Beyond improving traditional marketing metrics, a crucial new shared KPI for the CMO-CIO partnership is "Time to Value." This measures the efficiency of AI pilot selection, execution, and scaling, ensuring the collaboration delivers on AI's promise of speed without getting bogged down by process or governance hurdles.

To quantify the real-world impact of its AI tools, Block tracks a simple but powerful metric: "manual hours saved." This KPI combines qualitative and quantitative signals to provide a clear measure of ROI, with a target to save 25% of manual hours across the company.

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.

Unlike traditional software that optimizes for time-in-app, the most successful AI products will be measured by their ability to save users time. The new benchmark for value will be how much cognitive load or manual work is automated "behind the scenes," fundamentally changing the definition of a successful product.

Traditional software development focuses on adding features for a user to operate. In the AI era, the product roadmap should be measured by the amount of work the software can perform autonomously, directly saving the customer time and freeing them from tedious tasks.

With infinitely scalable AI agents, cost and time per interaction are no longer primary constraints. Companies should abandon classic efficiency metrics like Average Handle Time and instead measure success by outcomes, such as percentage of tasks completed and improvements in Customer Satisfaction (CSAT).

AI tools will drive higher refinancing volumes, increasing the total market size for mortgage originators. However, by making it effortless for consumers to compare offers, AI will also intensify competition. This price transparency will pressure the "gain on sale" margins lenders earn on each loan, pitting the benefit of higher volume against lower per-unit profitability.

AI automation is making the daily financial close a tangible possibility, moving beyond the traditional monthly cycle. This provides near real-time visibility into business performance, which is a powerful but potentially demanding capability for PE-backed companies.

Vanity metrics like "AI lines of code" are misleading. Coinbase measures AI success by its impact on the end-to-end development cycle: the total time from a ticket's creation to the change landing with a user. This metric holistically captures gains and focuses the team on true velocity.