Introduction

Granola’s 70% retention rate shows the power of “invisible AI” - tools that help quietly, without getting in the way.

What’s the problem it solves?

Many AI tools are flashy or intrusive, but they don’t stick because they disrupt user workflows. Granola solves this by blending AI into daily habits so naturally that users barely notice it’s there.

Quick Summary

Granola, a London-based startup, created a note-taking app that feels more like an assistant than a replacement. Instead of recording whole meetings or interrupting with bots, it lets people take their own notes and then uses AI afterwards to polish, organize, and expand them. This keeps the human in control but makes the final output far more useful.

The secret is what Granola’s team calls “invisible AI” - tech that stays in the background. It preserves human agency, improves notes without overwriting them, builds long-term context across meetings, and shows clear links back to the source so users can trust the results. The company reached its success by focusing on core features, cutting out what didn’t work, and evolving step by step with heavy user testing.

Granola is now shifting from a solo tool to a team platform. With folders for sales calls, feedback, and hiring, entire teams can ask bigger questions like “Why are we losing deals?” and get AI-powered answers with citations. This points to the next wave of AI design: helping groups, not just individuals, think and work better.

Key Takeaways

• The best AI is “invisible” - it supports users without stealing control.
• Human-first workflows are more powerful than AI-first features.
• Building trust means showing how AI reached its conclusions.
• Cutting unnecessary features leads to stronger, stickier products.
• The future of AI is not just individual productivity but collective intelligence.

What to do

• Start with the human task, then layer AI to support it.
• Design AI to enhance, not replace user work.
• Test with real users early, and be willing to remove weak features.
• Add transparency so users can verify AI outputs.
• Think about long-term context - how your product gets smarter with use.
• Explore team-level intelligence, not just personal productivity.

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