Jeff Chow on Team Intelligence: Thinking and Moving Together
AI is giving individuals something that organizations have always wanted: more speed, more capability, and more ways to solve problems. But there is a catch: Are those tools actually creating greater business value?
Answering that question requires more than deploying AI tools. It calls for a rethink of how teams collaborate, how decisions are made, where accountability sits, and even what leadership looks like as AI takes on more of the execution.
Jeff Chow, Miro’s Chief Product and Technology Officer, believes the solution lies in building team intelligence – turning individual AI capabilities into collective progress.
Through this conversation, let’s explore what that means in practice, how organizations can encourage experimentation without losing alignment, where human judgment remains essential, and why AI success should ultimately be measured by what the business achieves – not simply how much faster its people work.
From AI adoption to organizational transformation
AI can accelerate execution, but speed alone does not create business value. Chow explains why leaders must focus on what teams are solving, not simply on how quickly they deliver.
AI has shifted the conversation from technology adoption to organizational transformation. How has your own leadership philosophy evolved as AI becomes embedded across every function of the business?
Chow: Although AI is certainly making waves across all areas of business, in general, my leadership philosophy and expectations of leaders have remained grounded in three critical pillars:
- Creative problem-solving
- Empathetic communication
- Pace setting
Those pillars haven’t changed, but what each demands in practice has.
Although AI makes it easier to move quickly, ensuring that speed leads to impact has become a bigger challenge. When the ability to deliver anything rises, leaders have to make sure teams are delivering the right thing, not just delivering quickly.
Hand in hand with this is communication. Leaders need to clearly articulate the biggest problems, break them into solvable chunks, and then empower and trust their teams to solve them. This drives teams to execute on delivery rather than just speed.
And most importantly, as market dynamics remain fluid, strategic shifts are no longer just quarterly or yearly. Leaders need to set the pace by being prepared to make clear decisions and cascade them quickly across the organization.
Many organizations are racing to deploy AI tools. In your experience, what leadership decisions have the greatest impact on whether AI becomes a competitive advantage? Or simply another technology initiative?
Chow: Adoption isn’t simply plugging AI anywhere into a company and claiming you’re an AI company. True AI transformation is foundationally a culture shift. Its not a technological shift. This means leaders invest in a strategy that clearly defines what the new workflow actually looks like, who owns what, and where they are willing to disrupt the structures built around legacy ways of working. Otherwise, you’ve invested in a tool, not true transformation.
Other intentional leadership decisions can also drive cultural shifts. These include encouraging grassroots and cross-functional adoption, fostering experimentation and collaboration, and identifying what works so it can be scaled across the organization.
Building Team Intelligence, Not Just Individual Productivity
AI can give individuals new capabilities, but those capabilities can lose their value at organizational handoffs. Chow explains why the next advantage comes from helping teams think and act together.
Miro focused less on AI tools themselves and more on strengthening how people think and make decisions together. What led you to prioritize collaboration over technology deployment?
Chow: Working at Miro is very “meta,” as the things we need to change ourselves are often what our customers use Miro for to change as well. In talking with our customers and our own experiences with AI transformation, it was very clear the bottleneck to business impact was not for a lack of giving individuals superpowers. Instead, it was harnessing the individual superpowers toward common, high-impact business goals. To do so, the biggest impediment is getting teams together to make decisions that unblock larger, more ambitious projects at the speed of AI.
For the past three years, companies have exhausted budgets on AI tools that make individuals faster. But speed at the individual level hasn’t translated into speed at the company level.
Our research found that 76% of leaders say most AI tools are designed for individuals, not teams. It implies AI lives in one tab while collaboration lives in another.
At the same time, 63% of knowledge workers say the data and insights they need are scattered across internal and external sources. And this forces constant context switching, undermining AI strategy. Individuals accelerate in their own tools, then hit a wall the moment their work has to meet someone else’s. Everyone moves faster, but the company still moves at the same pace it did a year ago. Because the value AI creates is leaking out at the handoffs between people.
This is a fundamental collaboration problem with organizations stuck on some key questions. How can teams rapidly gain critical context and work through human team dynamics to make quick decisions? And how do those decisions then cascade to teams and agents?
The faster this level of collaboration can happen, the faster organizations can reach alignment and business impact.
You describe developing “team intelligence.” How do you define that concept, and what practical changes were required to build it across the organization?
Chow: Team intelligence starts with the belief that achieving maximum business impact is a team effort. And that everyone should be empowered to do their best work together.
To do so and to move at the speed of AI, everyone working on a problem must have the full context to do their best work. This means you’re not siloing knowledge based on role or any stage of a project. But you are giving people visibility into the broader work and empowering them to contribute throughout the process. This challenges legacy processes, such as waterfall, where work often moves through a series of handoffs. Instead, it promotes greater transparency so teams have the context and empowerment they need to contribute effectively.
Leading cultural change in the AI era
Giving employees access to AI is only the starting point. The harder task is creating the trust, transparency, and new collaborative behaviors needed to make those tools useful at scale.
Giving every employee access to AI is relatively straightforward. Changing how an organization collaborates is significantly harder. What were the biggest cultural barriers you encountered, and how did leadership address them?
Chow: The fundamental starting place is giving everyone access to AI. So that you can see where the sparks of creativity and adoption happen. Grassroots experimentation is an important part of cultural change because it feels less like a mandate.
At Miro, we gave Claude Code access across our EPD teams, which led to some truly viral moments as people shared their new discoveries and process hacks.
But those new individual superpowers can also create new collaboration challenges as roles begin to blur. A prototype built by a PM can sometimes create a false sense of certainty. That’s why PMs and designers need to develop new ways of collaborating, regardless of who creates the prototype. Engineers must feel comfortable with designers checking in code, even when there are downstream questions like who is on-call to support in production.
What’s worked for us is focusing on convergent moments and entering these phases with a collaborative mindset. The framing shifts from critique to co-creation. “I’m here to launch the best product” rather than “I’m going to review your work.”
How do you encourage experimentation with AI while maintaining alignment around shared goals, governance, and accountability?
Chow: At Miro, we have focused on fostering a culture of experimentation and transparency in our exploration of AI. It gave teams a safe space to try and gather learnings. We found that key steps to following a similar path includes:
- Open Dialogue: Initiating and welcoming conversations on the role and opportunities for AI in an organization, while fielding ideas and concerns from teams.
- Reflection and Iteration: Having structure around experimentation, followed by retrospection. It can ensure learnings are pulled out of what teams try to do, rather than sticking with an individual.
- AI Committees: Leadership roles and cross-functional teams responsible for driving AI experimentation can help encourage workers through their AI journey, while remaining informed on tooling and regulatory updates.
- Incentives: Establishing reward systems can help not only encourage, but celebrate the sharing of AI insights. This can look like hackathons, organizational recognition, etc.
- Transparency: Trust in AI strategies across an organization quells hesitancy to use or be innovative with AI, particularly if a leader can showcase their own successes and failures.
- Ethics and Regulations: Remaining informed will ensure that any practices with AI remain ethical and compliant, again securing trust in teams to use the tools.
Our overall process still has key points of convergence—such as solution reviews, pre-release checks, and launches—that remain relatively consistent. This keeps governance and review processes intact while allowing teams to experiment and adapt between these stages.
The net result is a scaffold that stays consistent, an openness for process disruption through grassroots experimentation, and a leadership accountable for scaling out processes and communicating those changes at pace. This includes discussing both failures and successes, sharing knowledge through the organization, and ensuring insights aren’t siloed.
During periods of rapid technological disruption, how do leaders maintain trust and confidence among employees while introducing entirely new ways of working?
Chow: My biggest belief is that leaders need to own any changes and communicate it themselves. Too often, shifts in the ways of working are communicated through others, like operations or program managers. But since AI demands a cultural change, the accountability must clearly be at the feet of the CPO, CTO, and other functional leaders.
At Miro, we are also transparent about where new processes come from. They often start with an early experiment or pilot within a team. Being open about those origins and celebrating successes from across the organization helps build trust and creates greater cohesion around adoption.
Keeping humans at the center of AI-powered work
As AI takes on more of the execution, human judgment becomes even more important. Chow explores where leaders should draw the lines around strategy, accountability, and autonomy.
What principles should technology leaders establish to ensure AI augments human judgment instead of replacing critical thinking?
Chow: The most critical aspect that is not replaceable by AI is connecting strategy to execution.
Leaders and their teams must always articulate the “why” of an initiative and connect that to the “what“ and “how.” AI can augment this and create great artifacts (documents, diagrams, various outputs). But teams and leaders need to remain accountable in articulating how it all connects.
At Miro we have a simple phrase we use, “over beers,” which means “explain this to me in simple terms as if we’re chatting over beers.” It’s our stress test for whether strategy is genuinely understood. If the human scaffolding (the why, what, how) is weak, AI output is then just “AI slop.”
As AI agents become increasingly autonomous, how should CTOs rethink ownership, accountability, and decision-making across teams?
Chow: Accountability is one of the most critical elements in creating a truly new agentic behavior. Defining who is the true decision-maker drives the right design and workflows around software factories.
This leads to some larger shifts. Engineering managers shift from people and process management to strategic decision-making. Leaders can now set the pace and approve key decisions, allowing the “factory” to move as fast as autonomy allows. Additionally, governance also becomes more explicit, as leaders control how much autonomy is in a workflow and where human approvals are needed.
Measuring AI by business impact, not activity
The easiest AI metrics to track are often the least meaningful. Chow argues that organizations need to work backward from business outcomes rather than counting how much AI-generated activity takes place.
Productivity gains are often the first metric organizations highlight. What indicators do you believe better reflect whether AI is genuinely improving collaboration and decision quality?
Chow: As a technical deployment (and an expensive one at that!), it’s easy to want to find that one metric that proves ROI. Measures like the number of pull requests or lines of code written are easy to produce, but they only measure activity and not outcomes. The problem is that they leave little connection to company success, which limits AI’s value.
The promise of AI is that faster execution means more ambitious bets, which should lead to bigger business outcomes. Measuring AI ROI has to start by considering the end outcome, align to company business priorities and work backwards from there. That connection is harder to isolate, but showing how shifts in ways of working contribute to larger-scale business impact is ultimately a much more meaningful way to measure value.
What CTOs need to change now?
For technology leaders, AI transformation is becoming less about deploying technology and more about redesigning how the organization operates. Chow outlines what that shift demands from leadership.
Many organizations are still treating AI adoption primarily as a technology rollout. Based on your experience, what mindset shift should CTOs make before scaling AI across the enterprise?
Chow: AI disruption is the biggest ways-of-working shift since the move from waterfall to agile.
How we collaborate has fractured: Human-to-human, human-to-agent, and agent-to-agent work now happens in entirely separate places, without a shared surface to converge. At the same time, what we collaborate on has inverted, as AI has flipped work from being 80% doing and 20% thinking to the other way around. Most organizations, however, are not structured for a world where judgment, alignment, and direction-setting are the majority of the job.
CTOs need to embrace that shift and treat it as a change management imperative, not a tech rollout. Building team intelligence is part of that shift, because organizations need to help people collaborate effectively across human and AI-driven workflows. This includes upskilling every knowledge worker, not just the most technically skilled; looking beyond engineering to understand how end-to-end processes across product, design, marketing and other functions need to change; and involving employees in that transformation.
Leaders also have to practice what they preach and act as role models in an AI strategy, embracing the tools and being ready to guide their teams. Additionally, showing traits like curiosity and empathy can increase trust amongst teams and relieve hesitancies towards personal AI adoption.
Looking ahead, what leadership capabilities do you believe will distinguish organizations that successfully navigate AI disruption from those that struggle to adapt?
Chow: One observation I’m seeing, both at Miro and in conversations with customers, is that market dynamics are so fluid that strategy shifts are happening more often than before.
Traditional planning processes like OKRs are getting more antiquated, while more nimble strategic decisions have to happen ad hoc. One of the key capabilities leaders need today is decisive decision-making and ability to cascade those decisions quickly across the organization. Unfortunately, this is easier said than done, especially since traditional decisions and how they cascade through organization often take weeks, if not months.
How leaders can collapse that to days and make more frequent calls, while also keeping teams motivated, aligned, and nimble to those shifts is vital in navigating any disruption.
The leadership imperative: Connect AI to the “Why”
The technology may change rapidly, but the leadership principle remains remarkably simple: people need to understand what they are solving and why it matters.
If you could offer one piece of advice to technology leaders preparing their organizations for the next wave of AI innovation, what would it be and why?
Chow: One key piece of advice I offer leaders is to ground everyone in customer understanding and business impact drivers. When teams understand the “why,” innovation replaces mere execution. With tech, this is the gap between the good and the great leaders. Those who can connect the dots between business and customer impact will deliver outsized value for the organization and their teams.
Key takeaway
Chow’s perspective makes one thing clear: AI transformation is not just about giving people smarter tools; it is about changing how people work together. That is where team intelligence becomes critical – the ability to turn individual expertise, AI capabilities, and collective judgment into coordinated action.
As AI enters the workforce, leaders will need to focus even more on creating clarity around the “why,” strengthening collaboration, and keeping teams aligned to customer and business outcomes.
The takeaway is clear: the organizations that create lasting value from AI will be those that can think, decide, and move together.
