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Yaozhong Kang

\# Yaozhong Kang

Yaozhong Kang is an engineer, founder, and AI systems strategist with more than 11 years of experience building production software and scalable technology solutions. He is best known for helping companies apply AI in ways that drive measurable business outcomes, particularly across growth, revenue, and customer operations.

Over the course of his career, Yaozhong has worked at the intersection of engineering, systems design, and business operations, helping organizations move beyond AI pilots and disconnected tools toward fully integrated solutions that operate at scale. His work focuses on transforming fragmented data, workflows, and decision-making processes into unified systems that AI can effectively support and automate.

Yaozhong specializes in helping sales, marketing, and customer teams build operational infrastructure that enables smarter decision-making, improved efficiency, and more consistent execution across the business. Rather than approaching AI as a standalone product, he focuses on designing systems where data, processes, and teams work together in ways that make AI genuinely useful in day-to-day operations.

Known for his practical and systems-oriented approach, Yaozhong helps organizations identify where AI can create real leverage and how to implement it sustainably. His combination of engineering expertise and operational insight makes him a trusted advisor for companies looking to scale AI adoption beyond experimentation and into core business functions.

Session rate
$750 per 1-hour session

Expert profile

Engineer-founder; AI systems for revenue operations at scale

What this session is built for

Bring a concrete AI workflow, decision, or implementation challenge. This session is designed for focused diagnosis, practical options, and next steps you can use immediately.

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Example questions you might ask

  • What does responsible AI deployment look like for a mid-market or enterprise company?
  • Where are the high-leverage AI applications most companies miss?
  • What is the right way to handle AI-driven decisions in regulated or high-stakes contexts?
  • How do we avoid building AI workflows that break when models change?