Bridging the AI gap: Why production-ready systems require disciplined R&D, not just better tools
By
Liz Boschee, Capital One
A good honest bake. Not flashy, but you'll finish the whole bagel.
Summary
This article discusses the common challenge enterprises face when moving AI from experimental prototypes to reliable production-scale systems. Drawing from experience at Capital One's AI Foundations organization, the author argues that successful AI implementation requires a disciplined R&D approach that bridges foundational research and real-world deployment. The piece emphasizes that leaders should invest not just in tools, but in robust R&D processes and cultural foundations that enable responsible scaling of innovation.
Key quotes
· 4 pulledEnterprises aren't struggling to experiment with AI; they're struggling to make it work in the real world.
Moving from promising prototypes to reliable, production-scale systems is where most efforts stall.
Successful AI implementation isn't just about adopting the latest models or tools.
It requires a disciplined R&D approach that connects foundational research to real-world systems, and holds ideas accountable as they move from concept to production.
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