A digital marketing manager knows that their organization has a fixed annual budget for the upcoming year. In order to test new initiatives, they will need some flexibility to create campaign experiments. Which best practice should they follow in order to create budget flexibility within their clearly defined annual budget?

The selection is correct because planning ahead by setting aside a percentage of budget to be used for testing and reacting to market changes creates a proactive framework for innovation that respects the organization's financial constraints while allowing AI-driven strategies to thrive. In an AI-powered ecosystem, continuous experimentation—such as testing new broad match keywords or trying a Performance Max campaign—is essential for identifying incremental growth opportunities; however, these tests require dedicated funding to gather statistically significant data. By earmarking a specific portion of the fixed annual budget (typically 10-20%) specifically for a "test-and-learn" pool, the marketer ensures they have the agility to respond to real-time search trends and auction shifts without needing to secure new approvals for every experiment. This structured approach to flexibility allows the organization to maintain strict budget discipline while simultaneously fueling the machine learning models with the new data needed to optimize performance and maximize the return on the remaining core spend.