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.

For PMs in restrictive companies, the best way to get budget for AI tools is to show, not tell. Use free or personal plans to demonstrate a clear productivity gain or solve a specific problem. Frame the request around accelerating business impact, not just acquiring new software.

To get C-suite buy-in for long-term brand investment, marketers should run small, ring-fenced test campaigns. By isolating a market segment and layering brand tactics on top of demand generation, you can demonstrably prove superior growth compared to a control group, de-risking a larger investment.

Moonshot AI overcomes customer skepticism in its AI recommendations by focusing on quantifiable outcomes. Instead of explaining the technology, they demonstrate value by showing clients the direct increase in revenue from the AI's optimizations. Tangible financial results become the ultimate trust-builder.

Snowflake's CEO advises against seeking a huge ROI on the first AI project. Instead, companies should run many small, inexpensive experiments—taking multiple "shots on goal"—to learn the landscape and build momentum. This approach proves value incrementally rather than relying on one big bet.

The "AI ROI flywheel" is a strategy where an organization starts with AI projects that deliver massive, measurable returns (e.g., 10:1 to 30:1). These initial wins create credibility and buy-in, making it progressively easier to secure resources for future AI initiatives.

Instead of ad-hoc pilots, structure them to quantify value across three pillars: incremental revenue (e.g., reduced churn), tangible cost savings (e.g., FTE reduction), and opportunity costs (e.g., freed-up productivity). This builds a solid, co-created business case for monetization.

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.

When leadership pays lip service to AI without committing resources, the root cause is a lack of understanding. Overcome this by empowering a small team to achieve a specific, measurable win (e.g., "we saved 150 hours and generated $1M in new revenue") and presenting it as a concise case study to prove value.