DBS quantifies AI impact not by cost savings, but by the incremental revenue generated from AI-driven customer "nudges." Using rigorous A/B testing, they track the lift from these interactions, reframing AI's value proposition from an efficiency tool to a revenue growth engine, targeting over a billion dollars.

Focusing on AI for cost savings yields incremental gains. The transformative value comes from rethinking entire workflows to drive top-line growth. This is achieved by either delivering a service much faster or by expanding a high-touch service to a vastly larger audience ("do more").

Instead of abstract productivity metrics, define your AI goal in terms of concrete headcount avoidance. Sensei's objective is to achieve the output of a 700-person company with half the staff by using AI to bridge the gap. This makes the ROI tangible and aligns AI investment with scalable, capital-efficient growth.

While AI tools dramatically increase content production speed, true ROI is not measured in output. Leaders should track incremental engagement, conversion lift, and revenue per message. An often overlooked KPI is brand consistency—how often content passes governance checks on the first try.

Companies struggle to measure AI's return on investment because its value often materializes as individual productivity gains for employees. These personal efficiencies, like finishing work earlier, don't show up on corporate dashboards, creating a mismatch between perceived value and actual impact.

To move beyond FOMO-driven investment, AI21 Labs' CMO advises measuring AI's business impact across three pillars: its ability to scale growth, its power to improve decisions through faster analysis, and its capacity to help organizations avoid and plan for risks.

Don't rely on traditional project milestones to gauge AI progress. Instead, measure success through granular unit economics and operational metrics. Metrics like 'cost per release' or 'cycle time per feature' provide immediate feedback on whether your strategic hypothesis is valid, enabling rapid iteration.

The trend is shifting from simply adopting AI to proving its ROI with specific metrics. As industry leaders publicly share their AI-driven gains, it creates a competitive necessity for all other companies to follow suit and quantify their own benefits, making it 'table stakes' for all.

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.