How to Run AI Design Workflows in Miro (Flows, Sidekick + Claude MCP)

Overview

Every design team knows this feeling. You run a great workshop, the energy is high, you've brainstormed and collected all these insights — and the next day comes and none of that progress has been synthesized. The session was collaborative, but everything that came after it was just you trying to make sense of the mess alone.

AI was supposed to fix this. But here's the problem with nearly every AI tool: it's single-player. You open a blank prompt completely disconnected from the work your team just did, re-explain everything from scratch, and get an output you have to wrangle back into context. The more my team leaned on AI, the more our outputs scattered across five different tools. The mess got faster, not smaller.

For the past few weeks I've been running my design process inside Miro, where the AI works on the same canvas my team already uses. Miro sponsored this video, but I changed my actual workflow because of it.

The four pieces

Miro's AI system has four components worth understanding: Flows, Prototypes, Sidekick, and the MCP server. Flows are the heart of it, so that's where I'll start.

Flows: structure the mess without leaving the canvas

After five user interviews, a Miro board looks like a wall of sticky notes. Normally turning that into something a PM can act on takes me about two hours of clustering and rewriting. I built a Flow instead.

Flows are visual, multi-step AI workflows that run directly on the canvas. Mine does three things in sequence: clusters the raw notes into themes, pulls the top pain points with supporting quotes, and formats everything into a structured, prioritized table. Run it, and in a few seconds that wall of notes becomes three organized, readable views of the research — all still on the same board.

The key word is sequential. Each step builds on the last, and you can see exactly what happened at every stage. This isn't magic behind a loading spinner — the logic is visible and steerable.

Sidekick: collaborative, not just fast

After a Flow runs, I add a Sidekick — contextual AI expertise dropped in at exactly the right moment. I dropped a research-led Sidekick at the clustering step to pressure-test the themes.

Here's what makes this collaborative instead of just efficient: my teammate can jump onto the same canvas, see exactly what the AI did, steer it, and verify the output before anything moves forward. We're all looking at the same logic in the open at the same time. That's the upgrade I actually care about — going from collaborating on a session to executing on it together.

Single-player AI gives you a faster solo output. A Sidekick on a shared canvas gives you a faster team output. The difference matters.

Prototypes: from insight to interactive without leaving the board

Old workflow: export everything, open Figma, start mocking screens before anyone agreed on a direction. Building got easier but knowing what to build didn't get clearer.

With Miro Prototypes, I stay on the canvas. I select the insight I want to act on — a research cluster, a rough PRD, a brainstorm node — prompt the Sidekick, and it builds a working interactive prototype right there. Not a static image. Something you can actually tap through and test.

The AI sees the context around whatever I selected from the rest of the board, so the prototype isn't generated cold. It understood the problem we were solving.

This changes the stakeholder conversation. They react to something tangible in the same place the discussion lives. I generate three completely different directions in an hour, the team picks one, and nobody burned craft time on the other two. The old trap was spending a week in Figma polishing one direction, showing it, and being told to start over. Now we align on direction cheaply, then invest in execution.

The MCP server: Claude reads the whole board

This is the piece I'm most excited about. Miro has an MCP server, which means an AI agent — Claude, in my case — can read and write directly to your Miro board.

So when I ask Claude to help me draft a spec, it can read the entire board: the debates, the discarded directions, the tradeoffs we talked through, the research clusters, the prototype we landed on, and why we rejected the alternatives. It finally has the reasoning behind the work.

I've stopped re-explaining backstory to my AI every single time. I don't open Claude with 'okay, we interviewed five users and found this pain point, here's the quote, oh and we considered this approach but rejected it because...' It already knows. It just reads the board.

For engineers: connect your agent this way and it understands the thinking that shaped the work — sprint decisions, scoping tradeoffs, architecture notes. You're not asking Cursor to code in a vacuum. It's reading your Miro board with the full picture. For PMs: connect Claude to pressure-test your roadmap against the actual user research. For anyone running retrospectives: feed the board to an agent and turn the messy output into action items with full context intact.

The board becomes the shared source of truth for your whole team's AI stack, not just one more tool in the chaos.

The full loop

A messy session goes in. Flows structure it. Sidekicks keep the team in control. Prototypes get everyone aligned. The MCP server carries the full context off to whatever tool your team ships with.

The session still happens on the canvas. What changed is everything that comes after it. The collaboration doesn't stop when the session stops — it accelerates.

Most AI tools are built for individuals. The teams that ship the best work aren't the ones with the smartest prompts. They're the ones running collaborative AI workflows where the AI works on the same canvas the team is already on, where everyone can see the logic, steer the output, and build on what came before.

If you want to try it: miro.pxf.io

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