American Eagle's Former CDO Shares His Data-First Playbook For Long-Term AI Success

Across industries, the initial excitement for AI is waning. Now, as the complex realities of corporate implementation settle in, leaders are being forced to reassess. In this new era, a patient, long-term strategy focused on foundational readiness is required. Yet, for many leaders, the biggest roadblock is often unexpected: instead of AI, it's a lack of trust in the data that fuels it.

BK Vasan, formerly the Chief Data Officer at American Eagle Outfitters, has spent his career at the intersection of data and consumer behavior. A top analytics leader in retail with over 20 years of experience driving data strategy at giants like T-Mobile and Walmart, he's precisely the right person to ask for strategic advice. For many leaders, Vasan’s message is one of strategic patience, advising them to focus on foundational readiness as a conduit to large-scale AI adoption.

"I think most business leaders should use this time strategically as the technology matures over the next year or so, particularly for consumer companies in retail, telecommunication, and CPGs," Vasan said. Framing the current executive dilemma as a choice, he explained that while a select few are positioned to "play to win" with aggressive adoption, the vast majority are focused on building the resilience and readiness needed to thrive in the long term.

• Data distrust: For the group caught between technological promises and operational realities, Vasan offered additional advice. "The biggest challenge in AI adoption today is the trustworthiness of the data. You can find twenty convincing but contradictory things on the internet, which makes automation a challenge. Human judgment is still required to validate data before making a business decision." Unfortunately, this foundational problem of trust often leads to unrealistic ROI expectations, he said.

• Manual override: Meanwhile, the need for human judgment remains a key consideration for leveraging AI, he continued. "You need a human in the loop to synthesize the information before you can consume it and make a business decision."

Today, this trust issue actively shapes the data strategies companies actually use, Vasan explained. As one possible solution, he outlined a three-part playbook that demystifies how companies work around today's data constraints.

• First-party data: First, use your own customer purchase history for straightforward tasks like marketing campaigns, Vasan said. Here, he noted the importance of always respecting the "thin line" of privacy.

• The influencer workaround: Next, dismiss the popular myth that companies mine vast troves of third-party data from social media. "Privacy regulations make the practice prohibitively complex and risky," Vasan explained. Instead, the practical, privacy-compliant workaround is surprisingly low-tech: leverage influencers to tap into specific market segments.

• Buy, don't build: Finally, for "dot-connecting" insights, companies can buy intelligence from specialized market research firms rather than building that capability themselves. "Build when you want to differentiate, and buy when you want to accelerate," he clarified.

But Vasan's core advice for leadership was even more concrete. Before implementing any new technology, leaders must reimagine their business processes first.

• Process before tools: To truly assess an organization’s AI readiness, it often starts with a simple question from the CEO or CIO. For example, in a retail environment, Vasan advised, ask: 'Who owns setting the price for a particular product?' "You’d be surprised how often ownership is split across marketing, merchandising, and digital teams. When accountability is fragmented, it becomes nearly impossible to create measurable ROI or meaningful business value through AI."

Ultimately, this type of process-first approach requires patience and a realistic view of financial returns, Vasan concluded. Comparing the current hype to the Y2K and dot-com eras, he explained that without a clear strategy, corporate AI initiatives can risk becoming distractions that lack clear business impact.

In closing, Vasan shared his own experience in putting this patient, strategic approach into practice. "This is exactly what I had to do at American Eagle. I told the board, 'Hey, I'm going to try out generative AI, but don't expect any financial reward or revenue immediately.'" Today, that's the case for the vast majority of organizations—the return on AI won't simply materialize overnight, but beginning the investment early and re-assessing often as technologies evolve is a winning strategy. "Four or five years from now, you'll look back and see that we are operating much better than we were just a few years ago."