How I AI: Brian Greenbaum's 3-Step Playbook for Driving Company-Wide AI Adoption

Most How I AI episodes focus on specific workflows for building things. But a question I hear constantly is: "How do I get my whole team to actually use this stuff?"

Brian Greenbaum, a product designer at Pendo, figured this out. He built a step-by-step program that got his product and design teams genuinely excited about AI. The story starts, surprisingly, while he was on paternity leave playing with an AI coding tool.

In this episode, Brian shares everything: the exact Slack message he sent to leadership, how he structured interactive workshops, and the framework he used to measure results. If you want to lead your team's AI adoption, this is the playbook.

Workflow 1: Starting the Initiative

Step 1: Have a Personal Experience Worth Sharing

On paternity leave, Brian started playing with Cursor, an AI code editor. He had an idea for a music app—scan a QR code on a physical card to play an album on Spotify, like a modern record player. He's not a developer, but:

"I pulled up Cursor and within a couple hours I had a working prototype. I was creating QR codes, PDFs, doing all this really cool stuff."

At Pendo, Brian works on analytics features where creating realistic data-driven prototypes in Figma is a constant challenge. He realized he could use tools like Cursor to build high-fidelity, code-based prototypes that communicate ideas far better than static mockups.

Step 2: Make the Case to Leadership

While still on leave, Brian drafted a Slack message to his manager, their manager, the CPO, and other AI enthusiasts. He framed it with a clear business case:

1. Internal efficiency: The product team could "leverage AI tools to get more done in fewer hours, improve decision making, and communicate ideas more effectively."
2. External positioning: By becoming proficient in AI, Pendo could better serve customers going through similar transformations.

The CPO immediately asked him to present at the next all-hands. He had the buy-in to start a formal initiative.

Workflow 2: Building the Program

Step 1: Mix Scheduled Sessions with Ongoing Conversation

The biggest barrier to AI adoption is time. Everyone knows it's important, but they're too busy to figure it out. Brian's solution: create both dedicated calendar time and a space for continuous learning.

• Bi-weekly "Product AI" sessions: Interactive meetings designed to get people's hands dirty, not just listen to presentations.
• Public Slack channel: A hub for sharing articles, experiments, and questions—what Brian calls "radical many-to-many sharing."

Step 2: Make Sessions Hands-On

For his kickoff, Brian had everyone actually use AI, live. He designed a simple exercise using bolt.new:

1. Same prompt, different results: Everyone pasted the same prompt to create a to-do app.

The eye-opener? Even with identical prompts, the AI generated wildly different apps. Some had errors—which became a teachable moment about iteration.

2. Creative exploration: Brian encouraged people to "go wild" with modifiers like "add a retro 8-bit pixel art theme" or "make it look like MySpace from 2007." People laughed, experimented, and saw the creative potential.

This hands-on approach made AI feel accessible. The Slack channel kept the conversation going after meetings ended. They even saw designers using Midjourney to create animated UI characters—something too time-consuming before.

Workflow 3: Measuring and Scaling

Step 1: Track Sentiment

As part of a company OKR to improve AI adoption, Brian's group sent out a baseline survey asking about:

• Personal sentiment toward AI's impact
• Familiarity with company AI policies
• Awareness of which tools were available

They ran the survey at the start and end of the quarter. After implementing their programs, they saw significant improvements across all metrics—especially awareness of policies and available tools.

Step 2: Create Clear Guidelines

The survey revealed a big gap: people were using personal ChatGPT accounts and didn't know what data was safe to use or which tools were approved. Brian's team worked with legal, security, IT, and finance to create an AI Knowledge Center that included:

• An alphabetized table of approved AI tools
• Clear data-sharing guidelines for each (e.g., "Internal Data Only," "No PII")
• Security and legal status
• A process for requesting access or new tool evaluations

This replaced confusion with clarity. People could experiment safely instead of operating in the shadows.

The Result

Brian used his new skills to build a prototype MCP server connecting to Pendo's public APIs. He recorded a demo showing how he could use natural language in Claude to generate interactive dashboards of product usage data.

This caught the CTO's attention and directly influenced Pendo's roadmap, accelerating development of internal AI agents.

The Playbook

Brian's approach comes down to three things:

1. Start with a personal experience that shows clear value, then make the case to leadership
2. Structure a program with regular hands-on sessions plus ongoing async discussion
3. Create a "golden path" with clear guidelines, approved tools, and measurable outcomes

If you have the initiative to lead this at your company, it's a real opportunity. Take this framework, adapt it, and run with it.

Sponsors

Thanks to our sponsors:

• Google Gemini – Your everyday AI assistant
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Episode Links

• Watch on YouTube
• Listen on Spotify
• Listen on Apple Podcasts

Find Brian Greenbaum on LinkedIn. Check out ChatPRD for more AI product resources.