Functional AI Prototypes Over PRDs

The AI-Native Transformation Playbook

In the AI era, companies must fundamentally rethink how they operate to stay competitive. Howie Liu, CEO of Airtable, shares his approach to transforming a decade-old company into an AI-native organization.

The "Fast Thinking vs. Slow Thinking" Organizational Model

• Split your organization into two complementary groups:

  • Fast Thinking Group: Ships new AI capabilities on a near-weekly basis
    • Focused on rapid experimentation and iteration
    • Creates "jaw-dropping" value with each release
    • Drives top-of-funnel excitement and new use cases
  • Slow Thinking Group: Makes deliberate, premeditated bets
    • Handles complex infrastructure that can't be shipped quickly
    • Enables initial adoption seeds to grow into larger deployments
    • Provides stability and scalability for enterprise needs

• This structure allows you to:

  • Ship AI features at the pace of AI-native startups
  • Maintain the infrastructure needed for enterprise-scale deployments
  • Balance innovation with stability

The "IC CEO" Approach

• CEOs and leaders must become individual contributors again in the AI era

• Get hands-on with the technology to understand what's possible

• Reasons this matters:

  • AI is evolving so rapidly that you can't delegate understanding
  • Every software product must be "refounded" for AI
  • Product decisions require intimate knowledge of AI capabilities
  • You can't "taste the soup" without participating in creating it

• Practical implementation:

  • Cut standing one-on-ones to focus on timely, urgent topics
  • Use AI tools hourly (not just daily)
  • Experiment aggressively with compute resources
  • Lead by example by sharing your AI experiments with the team

Transforming Your Team for AI

• Encourage cross-functional capabilities:

  • PMs need to become "hybrid PM-prototypers with good design sensibilities"
  • Engineers need product thinking
  • Designers need technical understanding
  • Everyone needs a minimum baseline in all three areas

• Create a culture of AI experimentation:

  • Tell people to "cancel meetings for a day or week" to play with AI tools
  • Encourage personal projects that force deeper AI tool usage
  • Value experiential learning over theoretical understanding
  • Share experiments openly to normalize AI usage

• Shift from documentation to prototypes:

  • Prioritize interactive demos over decks or PRDs
  • Test on realistic (not just "golden path") scenarios
  • Feel the product experience directly rather than describing it

The "Watermelon" Approach to AI Opportunities

• Focus on the abundant "low-hanging fruit" in AI

  • "We have all these fruit trees with massive watermelons sitting on the ground"
  • Don't climb tall trees for hard-to-reach coconuts
  • Experiment with multiple directions simultaneously

• For early AI features, prioritize vibes over evals:

  • Start with open-ended exploration before defining metrics
  • Throw ideas against the wall to see what works
  • Only formalize evaluation frameworks after finding promising directions

The "Clean Slate" Test for AI Transformation

• Ask: "If you were founding a new company from scratch with the same mission, how would you execute using a fully AI-native approach?"
• Evaluate whether your existing assets help or hinder this vision
• If your legacy business doesn't provide an advantage, consider selling and starting fresh
• Leverage your unique building blocks that give you an edge over AI-native startups

Practical Leadership in the AI Era

• Be outcome-oriented rather than process-oriented
• Break down role silos across the organization
• Collapse dependencies so individuals can accomplish more independently
• Return to a startup mentality where everyone does what needs to be done
• Stay connected to the details that made your product successful initially