DBS quantifies AI impact not by cost savings, but by the incremental revenue generated from AI-driven customer "nudges." Using rigorous A/B testing, they track the lift from these interactions, reframing AI's value proposition from an efficiency tool to a revenue growth engine, targeting over a billion dollars.

Companies like Notion and Datadog are re-accelerating by targeting new, dedicated AI budgets. This is separate from shrinking 'efficiency tool' budgets. Growth comes from solving problems that unlock this specific new spending category, not just adding a minor AI feature.

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").

To increase deal size and escape the limitations of per-user pricing, embed AI into specific, productized use cases. This allows you to create new value-based pricing levers, such as AI credit consumption or custom AI agents, boosting average deal size.

Joe Lonsdale advises established SaaS companies to go on offense with AI. Instead of merely defending their core product, they should build AI agents on top of their platforms to automate customer workflows. This creates new, high-margin revenue streams by helping customers reduce headcount and increase efficiency.

While acknowledging AI's efficiency gains, Joe Tsai emphasizes its most significant impact at Alibaba comes from revenue growth. By infusing AI into consumer-facing products like e-commerce and maps, the company creates a 'massively better experience.' This directly translates to a larger user base and top-line growth, a more valuable outcome than just workforce reduction.

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.

In enterprise AI, competitive advantage comes less from the underlying model and more from the surrounding software. Features like versioning, analytics, integrations, and orchestration systems are critical for enterprise adoption and create stickiness that models alone cannot.

Snowflake moved beyond basic AI tools by building proprietary agentic models. One agent analyzes campaign data in real-time to optimize ad spend and ROI. A second 'competing agent' provides on-demand talking points for sales and marketing to use against specific competitors, solving a massive enablement challenge.