When AI needs to hand an interaction to a human, the transition should feel like one continuous conversation.
Customers don't care where AI ends and a human begins. They care whether the conversation continues without friction.
Five9's 2026 Business Leaders Customer Experience Report found that while 80 percent of consumers are willing to use AI-powered customer service, two-thirds still prefer speaking with a human. And 83% say they still have to repeat themselves at least sometimes after a transfer. The challenge for CX leaders is making the transition between the two feel like one continuous conversation. That is not happening in many organizations, as they often cannot see whether the context was usable, complete or acted on at the point it mattered.
Sam Counterman, SVP of GTM Technology & AI Operations at Five9, describes this as a measurement problem.
“I don’t think the industry is there yet as to how you quantify that and measure a successful handoff transition.”
A handoff can be technically successful but still fail the customer. The AI can recognize an escalation, route the interaction to a live advisor, and pass a transcript into an agent desktop. But if the advisor has to search for the detail, asks the customer to repeat it, or cannot take the right action the customer has effectively started again.
Fixing the handoff gap requires leaders to look beyond transfer rates. They need to define the outcome they want, join operational and customer data, and design the agent experience around quick, relevant context.
Here is a practical framework for doing that.
1. Define a Successful Handoff In Customer Terms
The first step is to establish what a good AI-to-human handoff should achieve for the customer. That definition should be specific enough to measure and flexible enough to account for different types of interaction.
Three indicators are particularly useful:
-
Repeat-contact rate: Does the customer need to contact the organization again about the same issue after the handoff?
-
Customer effort at the transfer point: Did the customer have to repeat information, switch channels unnecessarily or explain why they were being transferred?
-
First-contact resolution after the handoff: Did the advisor resolve the issue during that interaction, without avoidable further transfers or follow-up?
No single measure gives a complete picture. Customer effort scores are subjective, repeat contact can be caused by issues outside the handoff and resolution can be defined too broadly. But when combined, they can indicate whether the transition made the customer’s problem better or worse.
The advisors receiving the transfer should also be part of the scoring model. They can identify whether the AI understood the customer’s intent, captured the correct information, recognized the point at which escalation was necessary and equipped them to resolve the issue.
A short advisor quality score after the interaction can reveal where the AI experience is helping and where it is creating more work.
2. Join Operational Metrics to Customer and Advisor Feedback
Most organizations already hold relevant data, but the issue is that it often sits in separate reporting lines.
Operations teams track transfer volumes, containment, repeat contacts and queue performance, while CX teams track surveys, complaints, sentiment and satisfaction and quality teams review selected conversations. These views rarely connect at the level of an individual interaction or transfer path.
That makes the reporting descriptive rather than actionable.
Leaders need to connect the operational event to feedback from the same interaction. If a customer reports that they had to repeat themselves, the organization should be able to identify the AI journey, the escalation reason, the queue entered, the context supplied to the advisor, and the final outcome.
Counterman recommends learning from strong handoffs as well as weak ones.
“It’s equally as important to know when you don’t have a good score. And [understand], why did that happen?”
Useful handoff measures include:
-
Context transfer completeness: Did the advisor receive everything the customer had already told the AI, with no information lost or needlessly requested again?
-
Handoff necessity and precision: Did the interaction need to move to a person at that point, and was it routed to the right advisor or team?
-
Post-handoff resolution efficiency: Did the work completed by the AI allow the advisor to resolve the case faster and more effectively than a cold-start interaction?
-
Customer and advisor transfer feedback: Did both parties consider the transition clear, useful and proportionate to the task?
The objective is to identify which handoff outcomes matter, what metrics evidence them and how teams will respond when performance falls short.