AI steps, Tables and chatbots
Everything so far has been plumbing — reliable, literal, and a bit dim. It moves things and checks conditions, but it doesn’t understand anything. This week the automation gets a brain. Zapier now lets you drop AI into the middle of a workflow, keep smarter data, and put a chatbot over your own material. Used well, it closes the gap between moving information and making sense of it. Used carelessly, it’s how a confident mistake ends up in your queue.
AI steps: judgement, inserted mid-Zap
The headline change is the AI step. Between a trigger and an action, you can now ask a model to do something with the data passing through: summarise this article in one line, classify whether it’s relevant to my themes, pull out the key claim, draft a neutral one-sentence gist, tag it by topic.
Go back to the Signal filter from a fortnight ago. That filter matches keywords — it sees words, not meaning. An AI step can read the item the way a person skim-reads it and judge relevance on substance, not vocabulary. The blunt instrument becomes a sharp one. The same trick writes you a tidy summary column, or sorts items into themes, before anything reaches your eyes.
Tables: a database that thinks
Zapier Tables is a database built for automation, living inside Zapier itself rather than bolted on via Sheets. For a Signal-style queue it’s a natural home: structured columns, and crucially an “AI Enrich” feature that auto-fills a field by running a prompt against the rest of the row. A “one-line summary” column or a “relevance score” column that populates itself as each item lands — no extra Zap required.
If you’ve outgrown a Google Sheet that’s creaking under formulas and helper Zaps, this is the upgrade.
Chatbots: answers from your own material
The third piece is chatbots you can point at your own content — a set of files, webpages, or a table. Feed it your style guide, your past issues, your house rules, and you have something you (or a colleague) can ask in plain English: “have we covered this topic before?”, “what’s our line on AI-written patient materials?” It’s a way to make institutional knowledge queryable instead of buried.
The caveat that never goes away
Here is the part that matters more in our field than almost any other. The moment you let a model interpret, you inherit its failure mode: it will be confidently wrong some of the time. It will summarise a study and quietly overstate the finding. It will tag something safe that isn’t. It will, asked to “extract” a statistic, invent a plausible one.
So the rule holds, and hardens: an AI step is a drafting aid, never an authority. Use it to triage, summarise and sort — work where a wrong call is cheap and caught at the next human glance. Keep it well away from anything that ships as fact without a person checking it against the source. In regulated health communications, the verification isn’t overhead. It’s the job.
Next: 14. Zapier: just describe it — using Copilot and Canvas to build automations by describing what you want, instead of wiring every step by hand.
Zapier for health communications is a practical series. New post every week.
— Ned