This Company Created a New Job Title to Make AI Actually Work—Here's Their Exact Playbook

The Problem

Every company knows they should be "doing something with AI." But knowing you should use AI and actually getting hundreds of employees to use it effectively are two very different things.

The result is what you might call "AI theater"—companies announce AI initiatives, buy expensive tools, but see little actual change in how work gets done.

The Solution Explained

Webflow's answer was surprisingly straightforward: make AI adoption someone's entire job.

Anvita Gupta, who leads product at Webflow, created a new position called "AI Chief of Staff" and recruited Pavlin Chimik directly from OpenAI to fill it. Think of this role like an internal consultant who wakes up every day asking one question: "How can AI make our teams more effective?"

But here's what makes it work—this isn't a solo mission. They built a support system around it:

An AI Council: A small group that meets regularly to review AI projects, share what's working, and make decisions about which tools get company-wide adoption.

Human-in-the-loop systems: Every AI feature has a human checkpoint. AI might draft a customer support response, but a person reviews it before it goes out.

Custom testing for AI quality: They built ways to measure when AI gets things right and wrong, so they can catch problems before customers do.

How It Actually Works

The playbook breaks down into four phases:

Phase 1: Start ridiculously small Forget the grand AI transformation. Pick one annoying, repetitive task and see if AI can help. Webflow started with things like summarizing customer feedback and drafting initial code snippets—low-stakes experiments where mistakes wouldn't be disasters.

Phase 2: Hire for curiosity, not credentials The expert advice here contradicts what many companies do: don't rush to hire expensive AI specialists. Instead, find people who are naturally curious about new tools and good at teaching others. Specialists can come later once you know what you actually need.

Phase 3: Build internal education Webflow runs regular sessions where teams share AI wins (and failures). This normalizes experimentation and helps spread knowledge organically. When one team figures out a great workflow, others can copy it.

Phase 4: Create governance that enables, not blocks The AI Council doesn't exist to say "no"—it exists to help teams say "yes" safely. They've developed guidelines for when human review is required, how to handle AI mistakes, and which use cases are off-limits.

Real Examples

Customer Support Triage Before: Support tickets sat in a queue until a human could read, categorize, and route them. After: AI reads incoming tickets, suggests a category, drafts an initial response, and routes to the right specialist. Humans still review and send the final response, but they're starting 50% ahead.

Design Prototyping Before: Creating a new design concept meant hours in Figma, starting from scratch. After: Designers describe what they want, AI generates a starting point, and designers refine from there. What took a week now takes a day—a 10x improvement in speed.

Code Generation Before: Engineers wrote every line from scratch or spent time searching documentation. After: AI handles boilerplate code and suggests solutions. Engineers report spending more time on interesting problems and less on repetitive typing.

The key insight across all these examples: AI didn't replace anyone. It handled the boring parts so humans could focus on the parts requiring judgment, creativity, and expertise.