20:59

In this talk, the speaker argues that AI credits or tokens have no intrinsic value; their worth emerges only when linked to a customer's desired outcome. He proposes an outcome‑based, use‑case driven sales and pricing model for enterprise AI. A business use case is defined by four components: the actor(s) using the solution, the workflow or workload, the consumption metric (credits, tokens, DBUs, etc.), and the success criteria. By framing deals around these use cases, sellers can articulate clear ROI to CFOs, streamline onboarding, prioritize implementation, and create a roadmap for upsell and cross‑sell. The approach also simplifies procurement justification, as each use case provides a concrete business case with measurable deliverables. The speaker cites examples such as Snowflake, Databricks, and Clay to illustrate how companies have shifted from traditional feature‑based packages to consumption‑based, outcome‑focused models. He acknowledges challenges in transitioning sales teams, pricing credit values across varied use cases, and aligning with procurement cycles, but emphasizes that pricing should be dynamic and continuously refined based on observed value. The session concludes with a QR‑code linked playbook for adopting a use‑case‑based sales motion.

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