets and trade-offs between teams
- Anything about a person: feedback, performance, hiring, conflicts
- What you send to a client or publish under your name, until someone has read it
How to work with an AI coworker
- Pick one recurring process. A weekly report, a client onboarding or a launch works better than a one-off project, because you'll see it improve from one round to the next.
- Write the context down. One short overview of the goal, the audience and the rules the work must follow. The AI reads it, and so do new team members.
- Give it two or three steps. Choose drafting or summarizing steps that someone else usually waits for.
- Put a person after each one. The person who needs the output reviews it, in a step with its own due date.
- Review it like a new hire's work. Say what's wrong, fix the context that caused it, and expect fewer corrections each round.
- Measure the handoffs. After a month, compare how long the next person waited and how often work came back for rework.
What an AI coworker looks like in Nova
In Nova, you assign Nova AI to a whole session or to single steps, the same way you assign a team member. You choose when it starts: right away, on the session's start date, after the previous sessions are complete, or when someone starts it. It reads the project overview, the earlier steps, the session's files and the conclusions of connected sessions. Steps set as human only make it stop and wait for a person.
Its work stays in the session, where the team can read it, change it and approve it. Some changes, such as moving notes on a board, need your approval first. AI work uses your workspace's weekly AI credits, and the workspace owner can turn AI features off for everyone.
Try it on one process
Pick a template from the process library, assign Nova AI to its drafting steps and keep the reviews with your team. See how AI coworkers work in Nova.
Frequently asked questions
Is an AI coworker the same as an AI agent?
Not quite. Both do work without being prompted every time. An AI agent usually automates a task end to end, triggered by an event. An AI coworker owns steps in a process the team shares: it works from the team's context, on the team's dates, and hands its output to a person for review.
Which AI model does an AI coworker use?
Most AI coworkers run on a large language model such as Claude, GPT or Gemini. The model does the writing and reasoning. What makes it a coworker is everything around the model: the role it owns, the shared context it reads, the dates it works to and the review step after it.
Is there a free AI coworker?
You can try one for free. Every new Nova workspace starts with a free trial of Nova Pro, which includes AI credits for Nova AI, and you don't need a credit card. After the trial, Nova Free stays free for your whole team but has no AI credits, so AI coworkers need Nova Pro.
Will AI coworkers replace team members?
They replace tasks, not roles. An AI coworker takes the drafting, summarizing and preparation that fills a lot of people's weeks. The people on the team spend more of their time reviewing, deciding and talking to customers and each other, which is the part AI can't own.
What happens when an AI coworker gets something wrong?
The same thing that happens when a person does: the reviewer catches it in the next step and sends it back. That's why every AI step whose output someone else depends on should be followed by a human review step, and why the AI's work should stay where the team can see and change it.