- What is multiplayer AI?
- Single-player vs. multiplayer AI
- How do teams collaborate on AI work instead of everyone using their own chatbot?
- What does multiplayer AI look like in practice?
- How do shared workflows make the whole team better at AI?
- What gets shared in multiplayer AI, and what stays private?
- How we made AI multiplayer at Ramp
Most companies adopted AI one person at a time. Everyone has their own chatbot, their own saved prompts, and their own way of explaining the business to it, and almost none of that reaches the person at the next desk.
Multiplayer AI is the shift from that setup to a shared one: teammates work with AI in the same sessions and projects, start from the same company context, and reuse and improve each other’s best workflows. Single-player AI makes one person faster. Multiplayer AI makes the company smarter, because what one person figures out becomes everyone's starting point.
Key takeaways
- Multiplayer AI means people and AI work together in shared conversations, projects, and workflows, instead of each person using a private chatbot.
- Most AI at work is still single-player, so each person's best work stays in their own chat history and leaves when they do.
- Handoffs are where multiplayer AI pays off first: a teammate picks up the work where it stopped instead of starting over.
- Shared context and shared workflows matter more than shared chats, because they keep working after the conversation ends.
- Sharing should be a choice. Decide what's shared and what stays private before you roll anything out.
What is multiplayer AI?
Multiplayer AI is a way of working where several people and an AI assistant work in the same place: the same session, the same project, and the same company context. Work one person does with AI stays visible and reusable, so teammates can pick it up, add to it, and build on it instead of starting over.
The term comes from collaborative software. Figma described its editor as "the first design tool with live collaborative editing," and "multiplayer" became shorthand for products where people work on the same thing at the same time. Multiplayer AI applies the same idea to work done with AI.
Multiplayer AI also isn't the same as multi-agent AI. Multi-agent AI is several AI agents coordinating with each other on a task. Multiplayer AI is about people working together, whether one agent is involved or many.
Single-player vs. multiplayer AI
Most AI at work is still single-player. In Microsoft's 2024 Work Trend Index, 78% of AI users said they bring their own AI tools to work, meaning tools their company didn't provide. That puts company data in places IT can't see, and it means each person's best work lives in their own chat history, where it disappears when they close a tab, go on vacation, or leave.
Single-player AI | Multiplayer AI | |
|---|---|---|
Who sees the work | One person | Everyone invited to the session or project |
Where context lives | One person's chats and custom instructions | Shared projects and company-wide context |
When someone gets stuck | They start over, or paste the chat into a message | A teammate joins and picks up where they left off |
When someone leaves | Their prompts and workflows leave with them | Their workflows and context stay available for others to build on |
What compounds | One person's speed | The whole team's starting point |
How do teams collaborate on AI work instead of everyone using their own chatbot?
Teams collaborate on AI work by moving three things out of private chats: context, sessions, and workflows. Shared context means everyone's AI starts from the same definitions. Shared sessions let people work through one problem together. Shared workflows turn the best process into the next person's head start. Shared chats are the easiest of the three to turn on, and the least useful on their own.
Context comes first. When every teammate's AI knows the same metric definitions, policies, and internal names, two people asking the same question get the same answer, and collaboration stops being an exercise in reconciling them. A shared context, kept current by named owners, is what people mean by a company brain.
Sessions come next. Most major assistants now offer a shared space: OpenAI's group chats in ChatGPT let you "collaborate with others, and ChatGPT, in the same conversation," with up to 20 people invited by link, and Claude's projects can be shared on Team and Enterprise plans. Use them for work with more than one owner, like a quarterly plan or an account review, and keep personal drafting personal.
The best shared spaces sit where the team already works. Most collaboration happens in chat apps, docs, and ticket queues, so a teammate should be able to bring AI into a thread instead of copying the thread into AI.
What does multiplayer AI look like in practice?
The clearest example is a handoff. A support agent stuck on a ticket that needs an engineer shouldn't have to summarize the whole thread in a message. The engineer should be able to open the same session, see what's been tried, and finish it with them.
The same pattern works across teams: a finance lead and an analyst on the same forecast, or a sales rep and a solutions engineer preparing the same account review. The rule of thumb is to share the session, not a summary, and to say exactly what you need from the other person.
How do shared workflows make the whole team better at AI?
A shared workflow captures a process someone already got right: the steps that turn call notes into an account plan, or the checklist that catches errors before a board deck goes out. The next person doesn’t rebuild from memory. They start from the best version and improve it.
Shared projects give those workflows a home alongside the files, instructions, and conversations that explain how the team works. Instead of passing around a prompt and explaining the context again, teammates can return to the same project, reuse the process, and refine it together.
That’s where multiplayer AI compounds. A shared chat helps the people in it. A shared workflow, backed by shared context, helps everyone who comes after.
What gets shared in multiplayer AI, and what stays private?
Only what people choose to share. Multiplayer works when sharing is deliberate, because people need room to draft, test, and ask rough questions without an audience. Personal chats stay private unless someone shares them. Projects and workflows are shared with the team that owns the work. Connected tools follow the access people already have, so audit overshared files first. And every shared workflow has an owner who keeps it current.
Those defaults are also the safer setup. When everyone uses their own AI tools, company data ends up in accounts no one at the company can see. A shared workspace with clear defaults gives the company visibility into where its work, and the data in it, lives.
Not every AI task needs to be multiplayer. Drafting an email or testing a rough idea works fine solo. What matters is whether the work worth reusing gets shared. The signals to watch are how many workflows get used by someone other than the person who built them, how often sessions have more than one person in them, and how often a wrong answer gets fixed in shared context instead of in one person's chat. When those go up, one person's work is becoming the team's head start.
How we made AI multiplayer at Ramp
We saw the single-player problem at Ramp: the best AI work kept getting stuck with whoever did it. So we built Glass, the AI workspace where knowledge compounds. Teams work from the company's knowledge, share what works as reusable workflows, and solve problems together in the same place.
The goal isn't to make everyone as good as the company's best AI user. It's to give everyone a level of expertise they couldn't have reached on their own.
Here's what makes it multiplayer:
- Multiplayer: bring a teammate into a session, so a support agent and an engineer can finish a ticket together
- Shared workflows and projects: keep proven processes alongside the context your team uses, so one person’s best work becomes everyone’s starting point.
- Your company's knowledge: connect the tools your team already uses
- Any model: choose from multiple providers, so the whole team gets a better model the day it ships
The best workflow on your team is probably sitting in someone's chat history right now. You can now request early access to Glass and turn it into everyone's head start.
FAQs
Multiplayer AI is a way of working where several people and an AI assistant work in the same sessions and projects, from the same company context. Work one person does with AI stays visible and reusable as shared workflows, so teammates build on it instead of starting from scratch.
Yes. Several assistants support shared work, like group chats in ChatGPT and shared projects on Claude's Team and Enterprise plans. A workspace built for teams goes further, with shared company context and reusable workflows, so the work stays useful after the conversation ends.
Multi-agent AI is several AI agents coordinating on a task. Multiplayer AI is several people working with AI in the same place. A team can do either, both, or neither: the first is about how AI works, the second is about how people work with it.
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