Relying solely on data for 'go/no-go' decisions is a mistake. The best innovation decisions balance quantitative analysis (science), narrative and problem-solving (art), and an experienced leader's intuition (gut instinct) as a final override switch.

In large companies, a culture of A/B testing every decision can become a crutch that stifles innovation and speed. It leads to risk aversion and organizational lethargy, as teams lose the muscle for making convicted, gut-based decisions informed by qualitative customer feedback.

When leaders enforce memorizing every metric without a connecting narrative, teams resort to cherry-picking data to fit a story. This creates an illusion of data-drivenness while masking a lack of true strategic understanding and encouraging superficial analysis.

Like early pilots who flew by feel, leaders have traditionally operated without data. As work becomes more complex, leaders need 'instruments'—objective feedback from tools like AI—to navigate cloudy situations, build intuition, and understand their performance in real-time.

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.

Instead of seeking validation, leaders should test their strategy like a scientist. Formulate a specific hypothesis about customer value, commit to a clear test and a decision rule beforehand, and be prepared to pivot if the data proves the hypothesis wrong. This avoids confirmation bias.

In the AI era, the pace of change is so fast that by the time academic studies on "what works" are published, the underlying technology is already outdated. Leaders must therefore rely on conviction and rapid experimentation rather than waiting for validated evidence to act.

While data is crucial, leaders must teach teams to use judgment and not over-analyze obvious problems. The impulse to A/B test cleaning a milk spill versus restocking shelves is a sign of a culture that has lost its connection to practical reality.

The common tech mantra to 'follow the data' is shallow. Data is a powerful support system, but it primarily describes the past and can be misinterpreted. Truly great decisions, especially for zero-to-one innovation, require a deeper, more critical interpretation that incorporates qualitative insights to understand the 'why'.