"Customers don't pay for AI. They pay for Outcomes."
In this strategic session, Ramya Venkatesh (Head of Data & AI) tackles the biggest challenge facing AI product leaders today: Revenue. While AI usage is skyrocketing, many companies are struggling to translate high engagement into sustainable business models.
Ramya breaks down the "Art of AI Monetization" by introducing a rigorous Cost Framework that accounts for hidden expenses like inference and compliance. She explores four distinct monetization models—from Tier Upgrades to Agentic Pricing—and presents a compelling case study on a "Technician Copilot" where revenue was tied directly to productivity gains rather than just AI features.
Key Takeaways
03:15The Revenue Gap: Why high AI usage does not automatically equal high revenue, and the trap of focusing on features instead of value.
09:40The 4 Monetization Models: A deep dive into Add-ons, Tier Upgrades, AI Agents, and API-based pricing models suitable for different product types.
16:20The Hidden Cost of AI: Understanding the full "Cost of Goods Sold" (COGS) for AI, including Inference, Compute, and Legal/Compliance costs.
22:00Case Study - Technician Copilot: How to price a product based on "Time Saved" (Outcome) rather than "Queries Made" (Usage).
28:10Validation Strategy: How to conduct pre-release client validation to ensure your pricing model fits the user's workflow before launch.