How I AI

How one designer led an AI revolution at Pendo: The paternity leave epiphany | Brian Greenbaum

Brian Greenbaum is a Senior Staff Product Designer at Pendo who led a company-wide AI transformation after a personal epiphany while on paternity leave. After experiencing the power of AI coding tools firsthand, he created a structured approach to help his entire product organization adopt AI. In this episode, Brian shares his complete playbook for driving AI adoption across teams, measuring success, and navigating the organizational challenges that come with new technology adoption. *What you’ll learn:* 1. The exact Slack message Brian sent while on paternity leave that kickstarted his company’s AI transformation 2. How to structure both synchronous and asynchronous AI learning opportunities for maximum adoption 3. The two-pronged approach that dramatically increased AI tool usage across teams 4. Why becoming your company’s AI champion is one of the best career moves you can make right now 5. How to measure AI adoption success with sentiment surveys and clear metrics 6. The critical role of creating a “golden path” for AI tool usage with legal, security, and finance teams *Brought to you by:* Google Gemini—Your everyday AI assistant: https://ai.dev/ Lovable—Build apps by simply chatting with AI: https://lovable.dev/ *In this episode, we cover:* (00:00) Introduction to Brian Greenbaum (01:38) Brian’s paternity leave epiphany that sparked an AI initiative (05:00) Sending the message that launched a transformation (12:25) The two-pronged approach: synchronous and asynchronous learning (17:29) Encouraging experimentation and creative exploration (18:41) How AI enables designers to move beyond MVP thinking (22:00) Quick summary of the two-pronged approach (24:43) Measuring AI adoption (33:48) Creating a centralized AI knowledge center (35:58) Building an MCP server to demonstrate AI’s potential (44:08) Why technical understanding is crucial for non-technical roles (46:01) Final thoughts *Tools referenced:* • Cursor: https://cursor.com/ • Bolt.new: https://bolt.new/ • Claude: https://claude.ai/ • ChatGPT: https://chat.openai.com/ • Midjourney: https://www.midjourney.com/ • Gemini: https://gemini.google.com/ *Other references:* • Pendo: https://www.pendo.io/ • Confluence: https://www.atlassian.com/software/confluence • Slack: https://slack.com/ *Where to find Brian Greenbaum:* LinkedIn: https://www.linkedin.com/in/briangreenbaum/ *Where to find Claire Vo:* ChatPRD: https://www.chatprd.ai/ Website: https://clairevo.com/ LinkedIn: https://www.linkedin.com/in/clairevo/ X: https://x.com/clairevo _Production and marketing by https://penname.co/._ _For inquiries about sponsoring the podcast, email jordan@penname.co._

BGBrian GreenbaumguestCVClaire Vohost

Dec 22, 2025·47m·Watch on YouTube ↗

📖 CHAPTERS

1. 1

   Brian Greenbaum’s paternity-leave prototype that revealed AI’s leverage

   Brian recounts trying Cursor during paternity leave and rapidly building a working mobile prototype he couldn’t have coded alone. That “wow” moment clarified AI’s potential for designers and became the spark for a broader internal push at Pendo.

2. 2

   The Slack message that kicked off a product-org AI transformation

   Still on leave, Brian sent a long-form Slack message to product leadership proposing a cross-functional AI upskilling initiative. He framed AI enablement as both a productivity/quality advantage and a strategic market-positioning move for Pendo.

3. 3

   Why raising your hand to lead AI adoption is a rare leadership opportunity

   Claire highlights that early AI change agents can create outsized cross-functional impact and career acceleration. Brian confirms the initiative opened internal doors, visibility, and access to high-impact projects—despite being an IC.

4. 4

   Design prototypes that match real data: moving beyond Figma-only limits

   Brian explains why code-based prototypes enabled by AI matter for Pendo’s analytics-heavy workflows. Dynamic, data-like interactions improve communication, validation, and decision-making compared to static mockups.

5. 5

   The two-pronged adoption engine: synchronous rituals + asynchronous sharing

   Brian’s adoption strategy combines scheduled learning time with always-on knowledge exchange. This structure addresses the most common blocker—“I don’t have time”—and normalizes continuous experimentation.

6. 6

   Kickoff workshop: hands-on vibe coding together (Bolt.new exercise)

   In an early session, Brian had everyone build the same simple app in Bolt.new using the same prompt, then compare results. The team experienced variability, errors, and iterative troubleshooting in real time—building confidence and fluency.

7. 7

   Rebuilding the imagination muscle: escaping permanent MVP mode with AI

   Claire and Brian discuss how constant scope constraints have trained teams to aim only for minimal viability. AI makes it cheaper to explore “magic” product moments—visual polish, interaction, media, and delight—and retrains teams to ask for more.

8. 8

   Asynchronous wins: sharing experiments like animated assets (Midjourney video)

   The Slack channel becomes a showcase for small, high-leverage experiments—like an animated character concept for an onboarding screen. These artifacts demonstrate how AI reduces the friction of adding craft and personality to products.

9. 9

   Tracking momentum: adoption measurement with an AI sentiment baseline

   To understand readiness and friction, Brian’s cross-functional group ran a baseline sentiment survey and repeated it later. Scores improved across dimensions, especially around awareness of AI policies and available tools—showing measurable progress.

10. 10

    Operationalizing the ‘golden path’: an AI Knowledge Center for tools and data rules

    Pendo built a centralized Confluence hub listing approved AI tools, access steps, and data-sharing rules. This reduces shadow IT, accelerates safe experimentation, and creates a repeatable process with IT/security/legal/procurement.

11. 11

    Lightning project: Brian builds an MCP server to prove product value (not code quality)

    Brian describes building an MCP server prototype that lets an LLM query Pendo data via APIs and generate dashboards. The demo made a previously abstract concept tangible, influenced leadership, and helped accelerate roadmap thinking around agents.

12. 12

    Why non-technical roles need technical understanding in the AI era

    Brian argues designers and PMs must understand how LLMs, agents, and protocols work to propose meaningful solutions. The analogy: architects don’t do the plumbing, but must understand constraints to design functional buildings.

13. 13

    Getting unstuck: practical prompting tactics when AI isn’t cooperating

    When the model loops or fails, Brian shifts strategy: he asks it to consider alternative approaches and generate multiple solutions. The goal is to break it out of a single ‘groove’ and regain forward progress.