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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 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 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. By keeping research tethered to the use case, we can accelerate state-of-the-art breakthroughs that actually scale in the real world.Moving AI from concept to productionNot every AI idea should go straight to production. Rigorous evaluation from proof of concept to pilot to production is essential to determining what’s truly worth scaling, but only if those stages are treated as honest hurdles. Some considerations include:A proof of concept must be functional, not just theoretical. It shouldn’t be a “here’s what we could do” slide deck. It must be a machine actually doing something measurable. Even at this stage, you need an objective signal that the work is worth continuing.A negative pilot result isn’t a failure. If pilots always “succeed” by definition, then they aren’t functioning as decision points—they’re just a slow-motion commitment to production. A pilot should expand scope and realism, providing valuable data on whether a solution actually helps a human do real work.Production is a team sport. Solving the core model or algorithmic problem is only part of the job. Moving to production requires a cross-functional reality involving software engineering, science, product and design, technical program management, operations, and other disciplines across an enterprise. The technical breakthrough is necessary, but it’s not the end of the work.Throughout this 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 meeting the moment for our customers. If you can’t tell whether you’re improving, then you won’t. Prioritizing accuracy over opticsis what enables continuous improvement and progress.Enabling continuous learning and responsible innovationSustainable AI innovation depends as much on culture as it does on technology. Because research involves exploring the unknown, uncertainty is normal. A healthy culture recognizes that reality and creates space for informed risk-taking, paired with accountability.Organizations must encourage course-correction. If acknowledging “this isn’t working” is treated as a disaster, teams will learn to hide problems rather than solve them. But if teams are encouraged to evaluate honestly, pivot when needed, and learn from false-starts, then the organization can move faster and safer at the same time. That means treating pilots as real decision points — stopping, reshaping, or narrowing efforts based on what the data shows, rather than pushing them forward by default. At Capital One, we enable teams to try ambitious things, learn quickly, and build an ecosystem that works to ensure AI is useful, reliable, and safe.Final thoughtsBuilding impactful AI isn’t about chasing every new breakthrough. It’s about thoughtfully guiding ideas from research to reality through evaluation, collaboration, and a culture that embraces learning.As AI continues to evolve, leaders should invest not only in tools, but also in R&D processes and cultural foundations that allow innovation to scale responsibly. When you bridge research and application, prioritize continuous evaluation and measurement, and foster environments where teams can learn and adapt, you give AI its best chance to deliver lasting impact, at enterprise scale, in the real world.Liz Boschee us VP, AI Foundations at Capital One.Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. 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