AI call center: How voice and digital channels are changing CX

Despite years of innovation and investment into self-service and chat, voice remains a critical customer support channel, especially when it comes to complex or emotionally charged issues. This reality is shifting many organizations’ focus to the AI call center as a modern solution to rising customer expectations. Now more than ever, customers want faster, more personalized service, regardless of which channel they’re using.

This puts real pressure on customer-facing teams. Voice holds significant value, but it’s also the most expensive and hardest channel to scale.

AI in customer service is redefining how businesses deliver efficient, personalized, and valuable support. It’s revolutionizing how issues are understood, tickets are routed and resolved, and metrics are recorded across digital CX channels, including voice.

Whether through voice or digital channels, this guide explores what an AI call center is and how it’s reshaping AI-driven customer experience. You’ll learn about common use cases, benefits, best practices, and real-world examples of AI call centers in action.

More in this guide:

• What is an AI call center?
• The evolution of AI in call centers
• 10 ways to use AI in call centers
• Benefits of using AI in call centers
• AI call center vs traditional call center
• Best practices for implementing AI in call centers
• Real world examples of AI in a contact center
• Frequently asked questions
• Make the right call with AI

The evolution of AI in call centers

To stay relevant (and effective), traditional contact centers must keep up with evolving technologies—most notably, artificial intelligence (AI). As AI capabilities have advanced, CX operations have evolved beyond manual, reactive processes into intelligent, adaptive systems.

From manual call routing to AI-driven routing

Traditional manual call routing is mostly rules-based and reactive. Calls are sent to whichever agent is available or, even worse, funneled through rigid IVR menus that customers have to navigate. This commonly leads to long wait times and handoffs, high transfer rates, and customers having to repeat themselves after each transfer. As a result, first-call resolution (FCR) suffers.

With intelligent, AI-driven routing, AI can evaluate calls before and during an interaction with a CX agent. It can decide where to route the customer based on intent, sentiment, history, and context. This means customer requests can reach the right agent or resolution path more efficiently. The result is fewer transfers, faster resolutions, less customer friction and better use of agent expertise.

From manual QA to AI-powered QA at scale

Manual quality assurance (QA) is selective and retrospective. CX managers review a small sample of calls, giving feedback days or sometimes weeks later. Scoring can vary across reviewers, and because the call sample is limited, large gaps in visibility remain across customer interactions.

By contrast, AI-powered QA is comprehensive and continuous. AI evaluates 100 percent of interactions and gives immediate visibility into compliance risks, coaching opportunities, and exceptional performance. Scoring becomes more consistent as the benchmarks behind the scores are centralized and applied uniformly. This gives CX leaders a complete view of service quality and enables faster, more targeted improvements to the customer experience.

From agent execution to AI-assisted service management

In legacy call centers, agents are responsible for nearly all operational and administrative work. They must search for customer context, manually document calls, and navigate multiple systems throughout each interaction. This administrative burden pulls focus away from customers, while managers lack real-time visibility into active issues, agent performance, and customer sentiment.

AI-assisted service management solves this by summarizing calls, surfacing context for the agent and suggesting next steps. This improves employee experience, enabling your agents to focus on the work that really matters: delivering empathetic, high-value human interactions that build trust and resolve complex issues.

From historical reports to real-time insights

Traditional analytics rely on historical, sample-based reporting delivered through weekly or monthly summaries. These lagging indicators make it difficult to spot issues early or take timely action.

With AI analyzing every interaction, CX teams gain real-time insight into emerging trends, shifts in customer sentiment, and potential churn risk. This enables CX leaders to act proactively, make faster decisions, intervene earlier, and forecast demand, call volume, and emerging issues more accurately. The call center becomes a real-time intelligence engine, not just a reporting function.

Collectively, these shifts have made AI foundational to modern call centers. It transforms them from reactive service hubs into scalable, insight-driven engines for customer experience.

10 ways to use AI in call centers

Now we’ve covered the ways AI in the workplace has evolved, let’s look at exactly how you can leverage it in your day-to-day operations.

1. Solve complex issues with AI agents

AI agents can help teams keep up during periods of high call volumes where they would otherwise become overwhelmed and resolutions begin to slow down.

They do this by handling multi-step requests autonomously. Issues are only escalated to human agents when a conversation requires empathy, judgment, or exception handling. By using signals like intent and sentiment, AI agents can respond accurately and keep interactions moving. In some cases, it’s possible to automate over 80 percent of customer interactions, freeing up your CX agents to focus on the calls that really need the human touch.

This improves resolution rate, containment rate, and customer satisfaction (CSAT).

2. Enhance call routing

AI can also help address long handle times and frequent transfers caused by manual call routing based on availability.

With intelligent routing, AI automatically directs requests to the agent or team best suited to help the specific customer. Instead of relying on static rules, AI considers signals like customer intent, sentiment, language, and prior interactions to make routing decisions in real time.

By matching customers to agents with the right expertise from the start, teams can reduce average handle time (AHT) and transfer rates while improving first-call resolution (FCR).

3. Perform call quality control

Manual quality assurance (QA) relies on reviewing small samples of calls, which means many issues go unnoticed and feedback often arrives too late. AI-powered call listening removes this limitation and evaluates every call interaction for quality, compliance, and performance.

With AI, teams can:

• Review AI-generated transcripts from any call to assess conversations in detail and identify areas for improvement.
• Analyze insights at scale to track call center metrics, uncover trends, and spot recurring issues or shifts in customer sentiment.
• Evaluate agent performance consistently against customer service goals and benchmarks.

This increases your QA coverage, improves coaching effectiveness, and strengthens compliance.

4. Facilitate self-service and knowledge management

If it’s hard for your customer to find answers to common questions, or if your knowledge base is inconsistent, then they have no option but to get in touch with a human agent. This drives up call volume and pulls agents away from more complex issues.

AI-powered knowledge management helps solve this by surfacing accurate, up-to-date information in real time—both for customers using self-service and for agents during live interactions. By making answers easier to find and more consistent, AI improves self-service success and streamlines how knowledge is created, maintained, and applied.

As a result, teams can reduce call volume, lower cost per contact, and deflect routine inquiries to faster, more efficient resolution paths.

5. Reduce call wrap-up times

After-call work is one of the biggest drivers of longer average handle time (AHT) and agent fatigue. This is especially true when agents must manually document interactions and piece together context across systems.

AI call centers can solve this problem for agents in two ways:

• Call summaries: AI can automatically generate accurate summaries that identify key points, topics, and crucial information, such as customer complaints and required follow-ups.

• Call transcripts: AI provides full transcripts for each interaction, giving agents and CX leaders a reliable record to review, reference, or use for coaching.

By automating call wrap-up tasks, teams can reduce AHT, increase agent productivity, and shorten time to resolution.

6. Understand customer behavior

Teams can often lack visibility when it comes to customer sentiment and intent during calls. Without that context, it can be difficult for agents to respond effectively in the moment.

AI helps address this by analyzing cues like tone, language, and phrasing across live or recorded conversations to surface customer sentiment. This gives agents a better understanding of how a customer is feeling and what they need. Agents can then adjust their approach, respond more appropriately, and de-escalate situations when needed.

By understanding customer behavior in real time, teams can reduce escalation rates, improve resolution quality, and increase customer satisfaction (CSAT).

7. Support interactions with AI copilot

Finding the right information mid-call can be a challenge for CX agents. AI copilots can solve this by surfacing relevant information recommendations based on context in real time. This not only increases agent confidence and consistency, but it also reduces AHT and improves FCR.

8. Monitor performance and interaction quality

It can be difficult for managers to have a consistent view of agent performance across calls. With AI-enabled performance monitoring and trend detection, this is no longer an issue. As 100 percent of calls are evaluated by AI, managers are able to track key metrics like AHT and identify training and support needs, without relying on delayed or sample-based reviews.

9. Analyze data for smarter decision-making

In a traditional call center, even if managers can get a relatively consistent view of performance, they’re often relying on lagging reports and incomplete data. AI changes this by analyzing interaction data at scale and surfacing patterns, risks, and emerging issues across the operation.

This greatly increases forecasting accuracy, demand planning, and churn indicators, enabling managers to make more informed decisions about resource allocation and training priorities.

10. Implement conversational IVRs

Menu-based interactive voice response (IVR) can leave customers frustrated and increase abandonment rates. Conversational IVRs change this experience by allowing customers to speak naturally instead of navigating rigid menus.

Built on conversational AI, these systems can resolve simple requests automatically and route more complex issues appropriately. This increases containment rates, reduces call abandonment, and lowers customer effort on the path to resolution.

Benefits of using AI in call centers

Adopting AI call center software produces various benefits for service teams and their customers, including:

• More consistent CSAT: AI helps standardize service quality across interactions by reducing transfers, improving routing accuracy, and supporting faster resolutions. It also enables 24/7 support for routine requests and urgent issues. As a result, teams often see more consistent CSAT scores across channels and touchpoints.
• Boosted agent productivity: By automating routine tasks and supporting agents in real time, AI enables your team to handle more interactions without increasing workload. Organizations frequently report productivity gains–often exceeding 25 percent–as agents spend less time on admin and more time resolving customer issues.
• Scalable operations with faster time-to-value: AI allows call centers to scale support operations without adding complexity. Thanks to prebuilt workflows and automation, your team can expand capacity quickly and, in some cases, deploy AI-powered capabilities in days rather than months.
• Lower operating costs: AI reduces cost per contact by deflecting routine enquiries, improving FCR, and minimizing repeat contacts. Many organizations report operating cost reductions of up to 20 percent as a result of increased automation and efficiency.
• Automated quality monitoring at scale: AI enables continuous call monitoring of 100 percent of interactions, improving service quality without relying on manual sampling. This supports more consistent coaching and reduces operational risk.
• Deeper interaction insights: With real-time and predictive insights, AI call centers help teams detect emerging issues, shifts in customer sentiment, potential churn, and changes in demand earlier. This makes it easier to forecast workforce needs, plan ahead, and take proactive action before problems escalate.

Together, these benefits give CX teams the efficiency, visibility, and consistency they need to deliver better customer experiences at scale.

AI call center vs traditional call center

Best practices for implementing AI in call centers

Whether you use an AI as a service (AIaaS) company (a third-party vendor that handles the AI technology for a subscription price) or AI-powered call center software, we recommend following these key best practices for implementing AI in call centers.

Define your goals and objectives

Set SMART customer service goals that are specific, measurable, achievable, relevant, and time-bound, and define clear customer service objectives that align with your business’s needs. Ensure your AI initiatives serve specific purposes, such as improving customer satisfaction, reducing costs, or enhancing agent productivity.

Provide agent training and change management strategies

Offer call center training so agents can learn how to use newly implemented AI tools effectively and ethically, and don’t shy away from change management models to support their progress.

Use individual and organizational models to emphasize their roles in your company to eliminate fear of replacement and foster acceptance of AI. Plus, use this time to improve old and new call center skills like technical proficiency and communication, improving all aspects of your service.

Ensure data privacy and security

Adhere to customer data privacy regulations and implement robust security measures to safeguard customer data handled by AI systems. Also, prioritize AI transparency so your customers know what data you’re collecting (and why), how you’re using it, how long you’ll store it, and more.

Evaluate and monitor AI performance

Regularly monitor AI performance and gather feedback for ongoing improvements. This allows you to refine AI models, workflows, and processes based on feedback and changing business needs for continuous enhancement.

Focus on personalization

Keep personalization top of mind so you can tailor your interactions to customer expectations and preferences. Allow your AI tools to access historical data and past interactions housed in your unified workspaces to guide conversations and responses. Agents can also use AI to personalize responses in call center scripts based on sentiment, needs, and more.

Integrate with your existing service solution

Make sure you choose AI tools that fit your current business needs, CX strategies, and service software. Test each tool’s features and capabilities before settling on a choice to optimize performance and simplify integration.

Real world examples of AI in a contact center

From healthcare to finance and every industry in between, here are a few real-life examples of AI in a contact center.

CNH Care

CNH Care is an end-to-end healthcare solution provider using call center AI to ensure no calls fall through the cracks while maintaining a 96 percent CSAT score. This company uses AI-powered tools to automate ticket routing, optimize proactive support, decrease call waiting times, monitor dropped calls, and facilitate near-immediate agent callbacks.

Netwealth

As one of Australia’s leading financial companies, Netwealth uses call center AI to handle increased query volumes and customer expectations. With an AI call center, Netwealth customers reach an agent within 60 seconds, and agents resolve nearly 99 percent of call tickets with a single touch. Plus, the company fields more than 20,000 inquiries a month while automating call recordings, storage, and access.

Wyze Labs

Smart home technology innovator Wyze Labs uses a unified, omnichannel contact center to optimize scaling and reduce overhead costs. Since adopting Zendesk, Wyze Labs now reports a 78 percent increase in call acceptance and a drastic decrease in first contact resolution time from one week to one hour.

Frequently asked questions

Make the right call with AI

By combining AI agents, real-time agent assistance, and automated quality insights, organizations can handle higher call volumes and reduce operational costs. At the same time, AI empowers CX teams to deliver more personalized customer experiences as demand grows.

As voice and digital channels converge, AI call centers make it easier to support complex, high-stakes conversations efficiently. It also gives agents the context and guidance they need to show up with empathy and confidence.