It's a mistake to make 'using AI' the strategy itself. Fundamental business drivers like customer lifetime value (LTV), retention, and engagement remain unchanged. AI is a powerful new method for influencing these timeless metrics, but it is not a replacement for a sound business strategy focused on customer value.

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.

AI's primary value isn't replacing employees, but accelerating the speed and quality of their work. To implement it effectively, companies must first analyze and improve their underlying business processes. AI can then be used to sift through data faster and automate refined workflows, acting as a powerful assistant.

Teams embrace AI more quickly when it enables them to perform entirely new tasks they couldn't do before, like coding or advanced data analysis. This is more motivating than using AI for incremental improvements on existing workflows, which can feel less exciting and impactful.

Don't confuse adoption with transformation. Adoption is using AI to do existing tasks more efficiently. Transformation is using AI to achieve outcomes and build business models that were previously impossible. This distinction is key for measuring the true strategic impact of AI initiatives.

Demanding a direct, line-item ROI for foundational AI initiatives is like asking for the ROI on Wi-Fi—it's the wrong question. Instead of getting bogged down in impossible calculations, leaders should focus on measuring the business outcomes enabled by the technology, such as innovation speed or new product creation. Obsess on outcomes, not direct financial return.

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.

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.

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.