She loves a good question.

She wanted to understand why real enterprises succeed or fail with AI, and the executives who knew weren’t going to sit for doctoral interviews. During COVID, podcasting had exploded, and thousands of candid first-person interviews with exactly those people were sitting in public. She proposed using them as her primary data.

Her committee would approve the method only if someone, somewhere, had done it before. With a university librarian she searched the published literature. One study existed, anywhere in the world: a German foresight team had mined podcasts to spot future trends. No one had used executive podcast interviews as qualitative evidence. That one study was the precedent. Her research became the second, and the first of its kind.

The committee questioned her second choice too. Dissertation findings are normally checked by academics. She wanted hers checked by the people who would use them. So she recruited working technology leaders, IT executives, consultants, automation directors, SaaS founders, data science leaders, each with at least five years in enterprise technology, and built Reviewer Digital Cards for every episode. Every reviewer worked through every card: confirming her sub-themes, challenging them, adding their own, and writing in what they saw from the field. Their input became a second dataset alongside the podcasts. She built the above frameworks from it.