Analysts are evaluating companies' AI implementation not just on technology, but across six business functions: personalization, customer acquisition, product innovation, labor productivity, supply chain, and inventory management. The assessment also considers breadth, depth, and proprietary initiatives to differentiate leaders.

Analysts created a method to evaluate corporate AI adoption across six key areas: personalization, customer acquisition, product innovation, labor productivity, supply chain, and inventory management. Companies are then ranked on the breadth, depth, and proprietary nature of their AI initiatives.

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.

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.

An IBM study reveals a significant performance gap in AI adoption. The top 20% of companies achieve over 60% ROI from their product engineering efforts, while the median return for the rest is only 36%. This highlights the value of mastering key team behaviors.

Walmart demonstrates the tangible revenue impact of mature AI integration. By deploying tools like GenAI shopping assistants, computer vision for shelf monitoring, and LLMs for inventory, the retailer has significantly increased customer spending, proving AI's value beyond simple cost efficiencies.

AI's value for PMs is augmentation, not replacement. By automating tactical tasks that consume most of a PM's day (e.g., "six out of eight hours"), AI frees up critical capacity for higher-level strategic, creative, and innovative work—the core functions of a product leader.

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.

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.