It’s a scenario every product team knows. A designer remembers a user complaining about the “Checkout Flow” during an interview last month.
“Where is that clip?” they ask.
Is it in a Zoom recording? A Slack thread? Or buried in a 50-page PDF report?
After 20 minutes of searching, they give up. The insight is lost. The team builds the new feature based on assumptions, not evidence.
This is “Insight Amnesia,” and it happens because most research lives in static documents, disconnected from the actual product work.
Here is why you should move your research out of Google Drive and into a Visual Repository like AITable.ai.
1. Centralize the Evidence (The Gallery)
The biggest problem with research data is that it’s messy. You have video clips, screenshots of bugs, survey responses, and audio notes.
In a folder structure, these are just filenames. In AITable.ai, you use Gallery View.
Suddenly, your research comes alive. You can see the user’s face in the video thumbnail. You can see the screenshot of the broken UI.
Seeing a grid of real users struggling with your product is 10x more motivating for developers than reading a bullet point in a doc.
2. Tagging “Nuggets” (The Atomic Unit)
A 60-minute interview might contain 5 different insights. Storing the whole video file isn’t helpful because nobody has time to watch it all.
However, with AITable.ai, you can break it down.
Create a record for each “Insight Nugget”—a specific quote or observation.
- Quote: “I can’t find the logout button.”
- Tags:
#Mobile,#Navigation,#Bug,#Persona:Admin.
Now, when a PM is planning the “Mobile Refresh,” they can filter the database: “Show me all insights tagged #Mobile.” They get a curated playlist of evidence in seconds.
3. Connecting to Action (The Roadmap Link)
Research often stays trapped in the research team. The engineers building the features never see it.
In contrast, AITable.ai bridges this gap.
Because your Product Roadmap and Research Repo can live in the same database (or linked tables), you can connect them directly.
- Create a Feature record: “New Checkout Flow”.
- Link it to 5 Insight records (videos of users failing the old checkout).
When a developer opens the “New Checkout” card on their Kanban board, they see the linked evidence right there. They don’t have to ask “Why are we building this?”. The context is built-in.
Conclusion: Make Research Visible
Research is useless if nobody sees it.
Don’t let your hard-won insights gather dust in a digital drawer. Build a visual system where insights are searchable, linkable, and impossible to ignore.
Start your Visual Research Repository in AITable.ai today.