AI adoption hits 90% as CX deployment paths diverge

Ninety percent of CX organizations are now piloting or deploying AI, according to Five9’s “2026 Business Leaders CX Report.”

Overall, the report found that organizations are largely aligned on adopting AI, but sharply divided on governance, deployment strategies, and how to use it in customer service without creating new complexity or eroding customer trust.

AI adoption may be widespread, but there’s still no consensus on the best way to deploy it. Respondents are almost evenly split among end-to-end platforms, hybrid environments, and best-of-breed solutions, indicating that no standard AI architecture is emerging across the industry. Organizations are reaching the same destination through very different routes.

The same lack of consensus shows up in infrastructure. While 84% of organizations are somewhere in the transition from on-premises systems to the cloud, most continue to operate hybrid environments. Rather than rushing to complete cloud migrations, many appear to be preserving flexibility while AI models, vendors, and customer expectations continue to evolve.

That emphasis on flexibility extends to AI platforms themselves. Many organizations are building environments that let them choose different models for different tasks instead of relying on a single provider. The goal is less about finding a single platform that does everything and more about maintaining the freedom to adapt as technology evolves.

Trust is becoming one of the biggest operational challenges.

Data security ranks as the top concern, cited by 31% of respondents. Reliability, scalability, and customer consent each concern 27% of respondents, while ethics, regulatory compliance, infrastructure, AI expertise, budget constraints, and customer discomfort all register above 20%. Only 4% say they haven’t encountered significant implementation challenges.

Organizations are also being selective about where they deploy AI first. The most common implementations focus on improving existing operations rather than reinventing customer experiences.

Self-service automation leads adoption at 42%, followed by quality management and automated quality assurance (41%), speech and text analytics (40%), real-time compliance monitoring (39%), and agent assistance (38%). These applications improve efficiency, consistency, and employee performance while fitting into workflows companies already understand.

Customer-facing deployment remains low

Customer-facing capabilities are advancing more gradually. Knowledge authoring deployment is at 30%, journey analytics 28%, and personalization 26%. Those use cases require richer customer data, stronger governance, and greater confidence in AI-generated decisions, making them more difficult to deploy at scale.

That suggests organizations are proving AI’s value operationally before expanding into more sophisticated customer experiences.

Infrastructure decisions tell a similar story. Today, 74% of respondents operate hybrid customer care environments. Only 16% have fully cloud-based deployments, while 10% remain entirely on-premises.

Moving between those environments remains challenging. Data security and privacy concerns lead migration issues at 36%, followed by integration with existing IT infrastructure (35%), data migration and reliability (34% each), customer experience disruptions and regulatory compliance (33%), software adaptation (32%), implementation costs (31%), and technical support, scalability, and staff training (29% each). Just 4% of organizations report no migration challenges.

Taken together, those findings suggest flexibility is becoming part of the strategy. With AI capabilities evolving rapidly, organizations appear reluctant to commit to technology decisions that could limit their future options.

Changes in the customer service work organization

The report also points to a broader shift in how customer service work is organized.

Among U.S. respondents, 45% cite increased efficiency and productivity as AI’s biggest benefit. Another 42% report improved decision-making, while 42% say AI has improved customers’ perceptions of their organizations.

AI is also changing what agents spend their time doing. Overall, 45% of respondents say AI provides better data-driven decision support, while 44% say it helps agents manage exceptions and judgment calls. Routine work such as documenting interactions, retrieving knowledge, translating conversations, summarizing customer interactions, and handling self-service requests is increasingly being handled by AI, allowing agents to focus on situations that require context, empathy, and human judgment.

Organizations are seeing financial returns as well. Across every AI use case measured, roughly nine in 10 respondents report positive ROI, indicating the discussion has largely shifted from whether AI creates value to where it creates the greatest value.

The findings describe an industry entering a more mature stage of AI adoption. Deploying AI is now table stakes. The competitive advantage lies in operational execution — building governance that customers can trust, giving teams the flexibility to adapt as AI evolves, and using the technology to improve customer service.

The report can be downloaded here. (Registration required)