Presented by Capital One
Enterprises aren’t struggling to experiment with AI; They’re struggling to implement it in the real world. Moving from promising prototypes to reliable, production-level systems is where most efforts stop.
In my role in Capital One’s AI Foundations organization, I’ve seen firsthand that successful AI implementation isn’t just about adopting the latest model or tool. This requires a disciplined R&D approach that connects fundamental research to real-world systems, and holds ideas accountable as they move from concept to production.
It is more difficult than it seems. AI capabilities are rapidly evolving, but enterprise environments can be complex, fragmented, and risky. The question is not just what is possible, but also what actually works – for a specific workflow, user or decision – with today’s technology and constraints.
The following demonstrates how organizations can turn AI ambition into production reality through a more thoughtful approach to research, evaluation, and deployment.
Linking basic and applied research
Delivering impactful AI requires bridging the gap between cutting-edge research and practical, real-world use cases. When research exists in an academic vacuum, insulated from operational reality, models that may perform well in offline environments 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 for the end user to lose sight of what really moves the needle.
Our AI teams are intentionally designed to span the spectrum from basic research to highly practical 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 the underlying technology while remaining based on real business and collaborative needs. When fundamental research and practical development are connected to design, you can accelerate learning, avoid deadlocks, and take into account real-world constraints sooner.
At Capital One, this approach has helped us tackle critical challenges across financial services, including improving fraud detection, enhancing digital user experiences, and improving customer-first technologies by leveraging proprietary AI solutions.
For example, our research in combining multi-agent architectures goes beyond simple LLM logic; Its purpose is to enable specialized AI agents to coordinate different tasks, such as researching customer context and preparing documents together. This research supported the launch of Chat Concierge, a car-buying solution that mimics human reasoning to not only provide information, but also take action on behalf of customers based on their requests. We are also moving forward in providing cutting-edge solutions in agent servicing, AI personalization and other areas. By connecting research to use cases, we can accelerate cutting-edge breakthroughs that actually scale in the real world.
Taking AI from concept to production
Not every AI idea should go straight into production. Rigorous evaluation from proof of concept to pilot to production is necessary to determine what is truly worth scaling, but only if those steps are treated as honest hurdles. Some ideas include:
A proof of concept Must be functional, not just theoretical. This shouldn’t be a “here’s what we can do” slide deck. It must be a machine doing something actually measurable. Even at this stage, you need an objective indication that the work is worth continuing.
a negative pilot The result is not failure. If pilots are always “successful” by definition, then they are not acting as a decision point – they are simply a slow commitment towards production. A pilot should expand the scope and realism, providing valuable data on whether a solution actually helps humans perform real tasks.
Production Is a team sport. Solving the core model or algorithm problem is only part of the job. Moving toward production requires a cross-functional reality encompassing software engineering, science, product and design, technical program management, operations, and other disciplines across an enterprise. Technical success is necessary, but it is not the end of the work.
In this entire journey, measurement is an important input. At Capital One, the ultimate ROI is a happy customer, so we focus on a number of key AI performance indicators like accuracy, latency and more to ensure we’re delivering the right time for our customers. If you can’t tell whether you’re improving or not, you won’t. Prioritizing accuracy over optics enables continuous improvement and progress.
Enabling continuous learning and responsible innovation
Sustained AI innovation depends as much on technology as it does on culture. Since research involves the exploration of the unknown, uncertainty is normal. A healthy culture recognizes that reality and creates space for taking informed risks with accountability.
Organizations should encourage course-correction. If admitting that “it’s not working” is perceived as a disaster, teams will learn to hide problems instead of solving them. But if teams are encouraged to evaluate honestly, move forward when needed, and learn from missteps, the organization can move forward faster and safely at the same time. This means treating pilots as real decision points – stopping, reshaping, or limiting efforts based on what the data shows, rather than pursuing them by default. At Capital One, we empower teams to try ambitious things, learn faster, and build an ecosystem that works to ensure AI is useful, trusted, and secure.
final thoughts
Building impactful AI isn’t about chasing every new breakthrough. It is about thoughtfully guiding ideas from research to reality through a culture that embraces assessment, collaboration and learning.
As AI continues to evolve, leaders must invest not only in the tools, but also in the R&D processes and cultural foundations that allow innovation to grow responsibly. When you combine research and application, prioritize continuous assessment and measurement, and foster environments where teams can learn and adapt, you give AI the best chance to have a lasting impact in the real world, at enterprise scale.
Liz Boushie, vice president of AI Foundation at Capital One.
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