By: Joyce Jia

The Fourth Pricing Revolution: Outcome-Based Pricing

In August 2024, customer experience (CX) software Zendesk made a stunning announcement: customers would only pay for issues resolved “from start to finish” by its AI agent. No resolution or human escalations? No Charge. Meanwhile, Zendesk’s competitor Sierra, a conversational AI startup, introduced its own outcome-based pricing tied to metrics like resolved support conversations or successful upsells. 

Zendesk claims to be the first in CX industry to adopt outcome-based pricing powered by AI, but it seems to have already fallen behind: Intercom has launched a similar model in 2023 for its “Fin” AI Chatbot, charging enterprise customers $0.99 only when the bot successfully resolves an end-user query. 

The wave to outcome-based pricing represents the fourth major pricing revolution in software. The first revolution began in the 1980s-1990s with seat-based licenses for shrink-wrapped boxes, where customers paid a one-time flat fee for software ownership without automatic version upgrades. The second revolution emerged in the 2000s when industry pricing transitioned to SaaS subscriptions, converting software into a recurring operational expense with continuous updates. The third revolution came in the 2010s with consumption-based cloud pricing, tying costs directly to actual resource usage. The fourth and current revolution is outcome-based pricing, where customers are charged only when measurable value is delivered, rather than for licenses purchased or resources consumed.

In fact, the shift to outcome-based pricing extends far beyond AI customer support, spanning AI-driven sectors from CRM platform like Salesforce to AI legal tech (EvenUp), fintech firm (Chargeflow), fraud prevention (Riskified and iDenfy) and Healthcare AI Agent. These companies are experimenting with pure outcome-based pricing or hybrid models that combine traditional flat fees, usage-based charges with outcome-based components. Recent tech industry analysis shows seat-based pricing for AI products dropped from 21% to 15% of companies in just one year, while hybrid pricing significantly increased from 27% to 41%.

Historical Precedent from Legal Practice: Contingency Fees

Outcome-based contracting isn’t a novel concept. It has been growing for over a decade in other industries. In the legal field, professionals have long worked with its equivalent in the form of contingency fees. Since the 19th century, lawyers in the United States have been compensated based on results: earning a percentage of the recovery only if they successfully settle or win a case. However, this model has been accompanied by strict guardrails. Under ABA Model Rule 1.5(c), contingency fee agreements must be in writing and clearly explain both the qualifying outcome and calculation method. Additionally, contingency arrangements are prohibited in certain matters, such as criminal defense and domestic relations cases. 

Beyond professional ethical concerns, the key principle is straightforward: when compensation hinges on outcomes, the law demands heightened transparency and well-defined terms. AI vendors adopting outcome-based pricing should expect similar guardrails to develop, ensuring both contract enforceability and customer trust. This requirement stems from traditional contract law, not AI-specific regulation. 

The Critical Legal Question: Defining “Outcome”

One of the biggest challenges in outcome-based pricing is contract clarity. Contract law requires essential terms to be clearly defined. If such terms are vague or cannot be determined as reasonably certain, the agreements may be unenforceable. Applying it to AI agents, one critical question arises: How do you precisely and fairly define a “successful” outcome?

The answer can be perplexing. Depending on the nature of the AI product, multiple layers can contribute to “outcome” delivery, such as internal infrastructure or workflows, external market conditions, marketing efforts, or third-party dependencies. These complex factors make it hard to judge clear ownership of results or to establish precise payment triggers. This is especially true when “outcome” is delivered over an extended period.

The venture capital firm, Andreessen Horowitz, recently conducted a survey highlighting the issue: 47% of enterprise buyers struggle to define measurable outcomes, 25% find it difficult to agree on how value should be attributed to an AI tool or model, and another 24% note that outcomes often depend on factors outside the AI vendor’s control.

These are not just operational challenges. They raise a real legal question about whether the contract terms are enforceable under the law. 

Consider these scenarios that illustrate the difficulty:

• What happens if the outcome is only partially achieved?
• What if the AI agent resolves the issue but too slowly, leaving the user frustrated despite a technically successful outcome?
• What if an AI chatbot closes a conversation successfully, but the customer returns later with a complaint?
• What if a user ends the chat session without explicitly confirming whether the issue was resolved?

As Kyle Poyar, a SaaS pricing expert and author of an influential newsletter on pricing strategy and product-led growth, observed: 

“Most products are just not built in a way that they own the outcome from beginning to end and can prove the incrementality to customers. I think true success-based pricing will remain rare. I do think people will tap into the concept of success-based pricing to market their products. They’ll be calling themselves ‘success based’ but really charge based on the amount of work that’s completed by a combination of AI and software.”

Legal Implication for the Future

Just as the rapid growth of AI agents themselves, outcome-based AI pricing is evolving at breakneck speed. The blossoming of this new pricing model presents a challenge for contract implementation and requires existing contract terms to adapt once again to accommodate new forms of value creation and innovative business models.

The scenarios above are just a few examples, but they underscore the importance of attorneys working closely with engineering and business teams to meticulously identify potential conflicts and articulate key contract terms grounded in clear metrics and KPIs that objectively define successful outcomes. 

“Outcome” could mean different things to different parties, and its definitional ambiguity could create misaligned incentives. Buyers may underreport value while vendors might game metrics to overstate performance. These dynamics will inevitably lead to disputes. AI vendors that have adopted or plan to adopt outcome-based pricing must develop robust frameworks addressing contract definiteness and attribution standards before dispute rises. Without these safeguards, we can likely see a wave of conflicts over vague terms, unenforceable agreements, and unmet expectations on both sides as AI agents surge.