According to Five9’s “2026 Business Leaders CX Report,” 90% of CX organizations are now experimenting with or implementing AI.
Overall, the report found that organizations are broadly aligned on AI adoption, but sharply divided on governance, implementation strategies, and how to use it in customer service without creating new complexity or eroding customer trust.
Adoption of AI may be widespread, but there is still no consensus on the best way to implement it. Respondents are almost evenly split between end-to-end platforms, hybrid environments, and cutting-edge solutions, indicating that no standard AI architecture is emerging in the industry. Organizations reach the same destination through very different paths.
The same lack of consensus is found in infrastructure. While 84% of organizations are transitioning from on-premise systems to the cloud, the majority continue to operate hybrid environments. Rather than rushing to complete cloud migrations, many appear to be preserving flexibility as AI models, vendors, and customer expectations continue to evolve.
This emphasis on flexibility extends to AI platforms themselves. Many organizations are building environments that allow them to choose different models for different tasks instead of relying on a single vendor. The goal is not so much to find a single platform that does everything, but rather to maintain the freedom to adapt as technology evolves.
Trust is becoming one of the biggest operational challenges.
Data security is the top concern, cited by 31% of respondents. Reliability, scalability and customer acceptance each accounted for 27% of respondents, while ethics, regulatory compliance, infrastructure, AI experience, budget constraints and customer discomfort all registered above 20%. Only 4% say they have not encountered significant implementation challenges.
Organizations are also selective about where to implement 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 control (41%), speech and text analytics (40%), real-time compliance monitoring (39%), and agent support (38%). These applications improve employee efficiency, consistency and performance by adapting to the workflows that companies already understand.
Customer-facing implementation remains low
Customer-facing capabilities are advancing more gradually. The distribution of knowledge creation is at 30%, journey analysis at 28%, and personalization at 26%. These use cases require richer customer data, stronger governance, and greater trust in AI-generated decisions, making them more difficult to deploy at scale.
This suggests that organizations are demonstrating the value of AI at an operational level before expanding into more sophisticated customer experiences.
Infrastructure decisions tell a similar story. Today, 74% of respondents operate hybrid customer service environments. Only 16% have fully cloud-based deployments, while 10% remain entirely on-premise.
Moving between these 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). Only 4% of organizations report no migration challenges.
Taken together, these findings suggest that flexibility is becoming part of the strategy. With AI capabilities rapidly evolving, organizations appear reluctant to commit to technological decisions that could limit their future options.
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Changes in the organization of customer service work
The report also highlights a broader shift in the way customer service work is organized.
Among US respondents, 45% cite increased efficiency and productivity as the top benefit of AI. Another 42% report improved decision making, while 42% say AI has improved customers’ perception of their organization.
Artificial intelligence is also changing the way agents spend their time. Overall, 45% of respondents say AI provides better data-driven decision support, while 44% say it helps agents handle exceptions and judgment calls. Routine work like documenting interactions, retrieving knowledge, translating conversations, summarizing customer interactions, and handling self-service requests is increasingly handled by AI, allowing agents to focus on situations that require context, empathy, and human judgment.
Organizations are also seeing financial returns. In every AI use case measured, about nine in 10 respondents report a positive ROI, indicating that the discussion has largely shifted from whether AI creates value to where it creates the most value.
The findings describe an industry that is entering a more mature phase of AI adoption. The implementation of artificial intelligence is now table stakes. Competitive advantage lies in operational execution: creating governance that customers can trust, giving teams the flexibility to adapt to evolving AI, and using technology to improve customer service.
The report can be downloaded here. (Registration required)
