At certain points, teamspace can call on an AI to help: condensing a long ticket, drawing the key insights out of a report. These features are built in. You don’t need to connect anything, install anything or learn anything – an administrator connects the AI and grants you the features via rights, and after that they are simply there, right where you need them.
Two ways to use AI with teamspace
In everyday talk, both are called “AI in teamspace”, but they mean different things. The distinction is worth making, because it determines who you need to ask when something is missing:
| AI features (this topic) | MCP | |
|---|---|---|
| What happens? | teamspace uses an AI internally | Your assistant accesses teamspace from outside |
| Where do you work? | In teamspace itself | In Claude, ChatGPT, Cursor … |
| What do you do for it? | Nothing – the feature is there | Connect once and authorise via OAuth |
| Who sets it up? | An admin connects an AI and grants each feature’s right in the user groups | An admin defines the access, you connect your client |
| Example | Condense a long ticket history with one click | ”Show me all open bug tickets by status” |
So if you connect your ChatGPT account to teamspace via MCP, that is not using AI in teamspace – it is your assistant using teamspace as a tool. Conversely, condensing a ticket needs no client and no MCP: it runs in teamspace, with the AI your company has connected.
The two don’t rule each other out. How the other way works is described in Introduction to MCP.
What works today
| Feature | Where | What it does |
|---|---|---|
| Condense a ticket history | In the ticket | Condenses a long history into the matter of the ticket. |
| Matter via magic wand | In the ticket, in the “Matter of the ticket” field | Creates the complete matter from the ticket data, even if the field is empty. |
| Service description via magic wand | In the “Project time” dialog | Writes the description of a project time from the ticket it is booked on, as a daily or overall summary. |
| Analyse a report | In lists and reports, in the three-dots menu | Reads the report and names the notable points. |
More is listed in Available AI features – the list keeps growing.
Three decisions that explain everything
When it comes to AI features, teamspace is deliberately more restrained than others in several places. Once you know these three points, the rest follows on its own.
It is always your own AI. teamspace brings no AI provider along where your data ends up. Every tenant enters its own endpoint. That keeps the question “where does our data go?” one that your company answers – not us. Why this makes all the difference for data protection is explained in Your AI, your data.
The AI supports, it doesn’t decide. You won’t find a feature in teamspace that writes an invoice on its own or closes off an operation. AI suggests, summarises, points things out – the decision stays with you. This isn’t caution for the time being, it is a design decision: Why teamspace relies on rules for AI.
Where it has to be exact, rules do the calculating. Invoices, billing, figures: for these, teamspace has a rule-based system that delivers 100% correct results – not 99.9%. There, AI is used at most as a reviewer, never as a calculator.
What you have to do
As a user: nothing. If your user group has the right for a feature, it appears in its place – in the ticket, in the report. If it isn’t there, you lack the right or no AI is connected.
As an administrator: connect the AI once, then grant the rights for the individual features in the user groups. This lets you decide for each user who may use which feature. Because new features keep arriving, there is also the right “Always allow new AI features”. See Connect an AI.
Availability: The AI features are included in all editions. From September 2026 they are enabled for all tenants; prospective and beta customers can test them on request already. More features will be added in the meantime.
Related topics
- Why teamspace relies on rules for AI AI features Concept
- Your AI, your data AI features Concept
- Available AI features AI features Reference
- Introduction to MCP MCP & AI agents Introduction
- Integrations & open REST API