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.

Generic use cases fail to persuade leadership. To get genuine AI investment, build a custom tool that solves a specific, tangible pain point for an executive. An example is an 'AI board member' trained on past feedback to critique board decks before a meeting, making the value undeniable.

AI is a 'hands-on revolution,' not a technological shift like the cloud that can be delegated to an IT department. To lead effectively, executives (including non-technical ones) must personally use AI tools. This direct experience is essential for understanding AI's potential and guiding teams through transformation.

When transitioning Box to be "AI first," CEO Aaron Levie explicitly communicated that the goal was not to reduce headcount or cut costs. Instead, he framed AI as a tool to increase company output, speed, and customer service, which successfully aligned employees with the new strategy by removing fear.

When driving major organizational change, a data-driven approach from the start is crucial for overcoming emotional resistance to established ways of working. Building a strong business case based on financial and market metrics can depersonalize the discussion and align stakeholders more quickly than relying on vision alone.

AI models tend to be overly optimistic. To get a balanced market analysis, explicitly instruct AI research tools like Perplexity to act as a "devil's advocate." This helps uncover risks, challenge assumptions, and makes it easier for product managers to say "no" to weak ideas quickly.

Leaders often misjudge their teams' enthusiasm for AI. The reality is that skepticism and resistance are more common than excitement. This requires framing AI adoption as a human-centric change management challenge, focusing on winning over doubters rather than simply deploying new technology.

The most significant hurdle for businesses adopting revenue-driving AI is often internal resistance from senior leaders. Their fear, lack of understanding, or refusal to experiment can hold the entire organization back from crucial innovation.

When leadership demands ROI proof before an AI pilot has run, create a simple but compelling business case. Benchmark the exact time and money spent on a current workflow, then present a projected model of the savings after integrating specific AI tools. This tangible forecast makes it easier to secure approval.