How Product Leaders Should Use AI, with Webflow CPO Rachel Wolan

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Nov 30, 2025

Rachel Wolan, Chief Product Officer at Webflow, dives into AI product leadership, sharing insights from her journey in shaping AI-native features. She highlights the importance of self-serve tools for leaders and demonstrates how her calendar and email agents streamline productivity. Rachel also discusses practical strategies for rolling out AI in organizations, adjusting career ladders to incorporate AI skills, and emphasizes robust evaluation processes to support impactful launches. Her expertise offers a glimpse into the future of product management.

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INSIGHT

IDE-Based Agents For Fast Analysis

Running agent workflows from an IDE like Cursor turns leaders into practical practitioners with terminal-driven tasks.
Using monorepo context and Claude Code gives you rapid analysis and content understanding.

INSIGHT

Leader-As-IC Mindset

ICCPO means leaders can self-serve answers to nearly any question without waiting on others.
Modeling hands-on IC work inspires teams to experiment and learn faster.

ADVICE

Use Agents To Recommend, Not Act

Build agentic workflows (calendar, email) to triage and recommend actions, not to act autonomously.
Keep agents as recommenders so you approve actions and avoid unwanted autonomous sends.

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Today’s Episode

There are tons of tutorials about Claude Code and Cursor for IC PMs.

But what about leaders?

Today’s episode is a masterclass on both sides of AI product leadership. How to be a productive AI leader and how to ship AI-native features at scale.

Rachel Wolan is the Chief Product Officer at Webflow, the $4 billion company powering TED Talks, SoundCloud, and Reddit.

Rachel walks through building her agentic Chief of Staff live, sets up a LinkedIn post generator from scratch, and shares the brutal lessons from launching Webflow’s AI app generator.

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Brought to you by:

Linear: The task management platform dethroning Jira

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Key Takeaways:

1. IC CPO means self-serving answers - "As a leader, you are able to get your own answers to practically any question." No waiting on data scientists. No back-and-forth with analytics. You have tools to self-serve insights, make analysis, automate workflows. Model behavior for your team to inspire them.

2. Calendar agent analyzes time - Runs weekly with prompt: "Analyze my calendar for last two weeks. Where could I delegate?" Returns delegation opportunities, red flags (double bookings, context switching), what to cut next week. Rachel gives output to EA. Spot on when shown live.

3. Email agent watches behavior - Complete inbox access. Runs triage, archives junk (calendar notifications, marketing), pins important messages, creates draft replies. Twist: watches behavior. If email sits too long, it notices. Caught meeting missing link. Rachel's rule: agent recommends, she approves. No autonomous sending.

4. Analytics agent via MCP - Connected Claude Code to Snowflake via MCP servers (not officially supported repos, just fed them to Claude Code). Ask natural language questions, get SQL executed real-time. "How many sites does Shirts.com have?" Claude writes query, authenticates via SSO, returns answer. Data scientist in pocket.

5. Accept the adoption curve - Your org follows standard curve: early adopters, early majority, late adopters, laggards. Create pathways for everyone to ascend ladder at their pace. Don't force everyone to be you. Rachel to team: "I only want to see prototypes when you have meetings with me." Creates culture investing in prototype quality.

6. Builder Days strategy - Give everyone access: Claude Code licenses, MCP to Snowflake/Tableau, Figma Make, Cursor with design system. Run Builder Days where champions help others through technical hurdles. Everyone demos something outside comfort zone. Results: 0% to 30% of designers using Cursor weekly after first Design Builder Day.

7. Rewrite career ladder - Webflow rewriting career ladder to make AI-native work an expectation, not nice-to-have. Create right incentives. Make sure people supported. Avoid AI for AI's sake. Example: Two designers built similar prototypes. Director caught early: "Go harmonize your prototypes now." Easier now than late in product cycle.

8. MVO before MVP framework - Most teams: Feature → PRD → Design → Ship. Rachel flips it. MVO (Minimal Viable Output) before MVP. Get model's output right FIRST using RAG, prompt engineering, context engineering. Only then build feature. "If you don't have desired outputs, don't spend time productizing the AI feature."

9. Evals are now your job - Brutal story: Webflow's AI app generator 2 weeks from launch. Rachel tested it. Agent kept dying. Realized: changed underlying model, evals didn't have coverage. Evals = test cases for models. Want dream evals (should pass) and edge cases (should fail). Use BrainTrust. Teaching PMs to write evals is part of AI PM toolkit now.

10. Build on your strengths - Framework: See trend → Is it applicable to customers? → What's YOUR core competency? Webflow's strength: bringing visitors to front door via CMS. Built production-grade app generator (not prototype like Lovable). Uses your brand, CMS, hosting, security. "We're bringing a way to prompt an app to production." Don't copy trends, leverage unique strengths.

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Where to Find Rachel Wolan

* LinkedIn

* Website

* X

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Related Content

* Claude Code Tutorial for AI PMs

* AI Agents for PMs in 69 Minutes, with IBM VP

* 5 AI Agents Every PM Should Build, with CEO of Lindy

Newsletters:

* AI Evals Guide for PMs

* Prompt Engineering for AI Agents

* AI Agents: The Ultimate Guide for PMs

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PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps

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