Join us in the AI Product Kitchen this week as we talk to Ilan Frank, Chief Product Officer at Checkr. We explore the realities of building AI products in highly regulated industries, the counterintuitive challenges of AI implementation, and why most product teams are still in the early innings of the AI transformation. Ilan brings a wealth of experience from his previous roles as VP of Product at Slack (where he launched Slack Connect) and Head of Product at Airtable, giving him unique insights into scaling AI product organisations across different contexts.

Learn about the practical challenges and unexpected discoveries that come with deploying AI in mission-critical applications, as Ilan shares honest insights on moving beyond the ChatGPT magic to building AI features that solve real customer problems.

In this episode, we dive into:

• The 30-Inning Game of AI Product Development: Why most teams are only in inning 2 or 3 of AI adoption, what separates teams shipping AI products successfully, and the long journey ahead to truly AI-native organisations.
• Hiring More Humans Because of AI: The counterintuitive reality of how Checkr actually increased headcount in the short term due to AI implementation, requiring specialists to validate AI outputs.
• Building AI Products in Highly Regulated Industries: The critical importance of guardrails, compliance considerations, and why “we’re wrong only 1% of the time” isn’t acceptable when employment decisions are on the line.
• From Hack Day Brilliance to Production Reality: How a promising AI feature required careful reconsideration to ensure regulatory compliance, and the gap between AI demos and production-ready products.
• Data Moats and AI Strategy: How Checkr is transforming from a background check API into a comprehensive people intelligence platform, leveraging their unique data collection infrastructure to build sustainable competitive advantages in the AI era.

Here’s how to connect with us:

• Find Ilan on LinkedIn⁠
• Follow Matt on LinedIn⁠ and ⁠X⁠
• Learn more about Checkr
• And make sure to check out Sauce AI⁠ and follow us on LinkedIn⁠!

Timestamps:

00:00 – Introduction to Ilan Frank and the 30-Inning Game of AI

01:02 – What Separates Successful AI Product Teams: Overcoming Fear

01:51 – Checkr’s First AI Product: Charge Classification Success

03:00 – The Journey from Manual to AI-Powered Background Checks

04:36 – Customer-Driven Innovation: Speed vs Accuracy Trade-offs

06:18 – The Second AI Product: Charge Explainer Development

07:20 – When AI Features Go Wrong: Legal Compliance Challenges

09:05 – Identifying Problems Through Customer Feedback

10:18 – Building AI in Highly Regulated Industries: Zero Error Tolerance

11:11 – Human-in-the-Loop: Why AI Increased Hiring at Checkr

12:01 – Operations Specialists: The New AI Quality Assurance Role

12:42 – Future of AI Confidence: When Human Review Won’t Be Needed

13:33 – Checkr’s Evolution: From API to Comprehensive Platform

15:58 – The Data Advantage: Building Moats in Background Checks

16:52 – TAM Analysis: Expanding Beyond Background Checks

17:43 – AI Implementation Challenges: Magic vs Production Reality

20:11 – Hiring AI Product Managers: Skills and Imagination Over Experience

22:28 – Why AI Hasn’t Transformed Product Management Yet

25:04 – Designing AI-Native Organizations for the Future

27:27 – The Next Five Years: Automation vs Human Oversight

32:08 – Competitive Threats: Startups vs Incumbents in AI

33:01 – Slack Connect and Enterprise Product Lessons

39:34 – Airtable’s AI Strategy: Builders vs End-Users Decision

42:55 – Product Leader Advice: Just Do It and Focus on Pain Points

44:21 – Internal AI Tools: Design, Research, and Product Management

46:09 – The Unseen Pain: What AI Can’t Yet Discover

48:09 – Product Beliefs: Being an AI Skeptic While Embracing AI

48:39 – Looking Ahead: Building the Human Data Graph