Zendesk CEO Tom Eggemeier has championed the company’s innovative agentic AI pricing model.
The CRM stalwart blends outcome-based and flexible approaches to become the “first and only company offering flexible pricing” for AI agents.
Speaking at Zendesk Relate 2025, the CEO used his keynote speech to discuss the vendor’s pricing policy – alongside several of its major releases and product enhancements.
The pricing model ensures that customers are only charged when Zendesk’s AI agents successfully resolve issues autonomously.
While other vendors have considered such pricing models. Yet, defining what “good” outcomes look like – on a customer-by-customer basis – has proven a stumbling block.
Zendesk, however, believes it has found the answer by only charging customers when AI autonomously resolves the issue.
It’s also offering a starter usage level of no additional cost, so companies can first nail their AI agent strategy and then scale it.
As companies scale, they can pinpoint the intents they’d like AI agents to handle and those they’d like humans to handle – dialing this up and down as they refine their strategies.
The pricing model scales up and down with the contact center intuitively, enabling that flex.
In presenting this pricing approach, Eggemeier took aim at some of Zendesk’s competitors.
He argued that while they might declare outcome-based pricing as the future of the sector: “The farthest any of them have gone is offering consumption-based or interaction-based pricing.
Absolutely no one at our scale offers true outcome-based pricing – because no one else is as committed to aligning with your success as we are at Zendesk.
Eggemeier expounded on these remarks, providing a personal example where he recently used an airline’s AI agent to rebook his connection but lost service before completion.
Assuming the booking was finalized, he later called a human agent – only to find they had no record of the interaction with the AI agent.
Forced to start over, the CEO explained how this led to him facing an eight-hour delay while others secured new flights ahead of him.
While Eggemeier’s experience may seem fairly innocuous, he explained how it perfectly encapsulates the need for more sophisticated pricing models.
After all, the airline’s vendor “likely still charged them $2” for the AI interaction, despite it failing to resolve the issue.
With Zendesk’s outcome-based pricing, the customer only pays for successfully resolved issues.
“If Zendesk had powered that airline’s service, I wouldn’t have been charged for an unresolved AI interaction,” continued Eggemeier.
And if the AI agent had successfully rebooked my flight, the human agent would have had full context – ensuring a seamless, frustration-free experience.
Zendesk: An Innovator in the Agentic AI Era?
Outside of the tangible benefits of Zendesk’s AI agent pricing model, Eggemeier believes that it is a component of the company’s larger “revolution toward resolutions” strategy.
He argues that Zendesk’s customer-centric use of agentic AI positions the vendor as a leader in the shift toward resolution-focused service.
During the keynote, he revealed that Zendesk currently serves over 10,000 AI customers, with rapid growth expected to add tens of thousands more by 2025.
Indeed, the company’s new Resolution Platform took center stage during the keynote, with Eggemeier claiming that “the only metric that matters in customer service is resolution.”
The solution serves as a hub for AI agents that continuously learn and automate customer interactions. It includes pre-built AI agents along with an AI Agent Builder for creating customized solutions.
The platform also features a Knowledge Graph, which consolidates information for AI agents, and a Knowledge Builder that enhances content by analyzing successful resolutions.
Additional tools include an Action Builder to integrate AI and human workflows and a no-code App Builder for developing custom applications.
To ensure performance and transparency, the platform offers a QA module, an AI Insights Hub for monitoring AI effectiveness, and AI Reasoning Controls that provide real-time visibility into AI decision-making.
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