How to Build An AI Native PM Operating System with Mike Bal, Head of Product at David's Bridal
The Growth Podcast
Composable stacks and one-off APIs
Mike explains composable architectures, one-off API integrations, and flexible tool choices per project needs.
Today’s Episode
Most PMs are drowning in tools.
You log into JIRA. Then Figma. Then Confluence. Then Notion. Then Google Analytics. Then Slack.
Twenty different tabs. Twenty different logins. Zero flow state.
Mike Bal runs product at David’s Bridal, a company undergoing massive digital transformation.
And he operates from a single interface.
Cursor and Claude Desktop sit at the center. Everything else connects through MCP and custom integrations.
Research? Manus feeds into Claude. Analytics? Clarity exports into Cursor. Design? Figma pulls directly into his projects.
This isn’t a tool stack. It’s an operating system.
Today, Mike shows you exactly how to build it.
----
If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.
Are you searching for a PM job? Join me + 29 others for an intensive 12-week experience to master getting a PM job. Only 9 seats left.
----
Check out the conversation on Apple, Spotify and YouTube.
Brought to you by Linear: Plan and build products like the best.
----
Key Takeaways:
1. Operating systems beat tool stacks - Stop logging into 20 different UIs. Build one central interface through Cursor and Claude Desktop that connects to everything. The composable mindset adapts to your needs.
2. MCP changes PM workflows forever - Model Context Protocol lets you connect JIRA, Figma, GitHub, Notion, Confluence through natural language. Check ticket status without opening JIRA. Compare designs without manual cross-reference.
3. Design validation takes 30 seconds now - "Find my Confluence doc about Feature X, load this Figma design, compare them and tell me what I missed." Used to take 1-2 hours of manual comparison work.
4. Manus dominates heavy research - Gives you multiple file outputs: sample CSVs, combined datasets, data sources report, quick start guide, markdown summary. All traceable back to sources. ChatGPT just gives responses.
5. Research must stay external until vetted - The "conspiracy theorist LLM" problem is real. If you automatically feed everything into your system, AI anchors to wrong information. Vet research separately, then bring validated context in.
6. PMs can build what required engineers - Mike built a colorization app for e-commerce in one morning. Migrated content to Sanity CMS in a few hours. All from natural language prompts in Cursor.
7. Context switching kills productivity - Every time you open a new tab, you lose flow state. The operating system keeps you in one interface. The AI handles the context switching for you.
8. Corporate IT restrictions become irrelevant - You already have Cursor or Claude Desktop. You already use JIRA, Figma, GitHub. Connect them through a better interface. No new tool approvals needed.
9. Analytics workflows save massive time - Export Clarity data, upload to Cursor, prompt "analyze drop-offs and create visualizations." Takes 10 minutes vs hours of manual Excel work.
10. AI native PMs think in prompts - "What do I need to do? What are the steps? What tools will help?" Treat AI as an extension of yourself, not a separate tool to learn.
----
Where to Find Mike
* Youtube
* Website
----
Related Content
Newsletters:
* Product Requirements Documents
Podcasts:
* Everything You Need to Know About AI
PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!
This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribe
The AI-powered Podcast Player
