Don't just report on leading indicators like faster cycle times. You must explicitly connect them to forecasted lagging outcomes. Present a clear narrative showing how today's efficiency gain will translate into future revenue or cost savings, providing a range of potential impacts.

Demanding a direct, line-item ROI for foundational AI initiatives is like asking for the ROI on Wi-Fi—it's the wrong question. Instead of getting bogged down in impossible calculations, leaders should focus on measuring the business outcomes enabled by the technology, such as innovation speed or new product creation. Obsess on outcomes, not direct financial return.

Instead of focusing on cost-cutting metrics like "hours saved," leaders should measure AI's success by the capacity it frees up. For instance, faster research analysis enables more studies per year, leading to more customer-informed decisions. This reframes efficiency as a strategic advantage that drives growth, not just reduces costs.

AI requires significant upfront investment with uncertain returns, creating an "investment paradox" for CFOs. Traditional ROI models are insufficient. A new financial framework is needed that measures not just cost savings but also revenue acceleration, risk mitigation, and the strategic option value of competitive positioning.

When presenting to leadership, translate AI's impact into the two metrics they universally care about: growing revenue or reducing costs. This simple framing has a high probability of success, much like showing a Pixar movie to entertain children you don't know.

To get CFO buy-in, don't just model the upside of AI investment. A more powerful approach is to include a baseline scenario showing the quantifiable business impact of delaying action. This frames the investment not just as an opportunity, but as a necessary defense against competitive disadvantage and market pressures like the patent cliff.

When selling an AI platform to a CFO, go beyond abstract productivity gains. Calculate the direct cost savings from reducing token consumption on other, less efficient LLMs. This creates a powerful, easily quantifiable business case based on reducing existing AI spend, which resonates strongly with financial leaders.

Businesses are unlikely to use powerful AI simply to shave a few percentage points off their software spend. The real, high-impact ROI comes from applying AI to improve core business operations, making the actual business more effective and efficient.

Leaders often expect AI to produce a shiny, marketable feature. When AI’s value is 'invisible'—baked into workflows to improve efficiency—translate those gains into concrete financial outcomes like cost savings or accelerated revenue, rather than focusing on the process improvements themselves.