Grab your badge, meet peers, and explore hands-on AI demos over breakfast.

Everyone is building faster with AI. But the organizations pulling ahead aren't the ones with the most tools — they're the ones that have created the foundation for teams and AI to make decisions, solve hard problems, and build the right thing together. In this keynote, get a first look at Miro's latest product announcements and leave knowing exactly how to bring them back to your team.

Key takeaways:

• Miro is the missing collaborative layer in your AI stack. Walk away with a clear picture of where it fits — and how it helps teams build together.

• Be among the first to see Miro's newest agentic products in action, and discover how teams are already putting them to work.

• From AI experimentation to org-wide transformation. Bring back concrete next steps for making Miro the bridge that closes that gap.

Most organizations have run AI pilots. Far fewer have figured out how to make AI work the way their teams actually work — at scale, without sacrificing governance or traceability.

That's exactly the challenge Endava set out to solve — and, in this keynote, they share what the journey has taught them so far. With Dava.Flow™, their AI-native engagement lifecycle that uses Miro as its shared context engine, they're rebuilding delivery around human-agent collaboration. Get an inside look at Dava.Flow: the principles that shaped it, the decisions they would (and would not) make again, and the human side of change at the heart of their transformation.

Key takeaways:

• The bottleneck in AI-native delivery isn't output — it's decision-making. Go behind-the-scenes to see how Endava solved the human-agent bottleneck and what it took to sustain decision cadence at scale.

• Shared context is the connective tissue between strategy and delivery. Bring back a model for what that looks like in practice — and what it means for your own stack.

• Endava is still writing their transformation success story. Get a candid read on what's working, the challenges they still face, and what it might mean for your org.

AI hasn't just changed what teams can build, it's changed who can build, how fast, and what it means to do the job well. For product teams, that's both an enormous opportunity and a fundamental challenge: how do you build the right thing, together, when everything about how teams work is being reinvented in real time?

Tomer Cohen has been at the forefront of this shift, first as Chief Product Officer at LinkedIn, where he led one of the most ambitious reimaginings of product development in the industry, and now as a builder and investor shaping what comes next. In this keynote, he shares what he's learned about leading product teams through AI transformation: why clarity of direction matters more than ever, what it actually takes to build a team that can move from idea to launch at AI speed, and what the next generation of builders will look like.

Key takeaways:

• The lines between product, design, and engineering are blurring. Design for that shift intentionally — the leaders pulling ahead already are.

• The bottleneck has moved from writing code to figuring out what to build. Leave knowing what that shift means for the skills every builder on your team needs now.

• The organizations closing the gap between strategy and execution aren't adding more AI tools — they're rethinking how their teams work together. Leave with a clear picture of what that looks like in practice.

Swap notes over lunch and make connections that last long past Canvas.

Building is happening everywhere. AI coding tools, vibe coding, chat-based workflows ... the number of tools that can help teams ship is exploding. But just because you can build it, doesn't mean you should ship it.

The teams pulling ahead have figured out how to move quickly from idea to validated direction, so that alignment happens before build, not after.

In this session, see how Miro Prototypes acts as the decision layer in your product development workflow. It empowers you to turn ideas into visual artifacts your team can explore, validate, and align on before anyone picks up a dev tool. Walk away knowing how to make the decision on what to build faster, clearer, and better for your whole team.

Key takeaways:

• Building is happening everywhere, but the decision on what to build should have a home. See how Miro Prototypes becomes that decision layer for EPD teams.

• Getting to a first prototype is just the start. Explore how variant exploration and validation give your team the backing to choose a direction with confidence.

• Leave with a workflow and artifact your team can use immediately: from idea to aligned, validated direction without the back-and-forth that slows everything down.

Speed is really a function of how quickly teams align. So, J.Crew Group went beyond optimizing the process and redesigned the environment where the collaboration happens.

Amanda Kane, SVP of Product Operations, and Tracy Love, SVP of Enterprise Technology, share how J.Crew Group leveraged Miro as a foundation to build its Digital Atelier: a shared environment where design, operations, and technology work together in real time, with AI-driven insights — cost, feasibility, creative direction — embedded directly into the workspace where decisions get made. No handoffs. No context-switching. Just the right people, the right information, and the ability to act on it in the moment.

This is the story of what it takes to go beyond adoption — and build a collaborative AI ecosystem that works the way your organization actually works.

Key takeaways:

• Speed to market is an alignment problem. Learn how J.Crew Group built a shared environment where decisions happen in real time — and what that shift did for their speed to market.

• AI works best when it's built in, not bolted on. Walk away with a model for embedding intelligence directly into the shared workspace where decisions happen.

• A real implementation partnership changes what's possible. Get a firsthand look at how J.Crew Group collaborates with Miro's team iteratively to build something that fits how their teams actually work.

AI coding tools are only as good as the context they get — and most teams are still copy-pasting context into prompts.

Miro's MCP server works both ways. Start from your codebase: generate architecture diagrams and living documentation directly into Miro. Or start from your board: feed existing specs, decisions, and system designs straight into Claude Code, Cursor, or Codex.

In this session, we'll demo both directions live — and show how the loop stays alive as your code and context evolve together. Whether you're an engineer, PM, or designer, you'll see how MCP turns your Miro canvas into the context layer your AI tools have been missing. And when your AI tools see what your team sees, they stop guessing and start delivering.

Key takeaways:

• Your codebase is a starting point. Generate architecture diagrams and living docs from code — you don't need existing content in Miro to get value from MCP.

• MCP bridges the gap between planning and execution. Walk away knowing how to use Miro's MCP server in practical, day-to-day workflows — from pulling specs into your coding agent to keeping documentation in sync with your codebase.

• Agentic AI works better with the full picture. See first-hand how the quality of AI-generated code changes when your tools can access your team's architecture, decisions, and intent.

This session will bring another real-world story of how a leading organization is using collaborative AI workflows to build faster, align better, and scale what's working across their teams. Stay tuned!

Every organization has AI. Almost none have AI that works across teams. The gap between individual AI productivity and organizational impact is where most companies are stuck — and it's exactly what AI Workflows was built to close.


In this spotlight, we'll show you how teams are using AI Workflows to move from isolated experiments to connected, automated systems — with real customer examples and the roadmap announcements that take it even further. If you're trying to figure out how to go from "we use AI" to "AI runs our collaborative workflows," this is your session.

Key takeaways:

• Flows automate the space between tasks, turning one step's output into the next step's input. Leave knowing how to build that chain across your team's most critical workflows.

• Sidekicks don't wait to be asked; they bring proactive, context-aware AI into the work itself. Walk away with a clear picture of how to deploy agents that actually change how your team operates.

• AI Workflows is the infrastructure layer connecting your AI tools to the way your teams actually work — and leading organizations are already running on it.

AI has fundamentally changed what engineering teams are expected to deliver, and how fast. But as most engineering leaders know firsthand: transforming an organization to work AI-first isn't a flip of a switch. It's messy, iterative, and full of hard tradeoffs.

Miro engineering leaders Michael Aronzon and Łukasz Sągol will give a candid, unfiltered account of a journey that might sound familiar because most engineering orgs are navigating the same one. They'll share what it took to build AI structure, governance, and a team of change agents that drove adoption across the org, how cross-functional dynamics have evolved along the way, and where Miro's engineering transformation is headed next.

The second half of the session opens into peer conversation, where you'll have the opportunity to engage directly with Miro's engineering leaders and each other. Come prepared with your own AI-first engineering challenges, and we'll select a few to problem-solve live with the group. Whether you're an IC navigating AI-assisted development or a leader rethinking how your org works, this is a candid, participatory discussion about a new reality we're all navigating together.

Key takeaways:

• What a real AI-first engineering transformation looks like at scale: the wins, the missteps, and what actually worked

• How to build the organizational structures and change agents that drive AI adoption across large teams

• How cross-functional EPD dynamics are evolving in an AI-accelerated world

• Peer connections and practical ideas you can bring back to your own team

Agentic AI hasn't just changed what product, design, and engineering teams can build — it's changed how they work together, where the bottlenecks form, and what it means to move fast without losing alignment. For organizations serious about becoming AI-native, that's where the real work begins.

As leaders of Cisco's networking management platform and AI infrastructure, Austin Lin and his teams navigate this transition every day. In this session, he shares how Cisco is navigating the changing role of the engineering-product-design triangle: how AI is reshaping the product lifecycle from prototyping and customer insights through planning and delivery, why tradeoffs and collaboration remain the hardest problems to solve at scale, and what the next model for cross-functional product teams actually looks like.

Key takeaways:

• The New EPD operating model. Understand how the roles of product, design, and engineering are shifting — and what that means for how your teams are structured and how they work together.

• Becoming AI-native requires more than AI tools. Move beyond “AI as a tool” and learn how Cisco is transitioning EPD teams toward AI driven development processes that automate the mundane, expand team capacity, and human decision-making

• Cisco's approach to AI-accelerated product development is WIP — and that's the point. Get an honest look at what's working, real world challenges and what it takes to transform at enterprise scale.

AI has collapsed the cost of building. It's also collapsed the window for staying relevant. Every distribution channel your growth strategy was built on is being disrupted faster than most teams have a strategy for it. 

Brian Balfour, Chief Growth Officer at Miro, has spent the last decade helping companies build durable growth engines. In this keynote, he shares what AI has broken, what it's making possible, and what it actually takes to build compounding growth advantage in this era.

Key takeaways:

• The technology shift and the distribution shift don't happen at the same time. We're in the gap right now — position for what comes next before the window closes.

• AI hasn't just changed what you can build; it's changed whether your existing growth loops still work. Map which fits are holding — and which are quietly breaking.

• Build the kind of compounding advantage that means you're setting the pace, not chasing it — and walk away knowing exactly where to start.

A moment to celebrate the community in the room, reflect on what the day surfaced, and leave with a clear charge: the window to build differently is open. What you do with it starts tomorrow.

Revisit the hands-on activations and unwind with your new connections (the LinkedIn requests can wait 'til tomorrow).