#559 AI Without the Black Box: Nat Natarajan on Building Trust at Global Scale

In this episode, Mehmet Gonullu sits down with Nat Natarajan, Chief Operating Officer and Chief Product Officer at Globalization Partners, to explore what it really takes to deploy AI in highly regulated environments.

From labor laws and compliance across dozens of countries to human-in-the-loop AI systems, Nat shares how Globalization Partners built explainable, trustworthy AI that enterprises can actually rely on. This is a grounded, operator-level conversation on moving beyond AI hype toward real productivity and trust.

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👤 About the Guest

Nat Natarajan is the Chief Operating Officer and Chief Product Officer at Globalization Partners, a pioneer in global employment solutions. He previously held senior leadership roles at companies including TurboTax (Acquired by Intuit), PayPal, RingCentral, Ancestry.com, and Travelocity. Nat brings decades of experience at the intersection of technology, regulation, and large-scale enterprise systems.

https://www.linkedin.com/in/natrajeshnatarajan/

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🧠 Key Takeaways

• Why black-box AI fails in regulated industries

• How human-in-the-loop design builds trust and adoption

• The role of proprietary, vetted data in enterprise AI

• Where general-purpose LLMs fall short for compliance-heavy use cases

• Why AI should augment humans, not replace them

• How CHROs and boards are rethinking AI as a “digital workforce”

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🎯 What You’ll Learn

• How to design AI systems that can explain their decisions

• When to keep humans in the loop and when automation works best

• How enterprises can deploy AI responsibly without slowing innovation

• What makes AI adoption succeed inside large, global organizations

• Why regulated complexity is an advantage, not a blocker, for AI

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⏱️ Episode Highlights & Timestamps

• 00:00 – Introduction and Nat’s background

• 02:00 – Why regulated environments are ideal for AI, not hostile to it

• 05:00 – Lessons from TurboTax and encoding legal reasoning into systems

• 08:00 – Designing AI that avoids the black-box problem

• 12:00 – Human-in-the-loop systems and guardrails

• 16:00 – Why proprietary data beats generic models

• 19:00 – Enterprise vs startup AI adoption dynamics

• 23:00 – AI as a collaborator inside HR teams

• 27:00 – Explainability, trust, and employee-facing AI

• 32:00 – The CHRO’s role in an AI-powered workforce

• 36:00 – From hype to real productivity with agentic AI

• 40:00 – Final thoughts and advice for leaders adopting AI

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📚 Resources Mentioned

• Globalization Partners : https://www.globalization-partners.com/

• GIA: http://www.g-p.com/gia

• Prediction Machines (Updated & Expanded Edition) – referenced by Mehmet