The report found that 92% of organizations have already implemented or piloted AI use cases in customer service. At the same time, 80% of consumers said they are willing to use AI-powered customer service - a number that suggests acceptance is broad but conditional. Phone remains the most preferred channel overall, and that preference climbs with age: 23% of Gen Z, 33% of Gen X, 47% of Baby Boomers, and 66% of the Silent Generation.
The trust equation
Transparency is emerging as a non-negotiable. The report found that 71% of consumers consider it very or extremely important to know when they are interacting with an AI agent rather than a human. This, combined with the persistent preference for human support, creates a balancing act for contact centres. As Amit Mathradas, CEO of Five9, said: "AI has clearly crossed the threshold from promise to production in customer experience, but the next challenge is much harder than deployment."
Mathradas added that winning with AI in customer experience means "making every experience more relevant, more trusted and more human - giving customers choice, equipping agents for higher-value work, preserving context across every handoff and building AI strategies that can scale responsibly across the business." The data backs up the need for that human touch. While many consumers will engage with AI for routine tasks, they expect a clear path to a live agent when the situation calls for it.
The handoff that breaks trust
Perhaps the most striking disconnect in the report involves the AI-to-human handoff. Nearly all decision-makers said their organization preserves context during these transfers. Yet 83% of consumers reported having to repeat themselves at least sometimes after being handed to a human agent. This gap raises questions about whether companies are measuring handoff success accurately - or measuring it at all from the customer's perspective.
For customer support teams, this is where AI for Customer Support strategy either succeeds or fails. The handoff is not just a technical feature; it is the moment where a customer decides whether the system respects their time. Getting it wrong erodes the trust that AI adoption was supposed to build.
The infrastructure behind the ambition
AI execution depends on what sits beneath it. The report found that 84% of organizations are still moving from on-premises infrastructure to cloud-based systems. That transition matters because AI tools require cloud-native architecture to deliver the context preservation, speed, and data integration that customers expect. Without that foundation, even well-designed AI workflows can stumble.
Decision-makers are also split on how to build their AI stack. Some prefer end-to-end platforms, others choose hybrid approaches, and many opt for best-of-breed tools. There is no single playbook, the report notes, which means contact centre leaders must match their AI strategy - including models, workflows, governance, and human oversight - to each specific use case. For supervisors managing these transitions, an AI Learning Path for Call Center Supervisors can help bridge the gap between AI ambition and day-to-day execution.
Why this matters for customer support professionals
The numbers tell a clear story: AI is in your contact centre, but your customers still judge the experience by how well you handle the human moments. The 83% of consumers who repeat themselves after a transfer are not a statistic - they are a signal that handoff design needs more attention than it is getting. For support leaders, that means auditing not just whether AI works, but whether the transition from AI to human feels frictionless to the person on the other end. The full report is available at Five9.
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