What is resolution-based pricing? | Decagon

Resolution-based pricing

Resolution-based pricing is a model for charging businesses based on the successful outcomes delivered by autonomous AI systems in customer service. Unlike traditional software models that bill by users, time, or access, this model charges only when the AI completes a task without human help. It’s a form of outcome-based pricing, but with a specific, measurable result: resolution.

For example, in customer support, companies pay a fixed fee only when the AI fully handles and resolves a customer issue. If the case is escalated to a human, there’s no charge. This model aligns cost directly with value delivered.

To illustrate, let’s say a company uses AI to manage customer service conversations:

You pay only when the AI resolves a conversation from start to finish.
You don’t pay if the AI fails and the case is passed to a human.
The price per resolved conversation is higher than basic usage pricing, but only for actual results.
Volume discounts are often available for companies resolving high numbers of cases with AI.

This approach rewards outcome delivery, not just usage.

Key benefits of resolution-based pricing

Resolution-based pricing is often more accurate and effective than usage-based pricing for several reasons:

Value aligned with cost—Businesses pay for what they get (resolutions) rather than what they use. This creates accountability and makes it easier to track ROI for AI adoption.

Incentivizes AI performance—Revenue is directly tied to the resolutions AI can achieve, putting the onus on vendors to improve resolution rates and quality of interactions.

Lower risk for customers—Customers only pay for actual resolutions, so they don’t pay if AI fails. It’s an effective way for companies to dip their toes in AI technologies without putting too much skin in the game.

Challenges and trade-offs of resolution-based pricing

While the advantages make it an appealing model, resolution-based pricing can be complicated:

What is a “resolution”?—Defining what a resolution is can be tricky, as not all cases end wrapped up in a bow. A customer may leave the chat mid-conversation, or AI could present an answer that doesn’t fully solve a customer’s issue. Gray areas can lead to billing disagreements.

Billing can be unpredictable—Resolutions may vary month-to-month, making it tough to forecast costs compared to usage-based pricing.

Quality is hard to measure—Edge cases and vendors predisposed to push issues toward “resolved” can present risks to customers. Customer satisfaction (CSAT) scores help counter this risk.

Resolution-based pricing makes the most sense when resolutions are easy to define, AI is highly capable and accurate, and businesses are comfortable linking billing to labor savings or task automation. The ideal use case is high-volume customer support environments where performance is both measurable and repeatable.

It’s also worth noting that resolution-based pricing is a subset of outcome-based pricing. The latter is broader and may tie price to more general metrics like increased sales or engagement that can be harder to measure with precision. Resolution-based pricing focuses on clear, measurable outcomes that make billing and performance easier to align.