AI call center: Benefits, use cases, software, and best practices

Voice remains a critical customer service channel, especially for complex, urgent, or emotionally charged issues. But it’s also one of the hardest and most expensive channels to scale.

AI in customer service is changing that. AI can reduce manual work, improve routing, support faster resolutions, and give agents more context during customer conversations. It also creates more consistent service across voice and digital channels, while keeping human agents involved where they add the most value.

This guide covers the key benefits, use cases, implementation tips, and real-world examples of AI call centers, including how they fit into a broader AI-driven customer experience.

More in this guide:

• How does an AI call center work?
• AI call center software features
• The evolution of AI in call centers
• AI voice agents in modern call centers
• 10 ways to use AI in call centers
• Benefits of using AI in call centers
• AI call center vs traditional call center
• How to measure AI call center success
• How to implement an AI call center
• Real world examples of AI in a contact center
• Frequently asked questions
• Make the right call with AI

How does an AI call center work?

An AI call center uses artificial intelligence to understand customer calls, retrieve context, take action, and resolve or escalate requests. The process combines speech recognition, connected knowledge, reasoning, automation, and human support.

A typical AI call center workflow looks like this:

1. Understanding: First, the AI understands the request. Speech recognition converts the caller’s words into text. AI then identifies intent, language, sentiment, and other signals that shape the next step.
2. Retrieving: Then, the AI retrieves relevant context. The system pulls information from knowledge bases, customer records, previous interactions, and connected business systems. This gives the AI or human agent the context needed to respond accurately.
3. Determining: The next step is to determine what to do next. Advanced AI can reason through a request, identify missing information, ask follow-up questions, and apply business procedures. This enables AI to handle more than simple, scripted interactions.
4. Acting: Then, AI takes action across systems. Workflow automation and integrations let AI complete tasks instead of only providing information. For example, an AI agent might check an order, update an account, or trigger another business process.
5. Resolving or escalating: The final step is to either resolve or escalate the issue. If AI can complete the request, it delivers the resolution directly. If human expertise is needed, AI transfers the call with the conversation history and context intact.
6. Improving: After the interaction is complete, it serves as an example for future improvements. Analytics and quality assurance evaluate both AI and human interactions. Teams can use these insights to identify gaps, improve workflows, and maintain service quality.

AI call center software features

The best AI call center software combines automation, agent support, analytics, and governance in one connected service environment. When evaluating platforms, look for features that improve both customer resolutions and operational performance.

AI voice agents

AI voice agents are able to understand spoken requests, reason through problems, and take action without relying on rigid scripts. Advanced systems handle multi-step issues and transfer conversations when human expertise is needed. AI voice agents capabilities allow businesses to automate more voice interactions while keeping human agents available for complex or sensitive conversations.

Intelligent routing

Intelligent routing analyzes customer intent and other signals to determine the best path for each request. It directs calls to the right agent, team, or automated workflow. This feature matters because better routing reduces unnecessary transfers and gets customers to the right resource faster.

Call transcription and summaries

AI transcribes conversations and generates concise summaries after calls. These records give agents and managers a searchable view of what happened. This automated documentation reduces after-call work and preserves context for future interactions.

Agent assist

Agent assist powered by AI, such as Copilot, surfaces relevant knowledge, suggested responses, and next steps during customer conversations. It gives agents additional context without forcing them to search across disconnected systems. Having real-time guidance reduces manual work and supports faster, more consistent resolutions.

Analytics and reporting

AI call center analytics track performance across customer interactions, agents, and automated systems. Advanced tools can surface trends, anomalies, resolution outcomes, and operational bottlenecks. As a result, teams gain clearer visibility into what drives performance and where service needs improvement.

Quality assurance

AI-powered quality assurance can automatically evaluate interactions against defined service standards. Zendesk QA, for example, can score interactions across human and AI agents, including voice channels. Such broader quality coverage makes it easier to identify risks, coaching opportunities, and inconsistent service.

CRM and business system integrations

AI call center software should connect with CRM platforms, knowledge sources, ticketing systems, and other operational tools. These integrations give AI access to the data and workflows required to complete requests. Connected systems enable end-to-end resolutions instead of isolated responses or unnecessary handoffs.

Security and AI governance

Security features should include access controls, data protection, auditability, and visibility into AI activity. Organizations also need controls for monitoring and refining automated decisions. Strong governance gives teams greater control as they expand AI across more customer interactions.

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.

AI voice agents in modern call centers

AI voice agents are software systems that understand spoken requests, respond conversationally, reason through multi-step issues, and take action across connected systems. Unlike traditional interactive voice response (IVR) systems, they don’t rely only on fixed menus or scripted decision trees.

With traditional IVR, callers have to choose from preset options, such as “Press 1 for billing.” But with AI voice agents, customers can simply speak naturally and the agents are able to interpret their request. They can identify intent, ask follow-up questions, apply business rules, and adapt as the conversation changes.

Going the extra mile, backend integrations extend these capabilities beyond conversation. AI voice agents connect with customer records, knowledge sources, and business systems to complete tasks such as checking order status, updating account information, or processing service requests.

Common AI voice agent use cases include:

• Answering account, order, or policy questions
• Handling routine service requests
• Collecting and validating customer information
• Updating records across connected systems
• Routing complex or sensitive issues to the right human agent

When human support is needed, the transition preserves conversation history and customer context. This reduces repetition and gives agents the information they need to continue the interaction without restarting the process.

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 can improve service quality, agent productivity, and operational efficiency. Key benefits include:

• 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. According to the Zendesk CX Trends Report, 87 percent of CX leaders say AI materially accelerates first-reply and full-resolution speed. As a result, teams often see more consistent CSAT scores across channels and touchpoints.
• Boosted agent productivity: By automating routine tasks and providing real-time guidance, AI can increase the number of issues agents resolve. A 2023 National Bureau of Economic Research study of 5,179 customer support agents found that access to a generative AI assistant increased productivity by 14 percent on average, with larger gains among less-experienced agents.
• Scalable operations with faster time-to-value: AI allows call centers to scale support operations without adding complexity. In a 2025 Forrester Consulting study commissioned by Zendesk, the modeled composite organization automated 30 percent of inquiries by year three and reduced average handle time by three minutes.

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 first contact resolution, and minimizing repeat contacts. McKinsey estimated in 2023 that applying generative AI to customer care could create productivity value equivalent to 30 to 45 percent of current function costs.
• Automated quality monitoring at scale: AI enables continuous call monitoring, improving service quality without relying on manual sampling. Zendesk QA, for example, can score 100 percent of human and AI interactions across channels, giving teams broader visibility into service quality, risks, and coaching opportunities. 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

How to measure AI call center success

AI call center success goes beyond automation volume. The strongest measurement strategy looks at whether AI resolves issues accurately, improves customer satisfaction, reduces costs, and makes agents more productive.

To accurately measure AI call center success, track these key performance indicators (KPIs):

• First contact resolution (FCR)
• Average handle time (AHT)
• Customer satisfaction (CSAT)
• Automated resolution rate
• Transfer rate
• Cost per resolution
• Agent productivity

But be mindful to not evaluate these metrics in isolation. Establish a baseline before expanding AI, then compare performance by channel, issue type, and automation level. Review low-performing interactions to identify gaps in knowledge, workflows, routing, or AI behavior.

Continuous measurement creates a feedback loop. Use quality assurance, analytics, and customer feedback to refine AI performance while monitoring resolution rates, satisfaction, costs, and agent outcomes over time.

How to implement an AI call center

Implementing an AI call center requires more than adding AI to existing workflows. A strong rollout connects clear goals, trusted knowledge, business systems, human oversight, and continuous performance monitoring.

Follow these steps to implement AI in your call center.

Step 1: Define your goals and success metrics

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.

Step 2: Assess your current call center workflows

Map how calls move from intake to resolution. Identify high-volume requests, repetitive tasks, common escalation points, knowledge gaps, and manual processes. This assessment can reveal where AI is most likely to reduce effort or improve resolution speed. It also shows which workflows still need human judgment.

Step 3: Choose the right AI call center software

Evaluate call center solutions based on the capabilities your team needs now and as call volumes grow. Look for AI voice agents, intelligent routing, transcription, agent copilot, analytics, quality assurance, integrations, and security controls. Prioritize software that works with your existing service environment instead of creating another disconnected system.

Step 4: Connect knowledge, customer data, and business systems

AI needs accurate context to deliver useful resolutions. Connect it to trusted knowledge sources, customer records, interaction history, and the systems required to complete service tasks. Connected knowledge improves answer accuracy, while backend integrations allow AI to take actions rather than only provide information.

Step 5: Design escalation paths and human handoffs

Define when AI should resolve an issue, when it should escalate, and when human approval is required. Make sure transferred calls include the conversation history, customer context, and actions already taken. Seamless handoffs reduce repetition and allow agents to continue from where the AI stopped.

Step 7: Train agents and prepare teams for change

Provide call center training so agents understand how AI fits into their daily workflows. Use change management models to clarify roles, introduce new processes, and build confidence in human-AI collaboration. Training should also strengthen relevant call center skills, including technical proficiency, communication, and judgment.

Step 8: Test security, privacy, and service quality

Before expanding AI across more interactions, test how it handles sensitive data, business rules, edge cases, and escalations.

Follow applicable customer data privacy requirements and maintain clear controls around access, storage, and AI activity. AI transparency also matters: customers and teams should understand when AI is involved and how automated decisions are governed.

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.

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

AI call centers make it easy for businesses to handle more interactions and provide exceptional CX without breaking the bank. With generative AI and AI-powered tools like Zendesk AI agents, call summaries and transcriptions, and data-driven insights, you can take your service and experiences to a whole new level.

Zendesk boasts all these features and more. We specialize in call center software with AI at our core. By partnering with us, you have access to AI-powered knowledge management, intelligent call routing, and sentiment analysis—everything you need to level up your call center.

Dial up your services and invest in voice support for the AI era with Zendesk voice software today.