From discovery to delivery: How to stop building the wrong thing

Summary

In this article, you'll learn:

• Why the gap between discovery and delivery keeps costing product teams
• How false consensus in written documents leads to wasted work
• Why moving visuals earlier in the product lifecycle changes everything
• How Miro Prototypes converts messy discovery work into tangible concepts
• How teams use Miro Sidekicks and preview mode to validate before they build
• Real workflows for zero-to-one and brownfield product development
• Tips for getting the most out of Miro Prototypes

Here’s a scenario most product teams know all too well: you’ve spent weeks working through a PRD, everyone has read it, nodded along in the meetings, and by all appearances, the team is aligned and ready to build.

Then someone creates a visual prototype, and the whole thing cracks.

Technical constraints surface that no one had flagged, user flows that seemed logical on paper suddenly don’t hold up, and requirements that everyone agreed on start to look a lot less clear once they’re rendered on a screen. By the time the team recalibrates, weeks of work are already behind them.

This is the discovery-to-delivery gap, and it’s one of the most persistent, expensive problems in product development. According to Shipra Kayan, Principal Product Evangelist at Miro, it’s a pattern she’s seen play out on almost every project across her two decades in the industry.

“On every project I’ve been a part of, it seems initially like everyone agrees to the solution — until they see a visual prototype. And that’s when the technical constraints, the unmet requirements, and the customer confusion all bubble up to the surface,” she says.

The question isn’t whether the gap exists. It’s how to close it before your team wastes time building the wrong thing.

Shipra Kayan, Principal Product Evangelist at Miro, and Mathias Davidsen, GM of Prototypes, have spent years working with product teams on exactly this problem. The insights in this article are drawn from their session below, where they walk through how Miro Prototypes helps teams move from messy discovery work to validated concepts, faster.

The real reason discovery handoffs break down

The discovery-to-delivery gap isn’t just a process problem. It’s a communication problem. And at the heart of it is an over-reliance on written documents as the primary way teams share ideas, align on direction, and move work forward.

Written PRDs, specs, and requirements documents are genuinely useful. But they’re not sufficient for building shared understanding across a cross-functional team. Words describe. Visuals show. And when it comes to building software, showing is what actually creates alignment.

Mathias Davidsen, GM of Prototypes at Miro, has spoken with well over a thousand companies about this exact challenge, and in the session above, he describes a pattern he sees consistently: design teams have too many competing priorities, and product teams don’t have the right tools to communicate concepts and requirements visually. The result is a process that only produces visuals at the very end of a long, structured discovery phase, instead of throughout it.

“Visuals only follow a long, structured discovery process, instead of being built during it,” Davidsen explains. “And in the end, we build a product that isn’t validated with customers, hasn’t been surfaced to stakeholders, and hasn’t been shared between teams. So we lack alignment, and instead of building the right solution, we just built a solution.”

The consequence? Teams that believe they’re aligned discover they aren’t, but only after they’ve invested significant time and money in development. Discovery handoffs fail not because teams aren’t working hard, but because the medium they’re using to communicate isn’t built for the complexity of what they’re trying to convey.

Why moving visuals earlier changes everything

The fix isn’t to abandon structured discovery. It’s to introduce visuals much earlier in the process, so that the feedback, misalignment, and course corrections happen before the team has committed to building.

This is the core premise behind Miro Prototypes. Instead of treating prototyping as a step that follows discovery, it integrates visual communication directly into the discovery process. Teams can go from sticky notes and research artifacts to interactive visual concepts in a fraction of the time, without starting from scratch, and without waiting for a designer to have bandwidth.

“We believe it’s all about speed to iteration, speed to earlier decisioning, speed to earlier alignment — and visuals are the best way for teams to communicate,” Kayan says. “We build digital products, and visuals are the best way for teams to come to decisions.”

The shift matters because the earlier a team identifies a problem with a proposed solution, the cheaper it is to fix. Catching a fundamental UX issue during a prototyping session in week one costs almost nothing. Catching it after six weeks of development costs a great deal: in time, morale, and momentum.

A Forrester Consulting study commissioned by Miro found that organizations often lose momentum during the Discovery (38%) and Definition (35%) phases of product development, the exact stages where prototyping can have the most impact. Getting teams visually aligned early doesn’t just save time. It directly improves the quality of what gets built.

Converting messy discovery work into tangible prototypes

One of the most practically powerful things about Miro Prototypes is that it starts from where your team already is. There’s no need to re-enter context, re-upload research, or brief an AI tool from scratch. Your discovery work, including the sticky notes, the journey maps, the opportunity mappings, the screenshots, and the PRD fragments, is already on your Miro board. Miro Prototypes uses all of it.

In a live demonstration, Davidsen showed how a team working on a payments app used their existing board as the prompt. The board included a PRD, an opportunity mapping exercise, an existing app screenshot for brand reference, and a set of feature ideas. By selecting those objects and opening the Prototypes panel, the team was able to generate a five-screen prototype, including a dashboard, a budget insights view, an alerts screen, and a profile page, without building anything from scratch.

Critically, the AI didn’t just generate generic screens. It read the opportunity mapping to understand which features the team had prioritized, and it reflected those priorities in the output.

“As the prototype was being generated, it looked at the opportunity map and actually recognized which opportunities we had prioritized higher — and within the prototype, those are reflected,” Davidsen notes.

This is the canvas working as the prompt. Instead of typing a brief into a chat window and hoping the output is relevant, teams give Miro the full context of their work, and the prototype reflects it.

Getting the right look and feel is often a sticking point with AI prototyping tools. Miro Prototypes addresses this in three ways: you can add a screenshot of an existing product for the AI to match stylistically, you can specify fonts, colors, and structural guidelines directly in the prompt, or you can use the Prototyping Library to upload custom atomic components that the AI will use as building blocks in the output. The result is a prototype that not only captures the right concepts but looks like it actually belongs to your product.

Iterating fast, without writing a line of code

Generating a first version is just the beginning. The real value of Miro Prototypes in the discovery-to-delivery workflow comes from how quickly teams can iterate on what they’ve generated.

Because prototypes live on the Miro canvas, not in a code editor, edits are visual and immediate. Team members can make precise changes by selecting a screen and prompting the AI with a specific instruction, like adding an “unusual spending alerts” section above the spending insights panel. No code to debug. No dependencies to manage. Just a direct visual edit.

“Nothing else has to be debugged or has to work together. We’re just implementing the visual edits we need,” Davidsen explains. “And what’s like a designer’s dream is always to have multiple options to look through. So this is one way where I can ask the AI to ideate with me and come up with a couple of different options based on the direction we’re imagining the product to go.”

Teams can compare versions side by side, cycle through options, and revert to earlier iterations if a direction isn’t working. They also retain full manual control: drag, drop, point, and select, so that simple tweaks don’t require a prompt at all. The combination of AI-assisted generation and hands-on canvas editing gives teams the speed of automation without losing the precision of manual control.

This is also a meaningful departure from vibe coding tools, which Davidsen acknowledges are excellent for many purposes but introduce the wrong kind of friction at this stage of the process.

“Code at this point can actually become a distraction,” he says. “At this stage, we typically just want to create some visuals to create alignment, get some early feedback, and validate that the solution is headed in the right direction.”

Democratizing discovery for the whole team

The discovery-to-delivery gap is partly a resourcing problem. Design teams are stretched. When creating a prototype requires a designer, prototyping only happens when a designer is available, which, during the busiest discovery phases, is rarely at the right moment.

Miro Prototypes changes this by making visual communication accessible to the whole product team. Product managers, business analysts, engineers, and even sales and marketing professionals can take their ideas, their user research, and their requirements, and turn them into visual concepts without design expertise.

“We see non-designers across product, across business analysts, developers — suddenly being able to turn their assets in Miro into visuals and communicate requirements at a level of detail they weren’t able to do before,” Davidsen says.

This isn’t about replacing designers. It’s about removing the bottleneck that means discovery work only becomes visual when a designer finally has bandwidth. When anyone on the team can generate a prototype, alignment conversations can happen earlier, feedback loops get shorter, and the handoff from discovery to delivery becomes far less painful.

From brownfield work to validated concepts

Not every product starts from zero. The vast majority of real-world product work, in Davidsen’s estimate 80 to 90% of Miro Prototypes use cases, involves making changes to an existing product. Miro Prototypes is built for this too.

Teams can take a screenshot of an existing product screen and convert it directly into an editable markup. Every element becomes individually editable: move things, delete what doesn’t belong, add new components from the Prototyping Library. This is dramatically faster than annotating over screenshots, stitching images together, or pulling design files from a system that may be out of date.

“What’s more up to date than your current product?” Kayan points out. “Just being able to take a screenshot and not having to look for those design files — it’s workflow-changing.”

Teams can also use existing product screenshots as stylistic context for generating entirely new screens. In the demonstration, Davidsen used a screenshot from a banking app to generate a new screen that added a cryptocurrency section, converted the UI to English, and maintained the visual style of the original, all from a single prompt.

Getting better feedback with Miro Sidekicks

Once a prototype exists, the next challenge is getting the right feedback. Most teams know they need input from stakeholders and end users, but they also know how those sessions typically go: feedback focuses on obvious usability issues that the team could have caught themselves, while the deeper, more valuable insights get crowded out.

Miro Sidekicks change this. Before sharing a prototype with stakeholders or customers, teams can select the prototype, open the UX researcher Sidekick, and ask it to analyze the screens, identifying the five most critical usability issues, complete with clear recommendations for how to resolve them.

“Instead of sharing this and having our stakeholders tell us these five things — which are very clearly things that would be told to us — we can just ask AI to surface this, and then make sure the feedback we get from our stakeholders and end users is the really valuable stuff we wouldn’t be able to catch ourselves,” Davidsen explains.

Once the report is generated, teams can take the recommendations and immediately prompt the AI to create a new, improved version of the prototype with those changes implemented. The same workflow applies to accessibility: instead of relying on an accessibility expert being available, teams can ask a Sidekick to generate an accessibility review and produce an updated version of the prototype with those issues addressed.

The result is a prototype that’s been refined before it ever reaches a user, so the feedback collected in real sessions focuses on the insights that actually matter.

Sharing prototypes for real concept validation

The final step in the discovery-to-delivery bridge is getting the prototype in front of real people. Miro Prototypes includes a preview mode that turns the canvas into a full, interactive product experience. Input fields work. Sliders respond. Dropdown buttons function. Connector lines between screens create a clickable flow that mirrors the actual product experience.

Teams are already using this to run concept validation with real customers during the early stages of discovery, before a single line of production code has been written.

“This is stuff we already see teams put in front of customers and end users for real concept validation during their discovery work — to make sure that once we move on and go deeper into delivery, we’ve actually already validated the concept we’re working on,” Davidsen says.

This is what closing the discovery-to-delivery gap looks like in practice. Not a handoff document. Not a presentation deck. A tangible, interactive concept that customers can actually respond to, while there’s still time to change course.

Tips for getting the most out of Miro Prototypes

Knowing the mechanics of Miro Prototypes is one thing. Using it well is another. Based on the best practices Davidsen and Kayan have seen across thousands of teams, here’s what separates teams that get genuine value from the workflow from teams that struggle with it.

Start with real context, not a blank prompt. The quality of your prototype is directly tied to the quality of your input. Before you generate anything, select the canvas content that best represents your team’s current thinking: the PRD, the opportunity map, the user journey, the feature ideas. The AI uses all of it to shape the structure and logic of what it generates. A vague prompt produces a generic prototype. A prompt grounded in real discovery work produces something your team can actually react to.

Be specific about what you want. You don’t need an exhaustive brief, but detail helps. If the visual styling matters, include your brand colors as hex codes, or select a product screenshot and ask the AI to match its look and feel. If the screen structure matters, describe it: specify how many screens you want, what each one should cover, and how they connect. The closer your prompt is to your actual intent, the fewer iterations you’ll need to get there.

Treat the staging area as your sandbox. Don’t place a prototype on the canvas until you’re happy with the direction. The staging area is where you experiment: keep what works, regenerate what doesn’t, and compare versions before committing. As Davidsen puts it, teams can “flip between versions, figure out which one they want, and always go back to the one they prefer if they go down the wrong path.” Getting this right in the staging area means fewer disruptive changes once the team is reviewing together.

Don’t prompt every change. Once a prototype is on the canvas, not every edit needs to go through AI. Some changes are simply faster to make by hand. Use the Prototyping Library to swap components, adjust spacing, or rewrite labels directly. This is especially true for small, precise tweaks where writing a prompt would take longer than just making the change. The combination of AI generation and manual editing is a feature, not a workaround.

Use Sidekicks before you share. Before you put a prototype in front of stakeholders or customers, run it through the UX researcher Sidekick. Ask it to identify the top usability issues and give you recommendations. Then create an improved version with those changes applied. This two-step process means you’re not wasting valuable stakeholder time on issues the AI could catch in minutes. “Get the obvious things out of the way,” as Kayan says, so that the feedback you get from real people is the kind that actually moves the product forward.

Create with Sidekicks to keep your AI chat history. When you generate prototypes through a Sidekick rather than directly through the AI panel, Miro retains the chat history. This means you can review the prompts you’ve already tried, pick up where you left off on any board, and avoid repeating directions the AI has already explored. For teams iterating over multiple sessions, this continuity makes a real difference.

Use Focus Mode when presenting. When you’re walking stakeholders through a prototype on a video call, the full Miro board can be distracting. Switch to Focus Mode, or use the Prototype Format, to share only the prototype itself. This keeps the conversation on the concept rather than the canvas, and makes the experience feel closer to a real product demo. For async feedback, record a quick Talktrack walkthrough so stakeholders can watch your thinking before they respond.

Capture decisions in comments, not Slack. Feedback from prototype reviews has a way of disappearing into chat threads. Make it a team habit to capture key decisions, unresolved questions, and next steps directly in comments on the board. This keeps the context where the work lives, and makes it much easier to pick up the thread in the next session without reconstructing what was agreed.

Align on direction before handing off to design or development. Miro Prototypes is most valuable when it’s used to reach genuine alignment before the team transitions to high-fidelity design tools or development. The goal isn’t a pixel-perfect output. It’s a shared, validated direction. Once the team agrees that the concept is right, that’s the moment to hand off, not before. Using prototypes to reach that agreement early is exactly what prevents the costly rework that happens when misalignment only surfaces after weeks of development work.

Putting it all together: a faster path from idea to delivery

The discovery-to-delivery gap doesn’t close by accident. It closes when teams build the habit of making their work visual earlier, iterating faster, and validating concepts before they commit to building them.

Miro Prototypes gives teams the tools to do exactly that. Starting from the discovery artifacts already on their Miro board, they can generate a multi-screen prototype, refine it with AI-assisted edits, run a pre-stakeholder usability review with Sidekicks, and share a clickable preview for real customer feedback, all without leaving the canvas, and all before the engineering team has been handed a spec.

“I’m way more confident that the things we are implementing for the product are the right things,” says Björn Ehrlinspiel, Product Owner at Miles & More (Lufthansa Group), who used Miro Prototypes to create, validate, and align on the right solution in less than one day. “Miro Prototypes helps me show my vision to the management team of the product.”

That confidence, knowing the team is solving the right problem in the right way with everyone genuinely aligned, is what the discovery-to-delivery gap has always been stealing from product teams. Miro Prototypes gives it back.

Ready to see it for yourself? Try Miro Prototypes and start turning your discovery work into validated concepts, fast.

Frequently asked questions

What is Miro Prototypes? Miro Prototypes is an AI-powered prototyping feature built into Miro’s innovation workspace. It allows teams to generate interactive, multi-screen prototypes directly from their existing discovery work on the Miro canvas, including sticky notes, journey maps, opportunity mappings, PRDs, and product screenshots, without needing to start from scratch or write any code.

Who can use Miro Prototypes? Miro Prototypes is designed for the full cross-functional product team, not just designers. Product managers, engineers, business analysts, marketers, and other team members can all use it to create and refine visual concepts. This democratization of prototyping is one of its core design goals.

Do I need design skills to use Miro Prototypes? No. Miro Prototypes is built to make visual communication accessible to anyone on the team. You can generate prototypes from your existing board content using natural language prompts, and refine them using a combination of AI-assisted edits and manual drag-and-drop controls on the canvas.

How does Miro Prototypes help with discovery handoffs? By making it possible to create visual prototypes during the discovery phase, not just after it, Miro Prototypes helps teams surface misalignment, technical constraints, and unmet requirements before they’ve committed to building. Teams can share clickable, interactive prototypes with stakeholders and customers for real feedback early in the process, which dramatically reduces the cost and pain of late-stage course corrections.

Can I use Miro Prototypes for existing products, not just new ones? Yes. Teams can take a screenshot of an existing product screen and convert it into an editable prototype, then modify it to visualize new features or changes. This makes Miro Prototypes just as useful for brownfield work, updating or extending an existing product, as it is for zero-to-one development.

How does Miro Prototypes get the right brand look and feel? Teams have three options: they can add a screenshot of an existing product for the AI to match stylistically, they can specify colors, fonts, and structural guidelines in the prompt, or they can use the Prototyping Library to upload custom design components that the AI will use when generating screens.

What are Miro Sidekicks, and how do they work with prototypes? Miro Sidekicks are custom AI partners within Miro that can be configured around specific tasks and expertise. In the prototyping workflow, teams can use the UX researcher Sidekick to analyze a prototype, identify the most critical usability or accessibility issues, and generate recommendations, which can then be used to automatically create an improved version of the prototype. This lets teams address obvious issues before sharing with stakeholders, so feedback sessions focus on genuinely valuable insights.

Is my data secure when using Miro Prototypes? Yes. Miro follows security-by-design principles across all its AI features, including Prototypes. Enterprise admins have granular control over which AI features are enabled at the organization and team level. Miro holds certifications including SOC 2 Type II and ISO/IEC 27001, and offers data residency options in the EU, US, and Australia. You can find full details in Miro’s Security and Compliance whitepaper and Trust Center.

Does Miro integrate with other tools my team uses? Yes. Miro integrates with a wide range of product development tools including Jira, Confluence, GitHub, Figma, Notion, Slack, Microsoft Teams, and many more. Enterprise customers can also access SIEM, CASB, and EMM integrations, and build custom integrations via Miro’s API.

Author: Sarah Luisa Santos, Content & Growth @Miro Last updated: April 14, 2026