Interview: Zendesk CEO on how resolution pricing is revolutionising AI agent value

Lara Williams

Thu, Jun 5, 2025, 9:03 AM 5 min read

Companies at the leading edge of automated customer service delivery would say that resolution—or outcome based pricing—is more aligned with the value an enterprise client derives from that product. And it’s a timely proposition as the hype around AI abates, and many businesses are starting to scrutinise their return on investment (ROI) for AI transformation projects.

Cloud customer service platform Zendesk's CEO Tom Eggemeier proffers that the company is the only one applying resolution pricing at scale. But much depends on the definition. The company works with a standard definition which its own customers are comfortable with, but may not be consistent industry-wide. “There’s some nuance in what a resolution actually is,” says Eggemeier.

Eggemeier explains how he was on an aeroplane in the US about to take off, when he realised that he would not make his connecting flight. He accessed the airline’s app and a chatbot completed around 90% of rearranging his schedule before the flight attendant asked him to turn his phone off.

On landing, Eggemeier couldn’t access the chatbot and ended up engaging with a human agent who didn't have any context for the query but ended up solving it nevertheless. “The airline was charged $2 for the chatbot interaction, even though it wasn't solved. So, the airline actually paid more money to the to the software company, by paying $2 for AI agent interaction, and they were also paying for a human seat,” explains Eggemeier.

With Zendesk’s pricing model, the fact that the query was not resolved by the AI agent would not generate a charge. This alignment of outcome-based pricing with AI, is something that is fairly unique in the industry, according to Eggemeier. “There are some really small players that are on this outcome-based pricing, but we're pretty much the only bigger player that's on a true resolution based pricing,” he adds.

Zendesk's proposition is a hybrid traditional SAS based seat model with levels of resolution pricing built in to capture the uncertainty of how many times a query may require human intervention.

“We want to give people the flexibility because it's important to know what your costs are up front,” says Eggemeier. “Usually for the first year, if you exceed predicted resolutions, because we want to drive more automation, and achieve ROI of the labour arbitrage there, we kind of cap it so that people have a little more predictability,” he adds.

More resolutions means less seats, and Eggemeier sees this as an equation of value between the two, with flexibility built in as critical. After all, businesses are only at the start of their automation journey where flexibility to transfer between a high-cost human interaction and a cheaper automated one will help the overall path towards greater automation.