Not all outcomes are created equal: A guide to outcome-based pricing

As AI has grown, outcome-based models are stepping in as the new way of working, leaving SaaS models behind. This reflects the new flow of work – the focus is on AI and humans working together and less about headcount. As models shift to consumption-based pricing, outcomes are incredibly important. 

While many service models have shifted towards outcome-based pricing, Zendesk has been in the game since 2024. Since then we’ve solved billions of outcomes that helped drill down on a fundamental question: What should count as an outcome? 

The way outcomes are defined has implications far beyond pricing. It shapes how businesses measure the value AI delivers, assess the quality of its work, and manage their investment as AI becomes more capable.

If you're evaluating outcome-based pricing, here are four things every CX leader should look for.

1. Measure outcomes you can hold accountable

The best outcome-based pricing models are tied to metrics your business already uses to measure success. They might be customer resolutions or operational outcomes that move work forward. What matters is that they're meaningful, observable, and aligned to the value your business receives. 

AI can complete hundreds of actions behind the scenes to achieve a single outcome. What matters is whether those actions result in a resolved customer request, a satisfied customer, and meaningful progress for your business.

2. Evaluate the quality of the outcome

An AI agent might complete an interaction, but that doesn't necessarily mean the customer's issue was fully resolved.

CX leaders need to look beyond usage, automation, and deflection metrics to understand the quality of the work AI delivers. Was the customer's request handled accurately? Was the issue fully resolved? Did the outcome meet the organization's standards?

The strongest approaches give organizations visibility into the quality and completeness of AI outcomes, so they can identify where AI is performing well and where improvements are needed.

This visibility helps teams continuously improve AI performance and deliver better customer experiences over time. 

3. Make sure your costs are predictable

Predictability is one of the biggest promises of outcome-based pricing. But predictability depends on how outcomes are measured and how consumption is managed over time.

The strongest pricing models use less granular, more predictable units of measure. When pricing is tied to every individual task or action, costs become harder to anticipate because they depend on how many steps AI takes to complete the work. 

Imagine hiring a contractor to build a cabinet and paying for every saw cut along the way. The final bill depends on how the work is completed, not the cabinet you want delivered, and is not predictable nor controllable by you.

How you're billed matters, too. Annual budgeting gives organizations the flexibility to scale AI over time instead of trying to predict demand every month. Pricing expressed in familiar currency rather than proprietary credits is easier to understand, compare, and budget for.

Organizations should have a clear view of what they're paying for, how consumption is tracked, and what controls are available to manage spending.

As AI takes on a larger role in service delivery, organizations need pricing that can support a growing range of outcomes without adding complexity to their operations.

4. Plan for AI at scale

Today's AI handles only a fraction of the work it eventually will. As AI becomes capable of resolving more customer requests and completing more operational work, pricing models need to evolve alongside it.

Leaders should look for models that can accommodate new use cases, more complex work, and changing service operations without introducing unnecessary complexity.

As AI takes on a larger role in service delivery, organizations need pricing that can support a growing range of outcomes without adding complexity to their operations. For growing teams, outcome-based pricing can also make AI more accessible by tying costs to successful resolutions, allowing businesses to expand their AI investment as the value it delivers grows.

Getting outcome-based pricing right

Outcome-based pricing represents an important shift in how businesses invest in AI. Paying for outcomes instead of seats or usage creates a stronger connection between AI investment and business value.

Pricing should strengthen the partnership between vendors and customers – we have seen this in reality with our Zendesk customers today. The best models create shared accountability for outcomes both sides can understand, measure, and improve together. That gives vendors every incentive to help customers achieve meaningful results as AI continues to evolve.