Make with Notion 2025: A Day in the Life with Your New AI Team

Notion’s new “AI team” is built to cut the hidden time sink of “work about work”—the note cleanup, context hunting, and tool-hopping that steals hours from actual execution. Instead of sending users to another tab or login, the AI teammates live inside Notion and work from the same workspace context where projects, decisions, and documents already exist. The result is less chasing for answers and more immediate follow-through: meeting notes turn into action items, action items become tasks, and research becomes first drafts that can be edited and shared.

The demo frames the problem with familiar workplace friction. People leave meetings busy but unsure what changed, then spend time tidying notes and chasing follow-ups. Quick questions also rarely stay quick: clarifying requests trigger a scramble through old threads, docs, and emails to reconstruct context. On top of that, many standalone AI apps generate outputs that feel generic or even wrong because they lack access to the team’s internal decisions and priorities. Notion’s pitch is that AI becomes genuinely useful only when it can “see” the work—using connected apps and workspace knowledge—so answers are synthesized rather than stitched together.

Three AI teammates anchor the workflow. First, AI meeting notes transcribes conversations and produces word-for-word transcripts, structured notes, and summaries with action items. In the onboarding scenario, Holly starts an AI meeting notes session during a one-on-one, then later reviews the transcript, the notes (including captured context like “check pilot timeline next week”), and a summary that lists takeaways and next steps—eliminating manual tidying.

Second, enterprise search acts as a shortcut to answers across connected sources. In the demo, Holly asks what a colleague (Renee) works on, turning off web search to focus on internal knowledge. The system returns a synthesized response rather than a list of links, with clickable source materials so the user can verify details and dive deeper when needed.

Third, research mode functions as a built-in research assistant that can work in plain language. It runs multi-step research across the workspace, connected apps, and the internet, then produces a report that can be saved as a Notion page. The demo uses it for market research and customer sentiment, and then escalates to drafting a product requirement document for a GPS tracking feature. Because the output lands inside Notion, it becomes collaborative immediately—Holly can tweak the draft, share it, and add comments.

The throughline is workflow continuity: a meeting produces notes, notes produce tasks, tasks feed project planning, and research produces specs—all without context switching. The practical setup tips reinforce that the AI team’s usefulness depends on context: connect apps like Slack, GitHub, Jira, and Google Drive; use the Notion desktop app for AI meeting notes so it can capture both system audio and microphone; connect calendars for meeting prompts; and insert AI meeting notes into meeting templates when a meetings database is used. The day-in-the-life ends with a simple promise—less busy work, more head space, and work that moves forward in one place.