Hey everyone,
I've been deep in the weeds lately trying to migrate a ton of legacy customer feedback data from old CSV exports into a clean, usable Notion database. We're talking thousands of rows with messy, inconsistent formatting—you know the drill. It got me thinking: could the new Notion AI features actually help with the tedious cleanup process?
I gave it a shot on a sample set, and my experience was... mixed, but with some promising highlights. Here’s a quick rundown of what I tried and how it performed:
**What Worked Well:**
* **Standardizing Text Formats:** Notion AI was fantastic at taking a column with wildly different text cases (e.g., "OPEN," "open," "Open," "oPeN") and rewriting them all to a consistent "Open" or "Closed" format. This saved me a ton of manual find-and-replace.
* **Summarizing Long Notes:** I had a "Customer Comments" column with paragraph-length text. Using the "Summarize" AI action on a database property quickly gave me a one-line gist, which I then populated into a new "Summary" column for quick scanning.
* **Extracting Key Tags:** From unstructured text fields, I could prompt it to "Extract the main product mentioned" and it would often correctly pull out product names into a new property.
**Where It Struggled:**
* **Complex Conditional Logic:** I couldn't find a way to get it to do multi-step cleaning based on other columns. For example, "If Status is 'Closed' and the Priority column is empty, set Priority to 'Low'." That kind of relational cleanup still seems to need a proper formula or manual work.
* **Batch Processing:** You have to run the AI action on each cell or a small selection. There's no way (that I found) to apply an AI transformation to an entire column at once, which makes large-scale cleanup a bit of a chore.
* **Data Type Conversion:** It wasn't reliable at converting a text "date" string into an actual Notion Date property format. I ended up using a traditional formula for that.
**My Quick Comparison Table:**
| Task | Notion AI Suitability | Best For |
| :--- | :--- | :--- |
| Text Standardization | ⭐⭐⭐⭐⭐ | Small to medium batches, simple case/format fixes |
| Summarization & Extraction | ⭐⭐⭐⭐ | Creating derived properties from text-heavy columns |
| Complex Data Rules | ⭐⭐ | Simple, single-column logic only |
| Full Dataset Overhaul | ⭐⭐ | Small datasets; lacks true batch operations |
So, my takeaway is this: Notion AI is a really handy **supplementary tool** for data cleaning, especially for textual cleanup and enrichment. It's like having a smart assistant for the repetitive parts. However, for heavy-duty, rule-based CSV transformation, you'll probably still want to lean on a dedicated tool (like Excel/Sheets, or a proper ETL script) *before* importing into Notion.
Has anyone else experimented with this? I'm particularly curious if anyone found a clever way to chain AI commands or use them with formulas to automate more of the process. What was your experience like?
Happy evaluating
customer first
Interesting point about summarizing long notes. I've run similar tests for dashboard-friendly excerpts. The catch I found is that Notion AI's summaries can drift from the original intent on highly technical or nuanced feedback, which introduces data quality risk.
For something as critical as customer comments, I'd only use it for a first pass before a manual review. For my team's pipeline, we now run these summaries through a quick sentiment check in BigQuery to flag any potential misinterpretations before they hit the dashboard.