How to manage the AI-to-human handoff

After months of intense effort, a large SaaS company felt like it had finally perfected its self-service AI agent. The AI agent was able to handle nearly all customer questions without human assistance.

But when they took a closer look at the transcripts of those chats, a pattern began to emerge. Customers frustrated by the AI’s inability to solve their problems left the conversation and never came back. Satisfaction scores began to drop. 

“We were working overtime on deflection,” says the manager in charge of the project. “We had to dial that back and make sure people could always access human support when they needed it.”

As more and more support functions become automated, companies are grappling with the question of when to let AI drive the conversation and when it’s time for humans to take the wheel. 

“Not everything can be solved with AI,” notes Payal Patel, a Freshworks solution engineering leader. “We still need to think about the human angle. No matter how sophisticated AI agents become, you still need a human to help troubleshoot the complex technical issues.”

Getting the AI-to-human handoff right can lead to greater efficiency and increased customer loyalty; getting it wrong can lead to dissatisfaction and churn. But when and how to make the handoff depends on a range of factors, such as the complexity or sensitivity of the issues at hand, the customer’s relationship with the company, or—most importantly—customers’ rising levels of frustration. 

The stakes are high

When agentic support systems are implemented well, they deliver benefits for both businesses and customers. AI-driven support can improve first response times by 43% and cut operational costs by 30%. An April 2025 study showed that well-designed AI agents can foster stronger emotional connections with customers, leading to increased loyalty and reduced churn.

Still, according to the 2025 State of Customer Service and CX report, 68% of consumers would prefer to talk to a human customer service agent, and 63% say they’d take their business elsewhere if human support weren’t available.

Flubbing the handoff—or making no handoff at all—can have seriously negative repercussions. 

“The biggest mistake companies make is treating AI like an interactive voice response tree,” says Barry Kunst, VP of marketing for Solix Technologies, a cloud data management and AI solutions provider. “Customers learn that ‘chat with us’ really means ‘argue with a bot that won’t listen.’ When they get the opportunity, they dump that company for a competitor that serves them better.”

Worse, when customers have a bad experience, you may never hear about it. According to Isabelle Zdatny, head of thought leadership for Qualtrics XM Institute, less than a third of customers share those experiences with the company. Half simply cut their spending, while others walk away entirely. 

“Leaders risk mistaking this silence for a healthy relationship with their customers,” she says. “But silence today often means disengagement, and disengagement has business consequences. If you sacrifice emotional connection for speed, you end up automating away your loyalty.”

What triggers a handoff

To get this delicate balance right, companies need to establish rules for handoffs, says Viktoria Lozova, a digital behavior specialist for Angle2, a B2B software performance company. For example, when her team built an AI support assistant for a clinical EHR system, they spent most of their time designing escalation strategies. If the AI’s first two attempts at troubleshooting fail, or a question lands outside the AI agent’s body of knowledge, it automatically routes the customer to a person.

The rules for and point of handoff may vary by industry and the sensitivity of each issue, according to an analysis by Lawrence Young, principal value engineer for Freshworks. For example, when e-commerce customers are disputing charges or requiring complex refunds, a human representative needs to step in.

Healthcare customers should be transferred to a human agent for issues such as clinical triage, emergency check-in scheduling, or when their prescription refills are denied. Higher education institutions will want humans to address concerns around financial aid eligibility appeals and problems with international student visas.

And while many issues around travel can be automated, Freshworks analysis finds that customer loyalty improves when airlines make a point of personally handling issues around weather-based cancellations, mechanical rerouting, and changes to complex travel itineraries. 

“Asking 'Can AI solve this?' is the wrong question,” says Lozova. “The right question is 'Will the user trust AI to solve this?' Even if the AI is technically able to help, users still want humans for anything involving sensitive data.”

How do you know handoffs are working?

It’s not enough to implement rules for handoffs, says Patel. You also need to establish metrics to determine whether they’re effective.

For example, organizations should measure how long it takes a human agent to resolve issues after the AI hands them over. Longer average handle times may indicate the AI failed to adequately prepare human support representatives with sufficient context, forcing customers to repeat themselves. In addition, sentiment analysis can help support teams determine how often the system escalates due to user frustration. A high rate of frustration suggests the AI is repeating itself, causing friction.

The appropriate metric may also be determined by industry or the point of transfer. E-commerce businesses who hand off when refunds are escalated will see the outcomes reflected in deflection rates and revenue recovery, while healthcare providers may consider clinical accuracy and a patient effort score, or how easy it is to get to a provider. Higher education institutions might keep the students in mind, tracking task completion rates, or the percentage of students successfully submitting forms with the help of AI.

Detecting frustration before it’s too late

Ultimately, emotional signals should override everything else. When a chat AI agent or voice AI agent senses user frustration, that's when a representative needs to step in.

“The moment the system detects hesitation, repeated rephrasing, emotional language, or signals that the customer is working harder than necessary, it needs to escalate,” says Greg Boone, CEO of Walk West, an “AI-fluent” strategic marketing agency. “A good model can predict these moments, and great companies should build that trigger into the workflow instead of waiting for someone to shout for a human."

Transparency matters, too. Customers need to know if they’re communicating with a bot and how to engage with a human when needed. A warm handoff from AI to agent is also essential, so customers don’t have to keep repeating the same information. 

“We don’t wait for users to figure out how to escape,” says Lozova. “We open the exit door for them.”

Freshworks’ AI advisory program helps companies evaluate when to hand off customers from an AI agent to a human.