To accelerate AI adoption, Block intentionally dismantled its siloed General Manager (GM) structure, which had given autonomy to units like Cash App. They centralized into a functional organization to drive engineering excellence, unify policies, and create a strong foundation for a company-wide AI transformation.

The main obstacle to deploying enterprise AI isn't just technical; it's achieving organizational alignment on a quantifiable definition of success. Creating a comprehensive evaluation suite is crucial before building, as no single person typically knows all the right answers.

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").

Treat AI initiatives as two separate strategic pillars. Create one roadmap focused on internal efficiency gains and cost reduction (productivity). Maintain a separate roadmap for developing new, revenue-generating customer experiences (growth). This prevents conflating internal tools with external products.

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.

Instead of adding AI tools to existing workflows, Qualcomm is radically redesigning its marketing department. The new model places a foundational AI systems architecture at the core, with processes and people organized around it. This holistic approach aims for true transformation rather than incremental efficiency gains.

To find valuable AI use cases, start with projects that save time (efficiency gains). Next, focus on improving the quality of existing outputs. Finally, pursue entirely new capabilities that were previously impossible, creating a roadmap from immediate to transformative value.

Relying solely on grassroots employee experimentation with AI is insufficient for transformation. Leadership must provide a top-down motion with resource allocation, budget, and permission for teams to fundamentally change workflows. This dual approach bridges the gap from experimentation to scale.

Instead of broadly implementing AI, use the Theory of Constraints to identify the one process limiting your entire company's throughput. Target this single bottleneck—whether in support, sales, or delivery—with focused AI automation to achieve the highest possible leverage and unlock system-wide growth.