Targeting AI

How Capital One is building an AI-ready data ecosystem with creative talent

Targeting AI

Show Notes

In this interview on the Targeting AI podcast from AI Business, Amy Lenander of financial services giant Capital One discusses the critical role of talent in building AI-ready data ecosystems. She explores how organizations can cultivate the right skills, develop foundational data platforms and use AI to drive business value. The interview was recorded on-site at the Gartner Data & Analytics Summit 2026 in Orlando. Featuring Amy Lenander, chief data officer, Capital One In this episode, we cover how:

Talent agility outweighs technical experience in AI success. Organizations that develop learning agility and curiosity foster talent capable of navigating rapidly evolving AI landscapes. Instead of hiring for a specific toolset, focus on candidates who demonstrate rapid learning, problem-solving, and collaboration—traits that enable mastery of new AI methods as they emerge. Building a unified data ecosystem creates a competitive moat. A well-designed data ecosystem, prioritized over immediate AI application, provides a robust foundation that supports all future data and AI initiatives. Investing in governance, data trustworthiness, and accessibility shields organizations from fragmentation, enabling scalable innovation regardless of future technological shifts. AI adoption is a cultural shift, not just a technology implementation. Domain-specific data products enhance AI interpretability and trust. Specialized data teams responsible for understanding business nuances ensure AI systems interpret data context correctly for strategic use.

To learn more about generative and agentic AI and AI-ready data ecosystems, check out AI Business from Informa TechTarget, and please subscribe to our newsletter to keep up to date on the most important AI news. To watch video clips from our podcast, subscribe to our YouTube channel, @EyeonTech. References:

The Shift Toward AI Data Quality as a Core Product Data Quality in AI: 9 Common Issues and Best Practices Data and AI Governance Must Team Up for AI to Succeed

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