By: Barbara Dondiego

When customers speak with an AI voice agent, they are inherently comparing it to their conversations with other humans. They hear how quickly it responds, how natural it sounds, and whether the call remains clear from beginning to end. Delivering that experience globally means coordinating AI applications, carrier networks, contact center platforms, and, in many enterprises, a mix of cloud and legacy systems. As AI moves from pilot programs into production, cloud voice can provide the orchestration layer needed to connect those environments and scale customer interactions reliably.

Voice remains essential when a customer’s issue is urgent, complicated, or personal. A 2026 ServiceNow study of more than 27,000 customers found that 87 percent still want to pick up the phone when an issue becomes complicated. Yet global voice is difficult to manage. Multiple regional and local carriers, inconsistent call quality, lengthy provisioning, limited performance visibility, and disconnected infrastructure have become familiar sources of cost and complexity.

Companies are responding by moving their contact center and communications environments to the cloud, creating a foundation for centralized management, performance data, software, and automation. The 2025 State of International Voice for the Contact Center report found that 54 percent of contact center calls were handled in the cloud, with respondents expecting that share to reach 56 percent by 2028.

This transition also gives enterprises an opportunity to reconsider how voice supports the wider customer experience. AI applications, contact center platforms, routing logic, and performance data must work together to support sophisticated interactions in real time.

Voice AI Raises the Infrastructure Standard

Metrigy’s latest CX Optimization study found nearly 85 percent of consumers would rather speak with a human than an AI agent. Enterprises are still investing heavily in conversational AI, automated agents, and real-time agent assistance. That apparent tension reflects how organizations expect AI to work over time: handling routine needs, helping human agents respond effectively, and making service available to more customers. They are betting that AI will be a significant net-positive for customers and their own business operations.

AI is already widely adopted across the contact center. The State of International Voice report found that 81 percent of organizations have enabled their voice environments with AI. Use cases already include automated Tier 1 support, improved IVR, caller authentication, conversation summaries and real-time agent assistance. As these applications expand, they can increase call activity and place greater demands on integration, performance and operational management. Emerging uses such as proactive AI agents, voice biometrics and multimodal experiences will extend those demands further.

Regional regulations, languages, carrier requirements and customer expectations add another layer of complexity for global enterprises. A voice AI application may perform well in a controlled pilot, yet scaling it across countries requires consistent connectivity, performance, and compliance. CIOs and contact center leaders must account for the complete path between the AI application and the customer.

What AI-Ready Cloud Voice Requires

Cloud voice has emerged as a critical control layer for AI, CX, CCaaS, UCaaS, and the broader enterprise technology stack. Many existing telephony environments, however, remain fragmented across providers, networks, and regions. They were not designed to support real-time AI conversations at global scale. Those conversations still travel across carrier networks, requiring voice infrastructure to connect AI applications with the telephone numbers, routing systems, networks, and contact center platforms needed to reach customers.

Latency is especially important. Customers notice awkward pauses, dead air, and audio disruptions. Even a short delay can make an AI conversation feel unnatural, lead customers to interrupt the agent, and weaken confidence in the experience. Optimizing how a call traverses the global network can reduce telephony-induced latency by 50 to 100 percent, which can make or break a customer’s perception of the call. Delivering natural conversations therefore requires close attention to the entire call path.

Enterprises also need flexible integrations between voice infrastructure and their chosen AI, CCaaS, and CX applications, which are expanding and evolving more than ever before. Vendor-agnostic connections allow organizations to adapt as models, applications and business requirements change, without rebuilding the underlying voice environment.

Other requirements include routing based on customer context, geography, availability and business rules; automated provisioning that supports expansion; and centralized administration of providers, numbers and networks.

Performance data must also be available across the technology stack so teams can understand how voice quality affects AI outputs, agent performance, and customer outcomes. With a human agent out-of-the-loop and unable to manually notice call quality issues or phone numbers out of service, analytics for automated diagnostics become mission-critical to ensure smooth operations.