50 - Cracking the Code of Data Labeling: Key Strategies for High-Quality AI
The Future of AI Depends on Data Quality, Not Just Model Size
In this episode of the AI & Data Driven Leadership Podcast, Dean Guida sits down with Michael Abramov, Co-Founder and CEO of Keylabs, to unpack a critical but often misunderstood reality of artificial intelligence: AI systems succeed or fail based on the quality, diversity, and evolution of their data. Michael shares practical insight from working with enterprise AI teams, explaining why disciplined data creation and annotation strategies are now a leadership priority—not just a technical concern.
Building AI Systems That Survive the Real World
Michael explains that most AI performance issues stem from narrow or outdated datasets rather than weak algorithms. Models trained on limited environments often break when exposed to new geographies, changing conditions, or edge cases that weren’t represented during training. Without sufficient diversity in data, AI systems struggle to generalize, making them fragile once deployed outside controlled settings.
Equally important is the idea that data must continuously evolve. The real world changes—products, infrastructure, behavior, and environments shift over time. AI teams that treat datasets as static assets see model accuracy degrade, while high-performing organizations establish feedback loops that continuously collect, label, and retrain data based on real-world performance signals.
The conversation also highlights the balance between automation and human expertise. While AI-assisted labeling can dramatically improve speed and scale, human judgment remains essential for ambiguity, edge cases, and quality control. Sustainable AI systems rely on disciplined processes that combine automation, clear quality standards, and expert oversight—especially in complex domains like computer vision and autonomous systems.
About Michael Abramov
Michael Abramov is the Co-Founder and CEO of Keylabs. He specializes in building scalable data creation and annotation systems that help AI teams move from experimentation to production with confidence. His work focuses on aligning data strategy with real-world AI performance.
About Keylabs
Keylabs is a data annotation and data creation platform supporting AI teams across computer vision, video, and emerging 3D data types. Keylabs partners with enterprise and high-growth AI organizations to deliver production-grade datasets through a combination of automation, human expertise, and rigorous quality assurance.
Links Mentioned in This Episode
Key Episode Highlights
Why data quality is the primary driver of AI performance
The risks of static datasets in dynamic environments
How continuous feedback loops improve AI accuracy
Where automation helps—and where humans are still essential
What production-ready AI data operations really require
Conclusion
This discussion reinforces a foundational leadership lesson: AI maturity isn’t achieved by chasing bigger models, but by building smarter data systems. Organizations that invest in data quality, diversity, and continuous improvement are far better positioned to deploy AI that adapts to the real world and delivers long-term value.
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