Presented by Capital One Enterprises 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.In my role within Capital One’s AI Foundations organization, I’ve seen firsthand that 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.That’s harder than it sounds. AI capabilities are evolving quickly, but enterprise environments can be complex, fragmented, and risk-minded. The question isn’t just what’s possible, but what actually works — for a specific workflow, user, or decision — with today’s technology and constraints.What follows reflects how organizations can turn AI ambition into production reality through a more deliberate approach to research, evaluation, and deployment.Bridging foundational and applied researchDelivering impactful AI requires closing the gap between cutting-edge research and practical, real-world use cases. When research exists in an academic vacuum, untethered from operational reality, models that may perform well in an offline environment often fall short when faced with real-world latency requirements and the complexity of live production data. Without a tight feedback loop, it’s easy to lose sight of what actually moves the needle for the end user.Our AI teams are intentionally designed to span the spectrum from foundational research to highly applied problem-solving, addressing these friction points before they stall a project. This integrated model brings research and application together under one umbrella, creating space to explore und …
