Webflow CPO: "I Run My Whole Day in Claude Code + Cursor"
How Webflow's Chief Product Officer uses AI coding tools as her primary work environment — a window into executive-level AI-assisted productivity.
A CPO's Daily AI Workflow
Rachel Wolan's daily workflow is a glimpse into how senior product leaders are integrating AI coding tools — not as a novelty, but as their primary work environment.
Her day starts in Claude Code, writing and refining product specs with AI assistance. Mid-day, she moves to Cursor for prototyping — testing product ideas in code rather than mockups. Afternoons involve code review where she uses Claude Code to understand implementation details she'd previously need an engineer to explain.
The key shift: for a CPO, AI coding tools aren't about writing production code. They're about closing the gap between product vision and implementation understanding. When the CPO can prototype and review code, product decisions are better informed and communication with engineering is more precise.
Tools and How She Uses Them
Claude Code for Specs and Strategy
Rachel uses Claude Code to draft product specs, analyze competitive products, and think through technical feasibility. The conversational interface works well for the exploratory, strategic thinking that CPOs do daily.
Cursor for Prototyping
When a product idea needs validation, she builds a quick prototype in Cursor. This replaces the traditional 'describe the idea → wait for engineering to build it → evaluate' cycle with a much tighter feedback loop.
Both Tools for Code Review
During PR reviews, she uses Claude Code to explain complex changes and Cursor to navigate the codebase. This lets her provide substantive product feedback on implementation, not just surface-level UI comments.
Impact on the Product Org
When the CPO uses AI coding tools, it changes the entire product organization's dynamic:
Faster decision-making: Product decisions that used to require engineering estimates and prototype sprints can now be validated in hours. Rachel can prototype an idea, test it, and decide whether to pursue it — all before the next standup.
Better specs: Product specs written with AI assistance are more technically grounded. When the CPO understands implementation constraints firsthand, specs are realistic from the start.
Engineering trust: Engineers respect product leaders who understand the code. Rachel's ability to read and discuss implementation details builds trust and improves collaboration.
The broader lesson: AI coding tools aren't just for engineers. They're for anyone who makes decisions about software — and that includes product leaders, designers, and founders.
