Sierra blog post (December 10, 2024, Elliot Greenwald) laying out the **founding text of *outcome-based pricing*** for AI agents. **Pivot thesis**: AI agents that execute processes autonomously make possible an **entirely new pricing model** — ***"you pay only when the software achieves specific, valuable outcomes: outcome-based pricing."*** The article traces a **four-age genealogy of software pricing**: (1) **shrink-wrapped software** (1980s-90s, the floppy-disk/CD-ROM box at Fry's Electronics — *"Whether you actually used it or not, you paid for it"*) → (2) **SaaS / seat-based** (pioneered by **Salesforce**, followed by Google/Microsoft/Adobe — the Internet makes it possible to sell software *as a service*) → (3) **consumption-based** (**Amazon/AWS** and **Snowflake** — *"charged only for what you used"*) → (4) **outcome-based** (AI agents). **Canonical definition**: ***"outcome-based pricing is tied to tangible business impacts—such as a resolved support conversation, a saved cancellation, an upsell, a cross-sell, or any number of valuable outcomes. If the conversation is unresolved, in most cases, there's no charge."*** **Aligned-incentives principle**: ***"With outcome-based pricing, Sierra gets paid only when we complete a task for you. Our incentives are aligned."*** **Critique of seat-based pricing & the concept of *shelfware***: *"Unused seats sit idly on a proverbial store shelf, hence the derisive moniker 'shelfware'"* — thousands of dollars per year are paid per license, whether used or not. **Structural conflict for Fournisseurs CX legacy**: their revenue depends on seat-based pricing, yet *"the more effective their AI becomes, the fewer contact center seats their clients need—undermining the provider's own revenue model"* — an effective AI agent **cannibalizes** the revenue model of a vendor whose pricing rests on seats. **Granularity of the outcome**: a distinction between **simple resolutions** (answering a question) and **complex resolutions** (handling a case that requires a 20-minute L2 call); **escalations generally incur no charge**; **blended pricing** is possible (e.g., consumption-based for routing/greeting interactions). **Continuous-optimization commitment** on the vendor side: *"we continue to deploy concerted, directed optimizations to refine the agent's performance over time"* — the vendor stays aligned to improve performance since it is paid only for the outcome. Significance: posed in **late 2024**, this post **precedes and grounds** the entire 2026 debate on the agentic economy — it supplies the **vocabulary of the billing unit** (the completed *outcome* rather than the seat, usage, or token) that will later be taken up by Gupta (*cost of a completed outcome*, *token-to-outcome attribution*), Bain (*outcome-based pricing shifts revenue from fixed seats to labor/operations economics*), Ng (*pricing power anchored on the salary of the replaced employee*). With Sierra as the **reference example** cited by Bain (*autonomous customer issue resolution*), this text gives the **vendor-side view** of the mechanics that others analyze from the buyer side. Directly relevant to the firm's positioning on **agentic-delivery / value-based pricing** and to the **Cost Optimization** slot (the vendor-side counterpart of *cost per outcome*).
**Elliot Greenwald** — Sierra (entreprise fondée par Bret Taylor & Clay Bavor, plateforme d'agents IA conversationnels pour l'expérience client). Billet publié sur le blog Sierra le **10 décembre 2024**. Sierra est l'**exemple-référence** cité par Bain (*The $100-Billion SaaS Opportunity*) pour l'*autonomous customer issue resolution* · et fait l'objet de plusieurs fiches du dossier (recrutement AI-native, interview Plan/Build/Review).
OpenAI's official case study on the ChatGPT Enterprise deployment at Moderna: 750 GPTs in 2 months, 100% legal adoption, the Dose ID GPT for clinical trials, Stéphane Bancel's "100,000 employees" quote, an organizational transformation framework (mChat, Generative AI Champions, an internal forum with 2,000 participants).
#Moderna#OpenAI#ChatGPT Enterprise
OpenAI (étude de cas officielle, citations Stéphane Bancel, Brad Miller, Brice Challamel, Shannon Klinger, Kate Cronin, Meklit Workneh)