AI Agents Embed Outputs into Workflows
Wade Foster, co-founder & CEO of Zapier, on AI agent orchestration
The key shift is that AI winners will not just answer in chat, they will drop outputs into the exact work surface where a person can act on them. In Zapier’s world, that means an agent might turn a Gong transcript, Salesforce fields, web data, and enrichment signals into a coaching note, a case study draft, or an approval step in Slack, instead of making someone read a long chat reply and manually translate it into work.
-
Zapier has long been strongest as the logic layer between apps, moving data step by step across thousands of tools. The AI extension of that is not a chatbot replacing software, but orchestration that decides when the output should be a memo, dashboard, thumbs up prompt, or background action inside the tools people already use.
-
This lines up with how automation platforms are actually used. The sticky pattern is not conversation for its own sake, it is removing repetitive work in a live workflow. n8n describes the same dynamic, users connect data sources, run AI on top, and send the result into another system so the next task happens automatically.
-
The strategic advantage for Zapier is distribution into existing work surfaces. With connections to 8,000 apps, plus years of workflow data and a growing enterprise business, it can place AI inside email, spreadsheets, databases, and Slack approvals, where teams already review and approve work, instead of asking them to live inside one general purpose assistant.
The next wave of AI software will look less like a universal chat window and more like software that quietly builds the right interface for each job. That favors orchestration layers like Zapier, because the company that already sits between systems is in the best position to decide where an AI result should land, and what human action should happen next.