For Amy Lenander, chief data officer at Capital One, a strong data foundation is the surest way to make AI systems succeed. Speaking on the Targeting AI podcast, recorded at the Gartner Data & Analytics Summit in Orlando, Lenander argued that because the technology is still young, the employees best positioned to help are those who are creative and quick to learn.
Talent agility over past experience
Lenander said she thinks a great deal about people who bring curiosity and learning agility, given how rapidly the field is changing. Hiring for expertise with specific legacy data or systems is useful, she noted, but on its own it is not sufficient for what the organization will need going forward. She pointed out that no one has years of experience with agentic coding assistants, simply because such tools have only just appeared. The emphasis, therefore, has been on recruiting people who are genuinely curious, strong problem-solvers, collaborative, and able to understand what the business actually needs.
New graduates, she added, often arrive with the most AI-native experience from their universities, which the company sees as an advantage as it adopts agentic and generative AI systems. A related perspective on organizing engineering work around these shifts appears in this overview of a capability architecture for AI-native engineering.
What an AI-ready data ecosystem looks like
Lenander defines an AI-ready data ecosystem as one that is well governed, well managed, and — importantly — easy to use. The reasoning is straightforward: AI relies on data for essentially everything it does, so the data has to be trustworthy. That trust, she said, is the foundation the company is trying to build. Maintaining that quality and accessibility at scale is the practical groundwork that lets an AI and machine-learning agenda move forward, a theme echoed in this case study on building a text-to-SQL system on enterprise data.
How AI changes the work
In Lenander’s view, the broader trend is that AI will automate tasks that are often the least enjoyable parts of a job, freeing employees to concentrate on the most creative problem-solving — whether that means analysis, coding, or building data platforms. Framed that way, talent and culture become as central to an AI strategy as the underlying technology.
What to watch
These observations reflect one organization’s approach and the perspective of a single executive, so they are best read as informed opinion rather than a universal blueprint. Hiring for curiosity and learning agility is a sensible response to fast-moving tooling, but the payoff depends on execution: data governance, security, and reliability still have to be built and maintained, and the value of any AI initiative ultimately rests on whether the underlying data can genuinely be trusted.
