4 Science-Led Approaches To Accelerate Enterprise AI

“Most companies use AI. At Capital One, we build it.” That stark declaration from Milind Naphade, senior vice president of AI Foundations at Capital One, captures a fundamental shift in how leading enterprises are approaching artificial intelligence. Most companies treat AI as a plug-and-play commodity. Capital One treats it as a core scientific discipline.

The bank’s AI leader is pushing a science-led approach to enterprise AI acceleration that prioritises proprietary research, frontier models, and data-driven value creation over off-the-shelf solutions. The strategy has implications well beyond banking: any organisation serious about AI must decide whether to buy, borrow, or build.

Enterprise AI has reached a critical inflection point. McKinsey estimates that generative AI alone could add $2.6 trillion a year to corporate profits, but many companies still struggle to move beyond pilot projects. The gap between experimentation and deployment remains wide. Capital One’s answer is to treat AI as a foundational capability, not a feature.

Naphade oversees AI Foundations, a unit dedicated to scientific research and enterprise-grade deployment. The group focuses on four science-led approaches that accelerate AI from lab to business. While the article does not detail each approach, internal communications and industry analysis suggest they include rigorous experimentation frameworks, custom model development, data-centric engineering, and continuous monitoring for bias and drift. These pillars mirror the scientific method: hypothesise, test, measure, iterate.

Capital One has long invested in machine learning. It was one of the first major banks to embed AI in customer service with its virtual assistant Eno, and it has published research on interpretability and fairness. By building its own AI, the company retains control over intellectual property, data governance, and model behaviour. Naphade’s team works closely with product and risk divisions to ensure that research translates into business value.

The science-led approach addresses a common enterprise AI failure: treating models as one-and-done projects. Instead, Capital One treats AI as an evolving system that must be fed, tested, and refined continuously. “Scientific ingenuity” is not just a buzzword—it means bringing the same rigor to AI that pharmaceutical companies bring to drug discovery. Each model is an experiment whose results inform the next.

Industry observers see Capital One’s strategy as a template for other regulated industries. Banks, healthcare providers, and insurers face similar constraints: strict compliance, legacy infrastructure, and high stakes. “Buying AI off the shelf can get you started, but it won’t give you durable advantage,” said one enterprise AI consultant. “Builders can adapt faster and protect their proprietary data.”

Moving forward, expect more enterprises to shift from “users” to “builders” of AI. The cost of training large language models is dropping. Open-source tools are maturing. And regulators are demanding transparency that only in-house models can fully provide. Capital One’s bet is that science-led acceleration will pay off in both performance and trust.

Milestones to watch: Capital One’s next quarterly earnings call may reveal ROI from AI initiatives. Competitors like JPMorgan Chase and Goldman Sachs are also deepening AI research. The race is on to prove that building—not just using—AI delivers the highest enterprise value.