TLDR Product Management 2026-04-28

A vibe coded prototype is nice, but it doesn't let you gather feedback or collaborate. With Miro Prototypes, you can validate concepts with your entire team in <1 day:

• Collaborate from the start - Bring PMs, designers, engineers, and stakeholders into one shared canvas.
• Clear direction before you commit - Explore options and align on the right direction before design or code begin.
• Bring teams along for the ride - Test and iterate along with your clients and stakeholders before product development begins.

Don't wait for the final phase of product development to rework concepts. Start with a free trial

AI isn't fixing organizational problems - it's exposing them, especially gaps in communication, context, and alignment. Without a strong shared understanding and trust, both teams and AI systems fail.

Products start as wedges, but lasting advantage comes from becoming a control point others depend on. In an AI-driven world, control determines who wins.

Faster output doesn't equal better outcomes. Real progress requires time for coordination, reflection, and alignment. Over-optimizing speed with AI can worsen systemic issues, making thoughtful collaboration more important than ever.

AI can generate outputs, but outcomes require clear goals, context, and measurement. It amplifies good strategy or accelerates the wrong work.

Most teams can build dashboards. Very few can scale them across users, teams, and products without creating fragmentation. Reveal's new report shows how teams will scale intelligence in 2026. Read it to see how AI is changing & challenging analytics, and why business intelligence needs to be embedded directly into products. Get the report

Great AI chatbots succeed through clear capabilities, accessibility, and guided interactions. Small design choices determine whether users find them helpful or frustrating.

Top apps win by building commitment, proving value early, and delaying the paywall.

Strategy and execution must evolve together, balancing clear direction with rapid learning. Great product work combines discipline with speed.

AI speeds up building, but without strong feedback loops, teams create unverified systems. Real progress requires a harness to ensure fast iteration leads to learning.

Data tools today answer questions people already know to ask, but the most important insights come from what they don't.

Early hypergrowth focuses on solving one key problem at a time, while late hypergrowth requires solving many at once for a broader, more skeptical market.