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Adding AI to individual tasks can make people faster without helping teams make better decisions or carry work through to completion. Miro cofounder and CEO Andrey Khusid argues that companies should instead connect AI to the way teams share context, coordinate, and act. That is an executive’s strategic view, not evidence that adopting Miro—or any single platform—automatically transforms an organization.
What Khusid means by an AI-first operating model
In a July 2, 2026 interview with McKinsey, Khusid describes Miro’s evolution from an online whiteboard founded in 2011 to a shared canvas for product, design, and engineering work. He argues that AI makes it easier for individuals to create and execute more of a task end to end. That shifts the organizational challenge: teams still need to identify meaningful customer problems, choose which bets to make, and coordinate the work.
For Khusid, “AI-first” does not mean handing decisions to an automated system. It means moving from waiting for someone to ask a question to AI proactively surfacing useful signals, patterns, and possible actions. As he puts it, “Humans can focus on decisions and accountability, while AI provides continuous context and recommendations.”
The distinction is between automating a step and redesigning the flow around it. A tool that drafts a document may speed up one person’s work; an AI-enabled workflow could also help a team see relevant customer evidence, discuss options, and carry the rationale into execution. The latter depends on teams having access to shared context and clear responsibility for decisions.
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Three workflow changes Miro recommends for product teams
In an August 13, 2026 article, Miro’s Head of Enterprise Accelerate, Mathias Davidsen, argues that product development should be redesigned around AI rather than simply layered with AI features. His recommendations are Miro’s guidance, not independently tested prescriptions.
Make prioritization shared and evidence-based
Bring customer signals into a shared space where the people involved can inspect and discuss the evidence together. The point is not just to have AI summarize feedback; it is to make the basis for prioritization visible to the group deciding what to build.
Prototype to decide, not just to produce
Use quick prototypes as alignment tools before committing substantial time and resources. A prototype can help a team expose assumptions, compare approaches, and decide whether a direction addresses the customer problem. Its value is in supporting a decision, not merely generating another artifact.
Carry context across people and agents
Make decisions, diagrams, prototypes, and specifications accessible across handoffs. When teammates and AI agents can work from the same picture of the problem and prior choices, teams are less likely to lose important context as work moves from exploration to implementation.
How to start without turning AI adoption into a tool rollout
Miro’s guidance is to begin with a handful of high-value, low-risk use cases embedded in workflows teams already use. A practical starting sequence is:
- Choose a real workflow bottleneck. Look for a recurring point where teams lose time or context, such as reviewing customer evidence or aligning on a product direction.
- Define the human decision. Identify what people must still judge and who is accountable for the choice; then decide what information or recommendations AI could usefully surface.
- Make the relevant context shared. Bring the evidence, discussion, and resulting decisions into a place accessible to the people and agents involved in the next steps.
- Evaluate team outcomes. Assess whether decisions are better supported and alignment improves, rather than treating AI-session counts or individual speed as sufficient proof of success.
This approach calls for leadership to support shared context as ongoing infrastructure, not a one-time setup. It also requires teams to preserve accountability: AI can surface a recommendation, but people still need to assess it and own the resulting decision.
What the collaboration figures do—and do not—show
Miro’s August 2026 article reports that one in four respondents credited AI with enabling better collaboration. The figure comes from a 2025 survey of more than 2,000 product, engineering, and design professionals, as reported by Miro; it records respondents’ views and does not establish that AI caused better collaboration.
The same Miro article cites a Q3 2025 Forrester Consulting survey, “AI Workflows for Team Innovation,” commissioned by Miro: one in three leaders said their AI deployments were actively reinforcing silos. That is a reported survey response, not a causal finding that AI necessarily creates silos. Taken together, the figures illustrate why deployment choices and team practices matter, but they do not prove that a particular operating model will improve business results.
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Miro’s May 19, 2026 announcement describes an AI-readable and writable canvas for third-party agents, alongside expanded MCP support, connectors, Sidekicks, Flows, generated board content, and prototyping features. These are capabilities Miro announced about its own product. The announcement alone does not establish their availability for every customer or plan, how well they perform in a particular workflow, or whether using them produces organizational change.
McKinsey’s 2026 interview introduction describes Miro as having more than 100 million users and more than 250,000 company customers. Those are scale figures reported by McKinsey in its introduction, not an independently verified market census or evidence of customer outcomes.
Likewise, customer testimonials in Miro’s Intelligent Canvas article are statements from those customers, not independent outcome studies. The relevant test for any platform is whether it helps a particular organization connect context, decisions, and execution while keeping responsibility clear—not whether it offers AI features.
The practical test: improve the work, not just the output
Khusid’s argument is most useful as a design question for leaders: does AI help the team make and carry out a better-informed decision, or does it simply accelerate one isolated task? Start with a contained workflow, keep the decision and accountability with people, and judge the change by the quality of team decisions and alignment. A platform can support that redesign; it cannot substitute for it.
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