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What Khusid means by redesigning work around AI
In a July 2, 2026 interview with McKinsey, Khusid describes Miro’s evolution from an online whiteboard founded in 2011 into a shared canvas for product, design, and engineering work. His argument is that AI makes it easier for individuals to create and execute more end to end, which makes choosing the right customer problems and bets more important—not less.
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He defines an AI-first operating model as one where AI does more than wait for a prompt. In his words, “By AI-first, I mean making AI more proactive—moving from asking questions to AI proactively surfacing insights, patterns, and recommended actions.” The intended shift is from people repeatedly seeking information to relevant context and possible next steps arriving as work unfolds.
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That does not mean handing decisions to software. Khusid’s division of responsibility is: “Humans can focus on decisions and accountability, while AI provides continuous context and recommendations.” AI may help expose patterns or options; people still need to judge them, choose a course, and own the consequences.
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How product teams can change their workflows
Miro’s August 13, 2026 guidance translates the thesis into three changes to product development. They are recommendations from the company, not independently validated prescriptions.
Make prioritization shared and evidence-based
Bring customer signals into a shared space where the people responsible for product, design, and engineering can inspect and discuss the same evidence. The aim is to make prioritization a team decision grounded in what customers need, rather than a sequence of isolated interpretations.
Prototype to decide, not just to produce
Use quick prototypes to make options tangible and align the team before making expensive commitments. A prototype can help people test assumptions and discuss trade-offs; producing one is not itself evidence that a decision is sound.
Carry context across people and agents
Make decisions, diagrams, prototypes, and specifications available across handoffs so that teammates and AI agents can work from the same picture. Shared context is useful only when it stays accessible and current; a collection of disconnected artifacts does not automatically create alignment.
Why individual speed is an incomplete measure
If AI makes one person faster but leaves teammates without the reasoning, evidence, or decisions behind the work, local productivity may rise while coordination remains difficult. The more meaningful test is whether the organization makes better-informed decisions and carries them through execution—not how many AI sessions people log or how quickly one task is completed.
Khusid told McKinsey, “The way software is built is changing, and it’s becoming more feasible to go deeper on specific customer needs because you can deliver changes faster.” Faster delivery may create room to address narrower customer needs, but teams still have to decide which needs matter and coordinate the work that follows.
What the collaboration figures do—and do not—show
Miro’s August 2026 article cites two survey findings that illustrate why collaboration is part of the debate, but neither establishes that AI causes a particular business outcome.
| Finding | Source and qualification | What it can support |
|---|---|---|
| One in four respondents credited AI with enabling better collaboration. | Miro, 2025; survey of more than 2,000 product, engineering, and design professionals, as described by Miro. | Some surveyed professionals perceived a collaboration benefit; it is not a causal measure of AI’s effect. |
| One in three leaders said their AI deployments were actively reinforcing silos. | Forrester Consulting, Q3 2025, in “AI Workflows for Team Innovation,” a survey commissioned by Miro and cited in Miro’s article. | Some surveyed leaders reported silo reinforcement; the finding does not establish that AI universally worsens collaboration. |
McKinsey’s interview introduction describes Miro as having more than 100 million users and more than 250,000 company customers. Those are scale figures reported by the publisher, not an independently audited census or evidence that customers achieved organizational change.
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Miro recommends starting with a handful of high-value, low-risk use cases in workflows teams already run, then assessing decision quality and alignment. A company can make that concrete without treating a new feature rollout as transformation:
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- Choose a workflow and a problem. Identify a recurring coordination or decision bottleneck, such as gathering customer signals for prioritization. Keep the first use case narrow enough to evaluate.
- Set human ownership. Decide who reviews AI-surfaced information, who makes the decision, and who is accountable for the outcome.
- Make shared context part of the process. Agree where evidence, decisions, prototypes, and specifications live, and how the team will keep them usable across handoffs.
- Evaluate team outcomes. Look at whether the evidence informed decisions and whether people stayed aligned as work moved forward. Do not substitute AI usage counts or individual speed for those outcomes.
- Expand selectively. Keep, adapt, or stop the use case based on what the team learns, then consider whether the approach applies to another workflow.
What a platform can—and cannot—change
Miro’s May 19, 2026 announcement describes a canvas designed to be read and written to by third-party agents, alongside expanded MCP support, connectors, Sidekicks, Flows, generated board content, and prototyping features. These are capabilities Miro announced; the announcement alone does not establish their availability for every plan or region, their practical performance, or the results a customer will get.
The product example makes Khusid’s distinction concrete: a shared workspace may help teams and agents access common context, but it cannot by itself decide which customer problem to solve, define who is accountable, or make teams use the same evidence. Those changes require choices about workflow and leadership as well as software.
For organizations seeking product-team training, Miro’s acquisition announcement says Reforge will remain a separate entity, with vendor-neutral courseware and continued investment in courses including AI prototyping, evaluations, and strategy. That is a description of the announced plan, not evidence of a particular training outcome.
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