Unlike traditional product management that relies on existing user data, building next-generation AI products often lacks historical data. In this ambiguous environment, the ability to craft a compelling narrative becomes more critical for gaining buy-in and momentum than purely data-driven analysis.

While strong data is a necessary condition for investment, it shouldn't be the sole determinant. Focusing too intently on a single metric, like quarterly net new ARR, can cause you to miss the larger secular trend. Data provides guideposts, but you can't lose sight of the bigger picture, the 'forest through the trees.'

Data's role is to reveal reality and identify problems or opportunities (the "what" and "where"). It cannot prescribe the solution. The creative, inventive process of design is still required to determine "how" to solve the problem effectively.

Quantitative data shows trends but can't explain why a restaurant partner isn't using a feature. True understanding for a three-sided marketplace comes from on-the-ground observation and conversation with consumers, partners, and couriers to uncover operational realities data can't capture.

Many leaders focus on data for backward-looking reporting, treating it like infrastructure. The real value comes from using data strategically for prediction and prescription. This requires foundational investment in technology, architecture, and machine learning capabilities to forecast what will happen and what actions to take.

Despite AI's capabilities, it lacks the full context necessary for nuanced business decisions. The most valuable work happens when people with diverse perspectives convene to solve problems, leveraging a collective understanding that AI cannot access. Technology should augment this, not replace it.

In high-stakes product decisions, data alone is insufficient to persuade senior leaders. A compelling narrative that taps into emotions and vision is more effective. The better story, even with less supporting data, will often win against a data-dump because decisions are both rational and emotional.

Instead of starting with available data, marketers should first identify and rank key business decisions by their potential financial impact. This decision-first approach ensures data collection and analysis efforts are focused on what truly drives business value, preventing 'analysis paralysis' and resource waste.

When VCs pushed for a data-driven focus on high-turnover products, Ed Stack prioritized the anecdotal experience of a customer awed by a vast selection. He knew that what looks inefficient on a spreadsheet can be the very thing that builds brand loyalty. The qualitative story was more predictive of long-term success than the quantitative data.