# Five9 Survey Finds Consumers Still Prefer Human Support > Source: > Published: 2026-08-12 07:28:00+00:00 # Five9 Survey Finds Consumers Still Prefer Human Support Five9 released a June 24 survey of 3,000 consumers and 600 customer-experience decision-makers across the U.S., U.K., and Germany, finding that 80% of consumers are willing to use AI-powered customer service but two-thirds still prefer speaking with a human. Retail Gazette's August 12 coverage highlighted the handoff gap: 92% of surveyed organizations have implemented or piloted AI, yet 83% of consumers still repeat information after transfers at least sometimes. Five9 released its 2026 Business Leaders Customer Experience Report on June 24, based on an April survey conducted with Hanover Research. The study covered 3,000 consumers and 600 customer-experience and contact-center decision-makers across the U.S., U.K., and Germany. It found that **80% of consumers** were willing to use AI-powered customer service, while **two-thirds** still preferred speaking with a human. Retail Gazette revisited the findings for a retail audience on August 12. ### Adoption is ahead of the experience Five9 reported that **92% of surveyed organizations** had implemented or piloted AI in customer service. Consumer expectations were more conditional: 71% said it was very or extremely important to know when they were interacting with an AI agent, and respondents expected a clear route to human help. The sharpest gap appeared during transfers. Nearly all surveyed decision-makers said their organizations preserve context when moving a conversation from AI to a human agent, but 83% of consumers said they still have to repeat themselves at least sometimes after a transfer. That comparison reflects two groups' survey responses rather than a technical measurement of handoff performance, but it identifies a concrete experience problem. Forbes and Retail Gazette both framed the findings around retailers' growing use of AI for discovery and service. The underlying Five9 survey was broader than retail, however: participants had interacted with customer service across industries including healthcare, financial services, retail, travel and hospitality, higher education, sales, outsourcing, and other customer-service settings. Five9 is a customer-experience platform provider, not the e-commerce consultancy described in the Forbes coverage. ### What applied-AI teams can measure For customer-experience and applied-ML teams, the report supports measuring escalation quality separately from chatbot adoption. Useful operational checks include whether an agent receives the conversation history, whether customer identity and intent survive routing, and how often a transferred customer must restate the problem. That is an LDS interpretation of the reported handoff gap, not a benchmark supplied by the survey. The vendor-sponsored study does not compare models, architectures, or production stacks, and its business-side findings are self-reported. Its strongest practical value is therefore the mismatch it exposes between organizations' confidence in context preservation and consumers' reported experience. ## Key Points - 1Five9 and Hanover Research surveyed 3,000 consumers and 600 customer-experience decision-makers in April 2026, publishing the findings on June 24. - 2Although 92% of surveyed organizations had implemented or piloted customer-service AI, 83% of consumers said they still repeat information after AI-to-human transfers at least sometimes. - 3The report supports measuring transfer continuity and repeat-contact friction, but it does not benchmark models, architectures, or production systems. ## Scoring Rationale The survey offers useful evidence for teams deploying AI customer-service systems, particularly around disclosure and escalation quality. It is a vendor-sponsored perception study rather than a model, platform, regulatory, or independently measured performance development, which limits its broader technical impact. ## Sources Primary source and supporting public references used for this report. Practice with real Retail & eCommerce data 90 SQL & Python problems · 15 industry datasets 250 free problems · No credit card [See all Retail & eCommerce problems](/problems/datasets/retail)