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.

Superhuman's CEO advises against simply tracking AI costs, a practice he calls 'token maxing'. Instead, they evaluate the ROI of internal AI tools by measuring developer productivity metrics like feature delivery pace. This output-focused approach has doubled engineering velocity, justifying the AI spend.

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.

Businesses should prioritize AI projects that can completely automate a recurring workflow. Transforming a multi-week manual process into an instantaneous one delivers transformative value, far exceeding the gains from projects that only offer partial assistance to a human user.

Howie Lu advises against anchoring AI costs to cheap software subscriptions. Instead, evaluate token costs against the opportunity cost of an equivalent human's time. A $150 agent-written board memo is cheap if it saves days of a CEO's time and produces a superior result.

According to Mike Cannon-Brookes, advanced enterprises are not tracking AI success by counting tokens. Instead, they are asking harder questions about overall output, such as engineering productivity and quality. They understand that high token usage doesn't always correlate with high productivity, shifting focus from raw usage to tangible business outcomes.

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.

Calculating the ROI of an AI initiative as a simple tool investment is a mistake. The true ROI comes from transforming entire systems and processes. This requires measuring the impact on organizational change, new workflows, and strategic alignment, which is far more complex than a basic software cost-benefit analysis.

While AI provides operational efficiency, its most profound value lies in enabling tasks that were previously impossible due to scale, like instantly rewriting 10 million pages of web content after a terminology change. This capability transcends traditional ROI calculations.