Miro puts AI at the heart of teamwork with a new Innovation Workspace
Miro has expanded from digital whiteboard to a multi-product workspace built for end-to-end collaboration. The company introduced the AI Innovation Workspace and Miro for Product Acceleration-two moves that bring agentic AI into the canvas where teams already plan, build, and ship.
AI adoption is no longer a side project. Miro reports 63% of knowledge workers use AI regularly, especially in product, engineering, and design. Most tools still focus on solo tasks late in the cycle. Miro's approach shifts AI earlier and makes it multiplayer.
AI that lives where the work happens
"AI's biggest opportunity lies in teamwork and accelerating outcomes that teams are driving," said Andrey Khusid, Miro's Founder and CEO. The canvas becomes the prompt, so teams can collaborate with AI in context, not jump between apps or lose momentum.
Wayne Kurtzman, Research VP at IDC, echoed the team-first approach: as work becomes more visual and hands-on, AI should amplify collaboration-not try to replace it.
What's new: AI Innovation Workspace
The AI Innovation Workspace brings a set of capabilities that keep teams in flow while automating the dull parts of product work:
- Flows: Visual AI workflows that automate multi-step processes with full visibility and control. Useful for backlog triage, intake, and discovery-to-delivery handoffs.
- Sidekicks: Conversational AI agents specialized by task or role. Use pre-built agents or create your own for PRD drafting, research synthesis, QA planning, or release notes.
- Your AI & Knowledge: Connect your preferred models (OpenAI, Anthropic, Gemini on AWS, Azure, or GCP) and internal knowledge sources (e.g., Glean, Amazon Q, Gemini Enterprise) so teams can query trusted data without leaving the board.
- Model Context Protocol (MCP): Let agents read and write to Miro and other tools. This links the canvas to agentic coding environments like Cursor and GitHub Copilot, accelerating spec-to-code workflows. Learn more about MCP at modelcontextprotocol.io.
Miro for Product Acceleration: ship faster, with better decisions
This collection of AI-first products reframes the product development lifecycle for cross-functional teams:
- Connect strategy to execution (Miro Portfolios): Bring goals, initiatives, and resource plans into one shared view. Prioritize based on impact and capacity, then track delivery against outcomes.
- Build the right things (Miro Insights & Prototypes): Pull customer data into the canvas, get AI suggestions on what to build next, and spin up visual prototypes in minutes to cut the cost of experimentation.
- Make AI codegen pay off (Miro Specs): Convert stories, user flows, prototypes, and architecture into clean technical specs that AI coding tools can consume via Miro's MCP server-resulting in faster, higher-quality code.
Why this matters for product leaders
Most teams use AI to speed up individual tasks. The bigger win is using AI to align strategy, compress feedback loops, and keep delivery moving without context loss. Putting agentic AI on the canvas lets PMs, designers, and engineers work with the same source of truth in real time.
This reduces the overhead between discovery and delivery: insights flow into concepts, concepts become specs, and specs feed codegen-without reformatting or copy-paste. The output: fewer handoff gaps, faster decisions, and clearer accountability.
How to pilot it in your org
- Start with one high-friction loop: discovery synthesis, PRD-to-spec, or release planning. Map it as a Flow and add Sidekicks for each role's tasks.
- Wire up Your AI & Knowledge with approved models and sources. Set guardrails (PII, data residency) before scaling.
- Connect MCP to your dev stack so Miro Specs feed AI coding tools directly. Measure lead time from spec to first PR.
- Run weekly reviews in Miro Portfolios to keep roadmap, capacity, and delivery aligned to outcomes.
The takeaway
Product velocity isn't just about what an individual can automate. It's about how the team plans, decides, and ships together-with AI embedded in the workflow, not bolted on. Miro's team-centric approach gives product orgs a practical path to that future.
If you're upskilling your product org on AI workflows, see curated options by role at Complete AI Training.
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