For years, many contact center strategies treated voice as a utility and a high-cost channel to contain. Digital self-service, chat, messaging and automation were positioned as lower-cost alternatives, while phone support was often reserved for issues customers could not resolve any other way.
As AI moves into the contact center, voice is becoming a more strategic part of the customer experience. Rather than making voice less relevant, it has the potential to make voice more accessible, more intelligent, and more central to the customer experience — as well as more cost-effective for the contact center.
The role of voice won’t really change with AI, especially for complex or difficult scenarios, where it will remain the preferred channel for engaging with human agents
Whatwillchange is how AI will expand the use cases for voice beyond telephony as a digital channel in the form of AI agents, especially for self-service. With these digital interactions being automated with AI, not only can the contact center handle more inquiries using voice, but also at a lower cost.
For CX leaders, the question is no longer whether voice will remain important. It’s whether their organizations have the infrastructure and operating model to make AI-powered voice work at scale.
The net result we expect will be more voice, not less. For contact center and enterprise IT leaders, that means rethinking the voice layer beneath the AI stack.
Voice is becoming “ungated”
Voice remains particularly valuable for interactions that are complex, emotional, urgent or difficult to explain through a series of typed messages.
Customers may be happy to use digital channels to check an account balance, reset a password or track a delivery. But when a flight is delayed, a claim is disputed, an account is at risk, or a high-value purchase requires guidance, many customers would rather have a conversation with someone — or something — that can understand the full context.
Historically, the high cost of a live agent gave companies an incentive to make voice harder to access. Customers were often pushed toward text-based digital experiences before being offered a phone number. These channels are less costly than live agents, but the quality of outcomes is highly variable.
AI economics offers a new path for contact centers, where voice-based self-service options enable better outcomes than with legacy tools, and can scale very cost-effectively. Today’s AI agents can resolve routine requests, collect information before a handoff, summarize an interaction and help human agents handle more complex conversations. This is the “ungating” of voice, where these new ways of engaging make voice more accessible to customers, and at any time of day or night.
That doesn’t mean every interaction will become AI-only. It means organizations can be more deliberate about where voice creates value without treating every call as an expense to be avoided. As the experience improves, customers may increasingly return to voice for situations where conversation is simply more effective than typing. This is why having the right voice infrastructure is so important, as it is foundational to enabling these new, AI-driven customer experiences.
The future is a blended model
Ultimately, we don’t foresee a contact center run entirely by AI agents or entirely by humans. Rather, we expect a combination of three interaction models:
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AI-led interactions for straightforward, well-defined requests.
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Human-led interactions for sensitive, high-value, nuanced or exception-based situations.
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Hybrid interactions in which AI and humans participate in the same customer journey.
In a hybrid interaction, an AI agent might authenticate the customer, identify intent, gather relevant information, and attempt to resolve the issue. If human judgment is required, the interaction can be transferred with the relevant history, customer context and AI-generated summary intact. The customer shouldn’t have to start over simply because the conversation changes hands.
AI can also make escalation and routing more intelligent. Rather than relying solely on a fixed IVR tree, organizations can consider real-time factors such as customer sentiment, frustration, issue complexity, customer value, agent availability, and projected contact center demand.
While personal AI agents (also called Machine Customers) are extremely new, we expect them to become more mainstream in the next three-to-five years. Over time, these AI agents will increasingly interact with businesses on consumers’ behalf, creating new machine-to-machine traffic patterns that organizations will need to identify, secure, and route intelligently.
This capability will be welcomed by some types of customers — prosumers — but it will also create opportunities for bad actors to use synthetic calls for fraud, identity theft, and other malicious purposes. As such, beyond the blended model, AI will create new forms of engagement, but these will require effective guardrails and security measures.
Infrastructure now shapes the experience
As voice becomes more central to the way AI is being used in the contact center, infrastructure becomes a strategic CX concern rather than simply an IT utility. The conversation about voice AI generally focuses on the AI itself — including the model, the agent, the naturalness of the conversation, and the ability to reason and take action. But none of that matters if the underlying call experience is poor.
A natural AI conversation depends on more than the quality of the underlying language model. It also depends on call quality, network reliability, routing efficiency, and especially latency. Even a highly capable AI agent will feel awkward if there’s a noticeable pause after every customer statement. Telephony-induced delay, unnecessary transfers, and multiple handoffs between platforms can quickly undermine the experience.
Latency is a particularly important issue. AI conversations need to feel natural, and even relatively small delays can make an interaction feel awkward or artificial. Telephony introduces another potentialcognition, AI processing, text-to-speech, networks, and other systems
Call quality remains equally important. Poor audio can affect not only the customer’s experience, but also speech recognition and the ability of AI agents to understand what the caller is saying. As humans become further removed from AI-driven interactions, contact centers lose an important sounds bad or when there is an unusual delay. An autonomous AI interaction won’t necessarily raise its hand and report the problem
That makes proactive diagnostics and observability increasingly important. CX leaders need visibility on both sides of the interaction: is the AI agent behaving appropriately, and is the underlying voice connection delivering the quality and latency required for a natural conversation? Those are related but distinct problems, and they may require different technologies and expertise.
In terms of providing the best possible voice experience, flexibility must also be taken into consideration. Maintaining control over phone numbers, routing, coverage and performance while preserving the ability to innovate and adopt new technologies is essential to a successful voice strategy in the contact center.
Visibility into every call
AI-driven voice creates two distinct but related monitoring challenges.
The first is the AI itself: Is it interpreting intent accurately? Following business rules? Providing safe, compliant, and appropriate responses?
The second is the voice experience: Is the call clear? Latency low enough for a natural conversation? Does the call take an unnecessarily complex route? Is there a problem with a number, carrier connection, or regional network?
These questions become more important when a human agent is no longer present to recognize and report a poor experience. Proactive diagnostics, call-quality monitoring, number testing and rapid root-cause analysis will become increasingly important operating capabilities. When a customer says that “the AI wasn’t working,” the contact center needs to determine whether the problem was the AI model, the configuration, the application, or the underlying voice connection.
This is an area where a voice provider with visibility into the carrier and network layer can play an important role. AVOXI, for example, emphasizes operational visibility into call paths and voice performance as part of its approach to supporting global enterprise voice through a single software platform.
The key point is that contact centers need visibility across the entire interaction, not just the AI application. AI platforms alone do not provide visibility into every element affecting end-to-end voice quality. When problems arise, contact centers need to understand whether the issue originates within the AI application, the voice infrastructure, the carrier network or another part of the interaction. This becomes especially important in multi-vendor environments, where consistent monitoring can help maintain voice performance across the different AI technologies being used.
Voice needs to become an integration layer
The strategic role of voice infrastructure is expanding beyond connectivity. Enterprises increasingly need a voice layer that connects customers to AI applications, contact center platforms, and human agents, while insulating the underlying voice infrastructure from constant application changes.
That becomes even more important as organizations experiment with AI. As the market evolves, enterprises will add, replace, and consolidate AI applications. A flexible voice architecture lets them make those changes without redesigning their entire telephony environment.
It also helps avoid unnecessary call routing. If a call enters one platform only to be routed to a third-party AI provider and then returned to the contact center for a human transfer, every additional hop can add cost, latency, and another potential failure point.
The challenge also becomes greater as organizations adopt multiple AI technologies, both for the contact center and other areas of operation. One platform may be better suited to a particular language or transaction type; another may offer stronger agent-assist or automation capabilities. A single customer interaction may touch AI platforms, CCaaS applications, unified communications systems, CRM platforms, and human agents. Every additional connection can introduce complexity, cost and latency.
This is one reason an independent cloud voice layer can become strategically valuable. The right voice platform providers can give enterprises a consistent voice foundation beneath changing AI, UCaaS, and CCaaS applications. Within the contact center, this means that customer context must flow seamlessly as calls move from one mode to another — and from one platform to another.
Flexibility is key. Enterprises should be able to change the applications sitting on top of their voice infrastructure without having to rebuild the underlying voice environment. Purpose-built voice infrastructure provides a vendor agnostic foundation, reducing dependence on any single AI or contact center platform while providing the integration flexibility needed to maintain a consistent voice experience as technologies evolve.
The more complex the AI ecosystem becomes, the more important it is to keep the voice path as simple and direct as possible. AI complexity aside, it’s important to state that telephony is still one of the most familiar and accessible ways for customers to initiate inquiries, and contact centers need to properly support the PSTN traffic that remains core to CX.
We are a long way from AI totally automating customer service, and app-based voice channels such as WhatsApp or Messenger serve different customer needs — rather than eliminating the need to support telephony as organizations integrate voice with AI.
Global scale raises the stakes
Most contact centers are in the early stages of deploying voice with AI. When run in pilot mode, the above issues can be managed and fine-tuned. The state of your voice infrastructure matters much more when scaling these deployments, especially on a global basis, where there will be more languages and carrier relationships to support.
Global deployments introduce additional requirements, including local phone-number availability, carrier relationships, call quality, regulatory compliance, data sovereignty, and security. Ensuring high performance on technical voice quality and speed, for example, becomes more challenging across international markets that collectively have diverse infrastructure and connectivity.
Some organizations may need calls and related data to remain within a particular region. Others need to support multilingual experiences across dozens of markets while maintaining efficient routing. These requirements cannot be addressed as an afterthought once an AI pilot moves into production.
Consolidation is therefore becoming more attractive. Managing dozens of local carrier relationships can create inconsistent service levels, limited visibility, and unnecessary administrative overhead. A more consolidated voice strategy can simplify governance, expose unused resources, and create a stable foundation for an increasingly dynamic application environment.
In addition, AI agents can extend service beyond traditional operating hours, enabling customers to resolve issues whenever they need to. The same applies to outbound calling, where voice-based notifications, reminders, and other interactions can be scaled globally, by language, at any time, in ways that legacy systems could never do.
A strategic mandate for CX leaders
CX leaders should not rely solely on an AI vendor to define the organization’s voice strategy. Voice now generates valuable customer data, feeds AI systems and carries interactions with significant revenue, loyalty, regulatory and fraud implications. It can no longer be treated as an isolated communications function.
As AI’s role expands in the contact center, the value of voice goes well beyond being a utility for moving telephony traffic. Rather, it is an experience-defining layer connecting customers, applications and people. Nowhere in the enterprise is that shift more important than the contact center, which by nature is data-rich, making it a natural use case for AI.
Equally important is the fact that a quality voice experience is paramount in the contact center, where the impact on the business is greater than anywhere else in the organization when using voice. When done right, the benefits of voice AI are strong enough that CX leaders need to recognize the importance of owning and managing voice infrastructure.
This requires rethinking the possibilities for voice that come with AI, and how strategic that becomes when modernizing the contact center. There’s simply too much at stake for voice to be treated as an embedded capability within any single platform. As AI reshapes the contact center, organizations will need a voice foundation that gives them the control and flexibility to support the technologies they use today and whatever comes next.
