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AI in Airtable: Where It Actually Belongs (And Where It Doesn’t)

Picture of Julia Eboli   Julia Eboli  ·  2 minute read

#6

 

AI belongs in Airtable, but only when it reinforces the system instead of standing in for design work that hasn’t been done yet.

 

AI is everywhere now. It’s built into the tools teams already use, the workflows they rely on, and increasingly into Airtable itself. The newer AI features are genuinely useful: summarizing messy inputs, drafting content, extracting structure from unstructured data. For many teams, they remove friction that used to feel unavoidable.

So the question isn’t whether AI belongs in Airtable. It clearly does.

The real question is where it belongs, and where it doesn’t, especially once systems start to scale.

 

 

When AI feels like a shortcut, and where it actually fits

 

AI isn’t an abstract add-on anymore. It’s part of Airtable.

Airtable’s built-in AI capabilities allow teams to summarize large text fields, extract insights, categorize and label records, and generate or translate content directly inside a base, without relying on external tools or scripts.

You can also create AI-powered fields that analyze, retrieve, or generate data at the cell level as part of a table’s workflow, rather than bolting AI on afterward.

At a small or mid-sized team level, these features feel immediately helpful. Teams generate first drafts of briefs, categorize customer feedback automatically, and let AI handle labeling or enrichment that used to require manual effort.

Those are the places AI belongs in Airtable:

  • Augmenting mundane work (summarization, categorization, extraction)

  • Speeding up pattern recognition

  • Reducing manual effort without changing the core workflow

  • Helping teams focus judgment where judgment actually matters

Each of these works because the system is already defined: AI operates inside it, not around it.

 

 

Where teams need to be more deliberate

 

As Airtable systems grow, they stop being isolated bases and start connecting to custom interfaces, downstream integrations, reporting layers, and operational commitments that extend beyond a single team.

At that point, AI isn’t just helping individuals move faster, it’s influencing how the system behaves as a whole.

This is where placement starts to matter. If AI is used to decide whether a record is “ready” without that state being clearly defined in Airtable, decision logic has moved out of the system and into a model with its own criteria. If AI is used to route work automatically without ownership or sequencing being encoded in the schema, part of the workflow has effectively been delegated without being made explicit.

The same applies to AI agents. In straightforward setups, they can be genuinely helpful, especially when workflows are simple and the consequences of variation are low. As environments mature, expectations around consistency and explainability increase. Agents work best when workflow boundaries are already clear and the outcomes they influence are well understood. When those boundaries aren’t explicit yet, agents can end up acting on assumptions the system itself hasn’t fully defined.

Nothing here means Airtable or AI is falling short. It reflects the fact that the system is carrying more responsibility. The base still functions, but it becomes harder to explain why things happened the way they did, especially when data feeds reports, dashboards, or external tools.

That’s the moment teams need to slow down just enough to decide where AI reinforces the system, and where it starts to stand in for design work that hasn’t been done yet.

 

#6 -1

 

How experienced teams approach AI placement

 

Teams that get this right don’t start with, “What can we automate with AI?”

They start with:

  • What must stay explicit in the system?

  • Which decisions need to be inspectable later?

  • Where does ambiguity help, and where does it create confusion?

  • Which parts of the workflow are safe to accelerate without changing meaning?

Only after answering those questions do they introduce AI-deliberately, in places where it amplifies the system rather than working around it.

 

 

In practice

 

AI absolutely belongs in Airtable. The platform is moving quickly, and the gains are real.

At scale, the work isn’t about adding AI everywhere. It’s about placing it carefully, so the system remains understandable, predictable, and trustworthy as it grows.

Teams that treat AI as a system component (not a shortcut) are the ones that move faster without losing clarity.