みんなが
を使っている場所は：
What an AI Product Manager actually does
An AI Product Manager is an autonomous AI worker that handles the writing and coordination side of product work. It drafts PRDs, writes user stories, keeps the roadmap current, prepares stakeholder meetings, synthesizes user feedback, and produces release notes when you ship.
It works inside Notion, Jira, and Linear, so specs link to real tickets and the roadmap reflects what engineering is actually doing. Competitive analysis and feedback synthesis draw on the web and the channels where your users already talk.
You stay the product leader; the worker does the production. Decisions, tradeoffs, and vision remain yours, while documents, updates, and prioritization inputs arrive prepared.
PMのボトルネック
プロダクトマネージャーはドキュメントと調整に時間をかけすぎています。PMが運用作業を担当するので、ビジョンと戦略に集中できます。 *
* 委任したいなら、止めませんよ...
プロダクトマネージャーがすること
自律的な製品運用とドキュメント
ステークホルダーとの議論後、ロードマップツールに直接詳細な製品要件ドキュメントを作成します。
製品ロードマップを最新に保ち、優先順位付けし、ビジネス目標と一致させます。
要件を収集し、議論を促進し、製品の方向性について全員を一致させます。
ユーザーフィードバック、市場データ、ビジネス目標を分析し、機能の優先順位を推奨します。
How it works
From raw ideas to specs, roadmap, and release notes.
- 1
Give it product context
Share your product docs, roadmap, and goals. The worker learns your domain, your users, and how you write specs.
- 2
Connect your product stack
Link Notion, Jira, or Linear so PRDs, stories, and roadmap items live where your team already works.
- 3
Feed it the inputs
Feature ideas, user feedback, support themes, and competitor moves go in; structured PRDs and prioritization briefs come out.
- 4
It keeps documents alive
Specs stay linked to tickets, the roadmap updates as reality changes, and release notes are drafted from what actually shipped.
- 5
You review and decide
Everything arrives as a draft for your call: you approve, edit, or redirect, and the worker learns your preferences.
Works inside your product stack
Your product manager writes and updates where your team plans and builds.
Notion
Jira
Linear
Google Docs
Google Sheets
Gamma
MixpanelPlus 50 more integrations, from analytics to presentation decks for stakeholder reviews.
A day in the life
A working rhythm for an AI Product Manager between two releases.
Summarizes overnight user feedback from support tickets and reviews into three themes.
Drafts the PRD for the next feature, linked to designs and open questions for you.
Updates the roadmap in Notion after two tickets slipped, and notes the downstream impact.
Prepares the stakeholder review deck with progress, metrics, and decisions needed.
Writes user stories with acceptance criteria for the sprint and files them in Linear.
Publishes release notes for today's ship and posts the internal announcement.
Compiles a competitive brief on a rival's launch, with implications for your roadmap.
実際の例
Taylorが製品ドキュメントを管理する方法
Taylor Mitch 🇺🇸
プロダクトマネージャー
“VPプロダクトと議論した後、ロードマップに直接製品要件ドキュメントを作成します。Notionを最新に保ち、SlackとWhatsAppで調整し、全員が一致したままであることを確認します。”
完全な機能リスト
プロダクトマネージャーが処理できるすべて
Frequently asked questions
What product leaders ask before delegating the production work.
What does an AI Product Manager do?
It drafts PRDs, writes user stories, maintains the roadmap, prepares stakeholder meetings, synthesizes user feedback, runs competitive analysis, and writes release notes, working autonomously inside Notion, Jira, and Linear.
Does it make product decisions?
No. It prepares the inputs (specs, analysis, synthesized feedback) and keeps documents current, while prioritization calls and tradeoffs stay with you. Everything ships as a draft for your review.
How does it learn our product?
You point it at your existing docs, roadmap, and feedback channels. It builds persistent memory of your domain, users, and writing conventions, and gets sharper with every review cycle.
Which tools does it work with?
Notion, Jira, Linear, Google Docs, Google Sheets, Gamma for decks, and Mixpanel for product metrics, among 50+ integrations.
How much does it cost?
Plans start at $50/month. Teams typically weigh it against the hours PMs spend writing and formatting rather than deciding.
Can it handle user feedback at scale?
Yes. It continuously ingests feedback from support, reviews, and sales notes, deduplicates it into themes, and quantifies them so your prioritization starts from evidence.
Explore more AI workers
Roles teams often hire alongside this one.
