How to Use Airtable AI to Automate Data Classification and Summaries

Airtable AI brings generative intelligence straight into your Airtable bases. With AI fields and automations, you can automatically classify records, summarize content, and enrich data across thousands of rows without writing code.

This tutorial explains how to use Airtable AI to automate data classification and summaries, so your team can stop doing repetitive data work by hand.

Step 1: Open your Airtable base and identify the task

Start with a base you already use. Decide what repetitive AI task would help, such as classifying support tickets by priority, summarizing meeting notes, or tagging customer records by segment. Pick a single clear task to start.

Step 2: Add an AI field

Create a new AI field in your table and write a prompt that tells it what to produce from existing fields. For classification, your prompt might be: ‘Categorize this ticket as bug, feature request, or question based on the description field.’

Step 3: Reference the right fields

Make sure your AI field prompt pulls from the correct source columns. Test it on one record to confirm the output is accurate before processing the whole table. Tune the prompt wording to get results you trust.

Step 4: Run the AI field across your records

Once the prompt works on a sample, generate results for the rest of your rows. Airtable can batch-process many records at once, turning hours of manual work into a few clicks.

Step 5: Build an automation on top

Create an Airtable automation that reacts to record changes. For example, trigger the AI classification whenever a new ticket is added, or send a summarized update when a project record is modified. This keeps your data live and current.

Step 6: Review, refine, and expand

Spot-check the AI output for accuracy and refine your prompts where needed. Once you trust the results, expand the pattern to other tables and use cases, such as drafting replies, translating fields, or flagging anomalies.

Airtable AI turns your structured data into an active, self-maintaining system. By setting up AI fields and automations for classification and summarization, you can eliminate repetitive manual data work and keep your operations fast and consistent.