Miro’s MCP server has surpassed 16 million total calls since its February 2026 launch, with usage more than doubling since May and on track for 6 million calls in September alone. Engineers account for under a quarter of users; product, design, marketing, operations, and project management roles collectively outnumber them roughly two to one. The trajectory illustrates a broader principle: software platforms that serve as the place where work gets done can extend that position into the agentic era, provided they open access to multiple AI clients rather than locking into one. Miro’s support for 15 distinct AI clients is an early test of that thesis in a market projected to reach $181.3B in 2026 [2].
What Is Covered in This Article:
- Miro MCP server growth milestones and usage trajectory
- Cross-functional adoption beyond engineering roles
- Multi-client AI ecosystem expansion led by Claude and ChatGPT
- Enterprise demand for productivity and workflow automation [3][4]
- AI platforms market size and growth outlook [2]
The News: Miro reported that its Model Context Protocol server has exceeded 16 million calls since its public launch in February 2026. Usage more than doubled between May 2026 and publication, and September alone is on track to reach 6 million calls. Claude Desktop and Claude Code together drove approximately 65% of August usage, while ChatGPT grew 71% in MCP users between August and September, making it the second-largest connected client. Fifteen distinct AI clients now connect through the integration, including Microsoft Copilot, Gemini Enterprise, and Grok Build. Engineers represent under a quarter of users; product, design, marketing, operations, and project management roles outnumber them roughly two to one.
Miro MCP Hits 16M Calls: AI Collaboration Goes Cross-Functional
Analyst Take: The numbers matter less for what they say about Miro than for what they reveal about where enterprise AI workflows are headed. Software tools that already hold a team’s working context, including boards, documents, diagrams, and project plans, have a structural advantage in the agentic era. They are where the work already lives. The question is whether those tools open up or wall off access to AI agents. Miro’s decision to support 15 AI clients through MCP, rather than building a proprietary integration with a single model provider, bets on openness. That bet is paying off: usage doubled in four months, and the user base has expanded well past engineering into the functions where most enterprise work actually happens.
Work Surfaces Hold the Advantage, If They Stay Open
The user composition data is the most important signal in this release. Engineers and developers account for under a quarter of MCP users, while product, design, marketing, operations, and project management roles together outnumber them roughly two to one. That breakdown mirrors how enterprises actually measure AI success: 55.1% of organizations in Futurum Group’s 1H 2026 AI Platforms Decision Maker Survey (n=820) cite productivity improvements as a primary AI success metric [4], and 51.1% rank operations and workflow orchestration among their top GenAI use cases [3]. These are not developer-centric goals. They belong to the people who manage projects, ship campaigns, and run supply chains. The software platforms those people already use daily have a built-in distribution advantage for agentic AI, but only if agents from multiple providers can read and write to them. Miro’s MCP architecture does exactly that: 57% of August calls were Miro-to-code, meaning an agent reads a board and acts on it, while 41% were code-to-Miro, where agents generate boards, diagrams, and layouts. The canvas becomes a bidirectional interface between human teams and AI agents, regardless of which model powers the agent.
Multi-Client Access Is the Moat, Not the Model
Miro’s integration with 15 AI clients makes a practical point about platform strategy: the value accrues to the work surface, not to any single AI provider. Claude Desktop and Claude Code led with roughly 65% of August usage, but ChatGPT’s 71% user growth between August and September shows how quickly the client mix shifts. For enterprise buyers, this matters directly. Integration complexity is the third-largest agentic AI concern at 15.6% of organizations (n=820), behind security and privacy (24.1%) and loss of human control (16.3%) [5]. A platform that supports many AI clients out of the box reduces that integration burden. It also reduces switching cost: if a team moves from Claude to ChatGPT for a particular task type, the board, the project data, and the workflow history stay put. The work surface persists. This is the structural argument for why established software vendors, not model providers, will remain the central places where cross-functional work gets done. The model is a capability layer. The workspace is the system of record.
The Market Is Big Enough to Reward This Bet
Futurum Group’s AI Platforms market forecast projects the market at $109.9B in 2025, growing to $181.3B in 2026 under the base scenario (65% YoY) and reaching $496.9B by 2030 [2]. That growth is not concentrated in model training or inference infrastructure alone. It extends into the application layer where agents interact with existing enterprise tools. Among organizations actively planning agentic AI deployments (n=766), the top areas are IT operations and cybersecurity (49.2%), customer experience and support (48.6%), and R&D/engineering (39.6%) [5]. Product, marketing, and operations roles, precisely the cohorts showing up in Miro’s usage data, sit downstream of those deployment priorities. As agentic AI moves from pilot to production (22.1% of organizations are piloting, 19.1% have deployed single-agent systems, and 15.4% are orchestrating multi-agent frameworks) [5], the software platforms that serve as shared workspaces across these functions stand to capture outsized engagement. The condition is straightforward: stay model-agnostic, support open protocols like MCP, and let any agent read and write to the work surface.
What to Watch:
- Non-engineering retention: whether product, design, marketing, and operations cohorts sustain or accelerate MCP usage into Q4 2026, which would confirm that work-surface platforms hold cross-functional advantage
- Client ecosystem dynamics: whether the 15-client roster grows in Q4 2026, and whether ChatGPT’s rapid rise reshuffles the usage mix or stabilizes alongside Claude
- Reliability at scale: 55.4% of organizations cite reliability and hallucination management as a top GenAI adoption challenge [3]. How Miro handles agent errors on shared boards will shape enterprise trust in canvas-based agentic workflows
- Competitive response: whether Figma, Notion, or Microsoft Whiteboard ship comparable MCP integrations in Q4 2026 or Q1 2027, testing whether multi-client openness becomes table stakes for collaboration tools
- Protocol standardization: whether MCP adoption across work-surface vendors accelerates enough to establish it as the default integration standard, reinforcing the thesis that the workspace, not the model, is the durable control point
See the complete details about the announcement on Miro’s website.
Sources
- Momentum grows for Miro MCP as users embrace the Canvas for AI collaboration, Miro
- AI Platforms Market Forecast
- AI Platforms DM: GenAI Usage (1H2026)
- AI Platforms DM: GenAI Impact (1H2026 vs 2H2025)
- AI Platforms DM: Agentic AI (1H2026 vs 2H2025)
- AWS and the End of the Naive Agent: Collapsing the Semantic Divide
- Agentic Agents and MCP Signal Shift to Development Work
- Futurum Signal Report | ERP Platforms
- Miro MCP Server Announcement (September 2026)
Declaration of generative AI and AI-assisted technologies in the writing process: This content has been generated with the support of artificial intelligence technologies. Due to the fast pace of content creation and the continuous evolution of data and information, The Futurum Group and its analysts strive to ensure the accuracy and factual integrity of the information presented. However, the opinions and interpretations expressed in this content reflect those of the individual author/analyst. The Futurum Group makes no guarantees regarding the completeness, accuracy, or reliability of any information contained herein. Readers are encouraged to verify facts independently and consult relevant sources for further clarification.
Disclosure: Futurum is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
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Author Information
Keith Kirkpatrick is VP & Research Director, Enterprise Software & Digital Workflows for The Futurum Group. Keith has over 25 years of experience in research, marketing, and consulting-based fields.
He has authored in-depth reports and market forecast studies covering artificial intelligence, biometrics, data analytics, robotics, high performance computing, and quantum computing, with a specific focus on the use of these technologies within large enterprise organizations and SMBs. He has also established strong working relationships with the international technology vendor community and is a frequent speaker at industry conferences and events.
In his career as a financial and technology journalist he has written for national and trade publications, including BusinessWeek, CNBC.com, Investment Dealers’ Digest, The Red Herring, The Communications of the ACM, and Mobile Computing & Communications, among others.
He is a member of the Association of Independent Information Professionals (AIIP).
Keith holds dual Bachelor of Arts degrees in Magazine Journalism and Sociology from Syracuse University.
