Claude Cowork versus Copilot Cowork

Chances are you already have Microsoft Copilot. For many companies it simply came with their Microsoft licenses, but it's often also a deliberate choice: you stay in the familiar Microsoft ecosystem, where you feel your data is safe. That's exactly why it matters that something has changed under the hood, even if you haven't noticed it yet.

What's changed?

Two things, and they're connected:

Microsoft 365 Copilot Model
Choose the model for this task
OpenAI GPT
Claude (Anthropic)
Copilot Cowork, an agent that does multi-step work in the background

In parts of Copilot you now pick the model yourself, and an agent can get to work on its own.

Claude Cowork or Copilot Cowork?

This is where confusion creeps in, because the same name is used for two different things:

So you're really comparing Anthropic's own agent with Microsoft's agent that can also run Claude. The differences side by side:

 Claude CoworkCopilot Cowork
Whose / whereAnthropic, in the Claude appMicrosoft, in Microsoft 365 (Teams, Outlook, …)
Default contextYour local files + what you connect (e.g. Office 365 mail)Your Microsoft environment (mail, calendar, SharePoint, org search), out of the box
Runs where?On your device (desktop app); for long-running work your computer must be onIn the Microsoft cloud; keeps running in the background, even when your PC is off
ModelAlways Claude, newest capabilities firstModel choice (GPT in Azure, Claude); "Auto" sometimes picks a lighter model
DataAnthropic processes everythingMicrosoft is the boundary; GPT stays in Azure, Claude goes to Anthropic
ManagementVia Anthropic, separate from your Microsoft tenantCentral in the M365 admin center (identity, DLP, audit)
CostFlat price per user (Max plan)M365 Copilot license + usage-based billing
Best forPower users, local work, not Microsoft-boundMicrosoft companies rolling it out broadly and governably

Same model, different result

In Copilot you can explicitly choose Claude Opus 4.8, the same top model that Claude Cowork uses. Yet they don't perform identically, and that comes down to a distinction most people don't make: the model versus the shell.

An AI agent is the model (the brain) plus a shell around it: how it plans, which tools it has, how it handles your files and memory, and which limits it respects. That shell makes a big difference:

In other words: the same AI can perform differently depending on the packaging. The brain can be the same, the way it's deployed isn't. Anyone who only looks at the model name misses half the story.

An example makes it concrete. Say you ask: "summarize this week's quotes and send them to my team." Same Claude, but depending on the shell you get something different:

The difference isn't in Claude itself, but in the shell around it: what it can see, which actions it can take, and how much of its power you get. So the question isn't "which model is better", but "which shell fits where my work and my data live".

What to watch out for

And your own systems?

Good news: Cowork can also reach your own systems, not just your Office files, via MCP. That's an open standard for connecting AI to your software, a bit like a USB port: one socket that fits many things. It lets an AI safely and predictably retrieve data from or perform tasks in another program.

But that socket isn't the installation. Someone still has to build and maintain that bridge: making the connection to your ERP, accounting or industry software (including systems without an API), and making sure the work keeps running reliably without a human in the loop. That's exactly where an assistant stops and a real process begins. And if you want it to run while your PC is off and connected to your own systems, you need a server-side setup rather than a desktop assistant, and that's precisely what we build.

Our honest take

The honest summary: Cowork does your office work, we keep your business running. Whether you pick Copilot Cowork or Claude Cowork, they remain assistants mostly within the world of your documents and mail. And the tool is rarely the problem: most AI projects never get past the test phase, not because of the technology but because of the approach. So the real win is in the choices around it, and that's where we come in:

Curious what this means concretely for your (Microsoft) environment? Book an introduction or see which use case is closest to your situation. We listen first, then we build together.