Leaders face a catch-22 when trying to secure AI funding. They are asked to forecast specific results to get a budget, but they often need to spend money first to experiment, understand potential outcomes, and then measure success. This creates a difficult justification cycle.

If your team cannot articulate the specific business outcome of their AI usage in a single sentence, you don't have an AI strategy. You simply have 'token maxing'—usage for the sake of usage. This framework forces a direct link between AI spend and business results.

People often balk at a $200/month AI tool cost by comparing it to Netflix. This is the wrong mental model. Powerful AI agents are investments in productivity and value creation that should be evaluated based on their potential return on investment (ROI), not as a simple consumption expense.

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.

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.

Thinking about token budgets per person is a flawed, input-focused metric. The correct model is to allocate a budget (potentially seven figures) to a project or desired outcome, like beating a benchmark. This reframes AI spend as a capital allocation towards business goals, not an employee perk.

Companies should reframe AI spending not as a traditional IT cost but as a direct investment in amplifying human capital. This model views AI agents as 'digital workers' that provide leverage to every employee, justifying spend based on the ROI of the augmented workforce.

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.