Miro Insights

An AI powered hub that turns scattered customer feedback into clear, revenue-backed product priorities.

Description

Product teams are drowning in feedback scattered across dozens of tools, leaving PMs guessing at priorities. Miro Insights is built for teams that drive product direction — primarily PMs and product owners, but also customer success, design, and sales teams who need visibility into what customers want. It solves the chaos by using AI to aggregate feedback across the tech stack, surface patterns, quantify customer impact, and align work with business value — so teams can stop guessing and start building with confidence.

Services

User Interface, User Experience, Product Strategy, Design Direction

Problem themes

Through user research and quantitative data, we found product leaders and EPD teams shared similar friction with the original experience — scattered feedback, heavy synthesis work, and roadmaps that drifted from customer reality.

Lack of integration

Managing feedback and synthesizing insights require significant manual effort — time-consuming and error-prone.

Usability challenges

Complex interfaces and cognitive overload made it hard to extract meaningful insights quickly.

Feature management

Tracking feedback and aligning prioritization with strategic roadmap expectations was a struggle.

Personalization needs

Users wanted customizable dashboards, filtering, and search tailored to role and tasks.

Roadmapping was fragmented, manual, and reactive. Our goal was to build a dynamic, AI-first roadmapping system where proactive intelligence continuously surfaced actionable decisions, keeping roadmaps aligned with reality and company strategy.

Product planning

Roadmapping was the second-highest traffic use case on Miro's website; ~60% of customers already used Miro for roadmapping. We proposed an opinionated experience with backlog and roadmap views, Insights enrichment, and Jira/CSV integrations.

User personas

The decision-maker

Product leaders prioritizing with clear customer evidence.

Checks what's changed since last review
Skims top signals and trends
Dives into evidence when it impacts work
Adjusts priorities, designs, or research focus

The builder

PMs, designers, and engineers creating a shared source of truth.

Reviews signal strength, confidence, and impact
Connects signals to goals and initiatives
Prepares decisions for leadership review

The practitioner

Designers and researchers grounding day-to-day work in evidence.

Scans what's changed since the last check
Digs into evidence when work is impacted
Adjusts design, research, or technical focus
Shares key signals to align the team

Low-fidelity exploration

Low-fidelity exploration focused on four interconnected ideas — proactive signals that cut through noise, collaborative agent-and-human intelligence, effortless feedback-to-roadmap linkage, and a workspace for human intervention — testing how AI and people could work together to turn evidence into roadmap decisions.

Proactive signals for roadmapping

Signals reduced PM anxiety by clearly showing either nothing (indicating they were on track) or a small set of items needing attention.

Proactive and collaborative intelligence

In addition to complying with AI governance patterns, updates were explored as a way to integrate agent updates and human conversation on roadmapping items.

Feedback to roadmap effortlessly

Linking customer feedback to roadmap items gave each feature a clear 'why,' enabling more objective prioritization, faster alignment, and decisions backed by real demand.

Human intervention ideas workspace exploration

Human intervention when needed

An AI-powered ideas workspace that surfaces, prioritizes, and connects product opportunities from signals and suggestions to help teams make faster, evidence-based roadmap decisions.

First-version release

We scoped the first release as a minimum lovable product: a focused, high-fidelity slice of the experience built to demo, sell, and generate momentum at our annual Canvas 26 event.