In this on-demand expert briefing, MK Blake and Rachel Pena unpack why most AI quality programs struggle in real-world deployment — and what it actually takes to design and run human judgment systems across multilingual, high-risk, and evolving AI programs. 

This is not a product demo. 

It is a practical, operator-level conversation about how quality actually works in production. 

What you’ll learn 

• Why AI quality cannot be reduced to benchmarks, accuracy scores, or pass/fail metrics 
• How cultural fluency and cognition shape real user-aligned AI behavior 
• What “teaching models to speak human” looks like in practice 
• How evaluator selection, training, and feedback loops prevent quality drift 
• Where quality breaks when speed is prioritized without human systems in place 
• Why localization was the original AI quality discipline — and why it matters now 
• What enterprises must rethink as they scale agentic and multimodal systems 

Speakers

• MK Blake, VP, Global Ops & Quality, Welo Data
  • Leads global operations, quality, analytics, learning and development, and assessments, with a background in localization and multilingual delivery. 
• Rachel Pena, Growth Marketing Manager, Welo Data 
  • Leads go-to-market strategy for Welo Data, translating AI quality concepts into operational realities for enterprise teams. 

Why this conversation matters

As AI systems move from experimentation into production, quality failures become harder to diagnose and more expensive to fix. 

This briefing explores why: 

• Human judgment is not an interchangeable input 
• Cultural nuance cannot be automated away 
• Drift emerges when feedback loops break 
• “Faster and cheaper” approaches often produce brittle systems at scale 
• And why the future of AI quality depends on intentional human systems — not just better tooling. 

Who this is for 

• AI / ML leaders scaling models into production 
• Trust & Safety and Responsible AI teams 
• GenAI program owners managing evaluator consistency 
• Enterprise teams deploying multilingual or agentic systems 
• Buyers evaluating AI data partners beyond surface-level claims 

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Want to talk about your quality system?