Box CEO Aaron Levy argues the focus on AI's return (R) is misplaced. The real leverage is making the initial investment (I) so low that companies can pursue projects previously deemed too expensive or risky, from custom software for small firms to new R&D initiatives, thus creating new value.
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The true ROI of AI lies in reallocating the time and resources saved from automation towards accelerating growth and innovation. Instead of simply cutting staff, companies should use the efficiency gains to pursue new initiatives that increase demand for their products or services.
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").
Snowflake's CEO advises against seeking a huge ROI on the first AI project. Instead, companies should run many small, inexpensive experiments—taking multiple "shots on goal"—to learn the landscape and build momentum. This approach proves value incrementally rather than relying on one big bet.
Most companies use AI for optimization—making existing processes faster and cheaper. The greater opportunity is innovation: using AI to create entirely new forms of value. This "10x thinking" is critical for growth, especially as pure efficiency gains will ultimately lead to a reduced need for human workers.
The "AI ROI flywheel" is a strategy where an organization starts with AI projects that deliver massive, measurable returns (e.g., 10:1 to 30:1). These initial wins create credibility and buy-in, making it progressively easier to secure resources for future AI initiatives.
Measuring AI's value by hours saved is misleading for law firms, as it can imply lower revenue. The true ROI comes from what lawyers do with that saved time: pursuing more complex strategies, conducting deeper analysis, and spending more time with clients—high-value work previously constrained by time.
JP Morgan's analysis that AI needs to generate '$34/month from every iPhone user' to see a return is a flawed framing. Like cloud computing, the cost and value of AI will be embedded into thousands of different products and services, not borne as a direct consumer subscription. This indirect value capture makes direct per-user ROI calculations misleading.
The most significant value from AI is not in automating existing tasks, but in performing work that was previously too costly or complex for an organization to attempt. This creates entirely new capabilities, like analyzing every single purchase order for hidden patterns, thereby unlocking new enterprise value.
Snowflake's former CRO offers a pragmatic view of AI, calling it a 'task automator.' He stresses that for enterprise adoption, AI tools can't just be 'cool.' They must deliver a clear return on investment by either generating revenue or creating significant cost savings, like the 418 hours per week saved by their support team.
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.
