Data, Trust, and the New Rules of AI

Sarah Fay has spent decades at the intersection of media, technology, and data—long enough to watch data evolve from a targeting mechanism into the central asset of the digital economy. Her view is direct: organizations that understand the provenance of their data, govern it responsibly, and build transparent relationships with the people generating it will have something their competitors can’t easily replicate.

The structural challenge is that most data markets weren’t built for this moment. Aggregated segments, opaque sourcing, and limited consumer awareness made data easy to acquire but hard to trust. As AI’s demand for high-quality, domain-specific data grows, that gap becomes a real strategic liability, and the organizations that close it first will be better positioned to scale.

What’s ahead is a more reciprocal data economy, one where consumers have more visibility, more control, and potentially direct economic stakes in how their data is used. For enterprises, that shift makes sovereign data practices less of a compliance posture and more of a growth strategy.

Key takeaways:

• Data provenance is a strategic imperative: Knowing where your data comes from—and demonstrating it—is a precondition for responsible AI development and a signal of organizational maturity.
• Scale in AI runs on data access: The organizations ahead in AI got there partly because they already own vast, high-quality data. For everyone else, how they acquire and govern external data is a consequential strategic choice.
• Trust is becoming a data moat: As consumers gain more visibility and control over their data, organizations that have built consent-based, transparent data relationships will hold a structural advantage.
• The data economy rewards early movers: Regulation, market structure, and consumer expectations around data are all shifting simultaneously. The organizations paying attention now will be better positioned to shape how it settles.