AI Collaboration: Share Work Across People and Assistants

AI collaboration means people use AI assistants within a shared workflow, with agreed goals, responsibilities, and checks. Start with one common brief, give each piece of work an owner, and save the result where the next person can find its status and context.

Using the same app is optional; keeping compatible instructions is essential. fenbs provides a shared project record that connected assistants can read and update, so the designer’s AI and the developer’s AI can work from the same decisions rather than separate versions of the story.

How do you set up a first collaborative workflow?

1. Choose one deliverable that needs input from more than one person, such as a client proposal or a website change.
2. Write a shared brief stating the outcome, audience, constraints, sources, and definition of finished.
3. Divide the deliverable into distinct tasks and name the person responsible for each result.
4. State what assistants may draft or change and which actions need a person’s approval.
5. Review the result against the original brief, then save progress and the next action.

Keep the first attempt small enough that the team can inspect the complete result. If the workflow fails, you want to discover whether the problem was an unclear brief, missing access, weak source material, or an ineffective review. Expanding several processes at once makes those causes harder to separate.

For individual starting points, use the guide to AI at work. The additional step for collaboration is making your instructions, dependencies, and completion checks understandable to someone who was absent from your conversation.

What belongs in the shared brief?

Your shared instructions should answer what you are making, why it matters, what must remain true, and what counts as acceptable evidence. Link to approved source material instead of pasting inconsistent excerpts into separate conversations. Distinguish a settled requirement from an open question.

• Outcome: a concrete result the team can recognize and review.
• Audience: who will use the result and what they need from it.
• Constraints: approved wording, technical boundaries, and actions requiring permission.
• Inputs: current sources, their owners, and any gaps that affect the work.
• Acceptance: the checks a person will use before treating the work as complete.

Say a studio is preparing a project proposal. The designer can explore layout while the project lead drafts the scope, provided both know that the delivery date is still unapproved. Neither assistant should fill that gap with a plausible date simply to make its draft look finished.

When a person settles that question, update the current record. fenbs keeps the agreed answer in Decisions and rules alongside the tasks it affects; how it works shows that shared context loop.

How should you divide responsibilities?

Give an assistant a defined contribution rather than a vague instruction to “help the team.” It might extract questions from approved notes, compare two drafts, or suggest tests. A person remains responsible for deciding whether that contribution is good enough for the next stage.

Separate responsibility for doing the work from authority to approve it. One colleague may prepare a client message, another may verify its facts, and the account lead may authorize sending it. Naming these roles prevents a polished draft from slipping through as an approved communication.

NIST’s guidance on human-AI interaction↗ (opens in a new tab) emphasizes defining and differentiating human roles and responsibilities. A practical team rule is to name who may accept, reject, or stop the assistant’s work before the assistant starts.

Also set an editing boundary. Two assistants can safely prepare separate drafts, but simultaneous edits to the same deliverable require coordination. Agree which version is current, who combines contributions, and when others should pause.

How do you keep assistants working from the same facts?

Ask each assistant to read the current project context before acting. Then have it briefly restate the relevant goal, constraints, and proposed next step. This gives a person a chance to catch a mistaken starting point before the assistant produces a large amount of unusable work.

A private conversation can contain useful reasoning without being the authority for everyone else. Save the important outcome in the shared record and point people to that record. The guide to AI memory for teams explains how to keep context available across sessions.

Use separate tools when the work calls for them: the guides to AI group chat and AI whiteboards cover those conversation and visual formats. Neither format removes the need to record what the team actually agreed.

What should the team review before accepting a result?

Review the result against the acceptance checks, not against how confidently it was written. Check factual claims against the original sources, calculations against the underlying data, and changes against the intended behavior. A second assistant’s agreement can suggest where to look; it is not independent proof.

Record what the reviewer checked and what remains uncertain. NIST’s AI risk management framework↗ (opens in a new tab) treats documentation and ongoing review as part of accountable AI use. For a team, “reviewed the source figures; client approval pending” is more useful than “looks good.”

Broader questions about permitted data and organizational obligations belong in AI compliance for work teams. Questions about choosing an application belong in AI applications for work and AI platforms for team work.

How to do this with fenbs

1. Create a free account and test the process on one project. Use a team kanban board with appropriate roles when colleagues need to share it.
2. Add the project goal and its tasks. Record the agreed constraints as Decisions and rules, keeping unsettled choices open for a person.
3. Connect a supported assistant from Integrations. Ask it to read the project context and explain its contribution before beginning.
4. Review its work and update the task with what was checked. Ask it to save progress, unresolved questions, and the next step before ending the session.

fenbs does not create the proposal, host the model, or replace the tool where you edit the final deliverable. If your work stays in one short conversation and has no shared decisions or handoff, maintaining a separate record may add little value.

Test the workflow by asking a colleague to continue without your verbal explanation. If they can identify the current goal, the responsible person, the last verified result, and the next action, the record is doing its job. Explore the team brain guides as the shared process grows.