Why AI that works in the lab often fails in production — and what actually fixes it

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 research Delivering 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 underlying technology while staying grounded in actual business and associate needs. When foundational research and applied development are connected by design, you can accelerate learning, avoid dead ends, and account for real-world constraints early on. At Capital One, this approach has helped us to tackle challenges that are core to financial services, including improving fraud detection, enhancing digital user experiences, and improving customer-first technologies leveraging proprietary AI solutions.

For example, our research into combining multi-agent architectures goes beyond simple LLM reasoning; it aims to enable specialized AI agents to coordinate across distinct tasks, such as researching customer context and preparing documentation simultaneously. This research supported the launch of Chat Concierge , a car-buying solution that mimics human reasoning to not simply provide information, but take action on customers’ behalf based on their requests. We’re also breaking ground in delivering state-of-the-art solutions in agent servicing, AI personalization, and more.