Contact center automation: A guide to better service

Contact centers are under constant pressure to resolve issues faster. As support requests grow, human agents suffer severe burnout, which affects support quality, interactions, and problem-solving. This affects customers who expect quick resolutions, especially those with pressing issues. 

Left unaddressed, demoralized human agents leave their positions, resulting in costly rehiring that organizations must bear. The numbers paint a stark picture. According to a 2026 study, contact center turnover reached 46%, three times the industry average. 

That’s why more organizations are turning to contact center automation to alleviate agent burnout, improve customer experience, and operate more efficiently. AI-powered contact centers use AI technologies to overcome inefficiency gaps that traditional IVR systems face. 

This guide explores AI contact centers, how they automate customer service, use cases, implementation approaches, and how to choose the right AI contact center software.  

Summary

• Contact center automation uses AI to handle customer support workflows with little or no human intervention.
• An AI-powered contact center system responds to customers, understands intent, retrieves information, and provides a concise response. 
• Compared to traditional interactive voice response systems, AI contact center software is more intuitive, efficient, and natural. 
• AI automation is shifting human agents' roles, allowing them to focus on complex cases. 
• When choosing AI contact center software, consider factors like CRM integration, omnichannel support, and latency. 

What is contact center automation? 

Contact center automation uses AI technologies to handle customer interactions with minimal human intervention. It integrates AI with software technologies CX teams commonly use in operations, such as voice agents, interactive voice response, and chatbots. This allows organizations to support customers across different channels, including phone calls, email, and chat, more efficiently. 

Unlike traditional contact center software, AI contact center systems use advanced artificial intelligence, such as machine learning, deep learning, voice models, and generative AI, to converse naturally with customers. These AI systems are far more advanced than traditional IVR. They can understand nuances, emotions, and context to respond more intelligently, like humans. 

It’s also important to distinguish contact center automation from call center automation. While they’re related, AI call center automation focuses on automating voice conversation. Meanwhile, contact center automation spans voice, SMS, email, and other communication channels. 

Effective contact center automation focuses beyond a single department. For example, instead of automating a front-line receptionist, you use AI to decide which department should handle the case based on real-time conversational data and supporting customer information. 

Case studies: How call center automation improves resolution and wait times

Traditionally, customers are put on hold in a queue while human agents process calls as they come. As organizations grapple with limited staff, waiting time can extend beyond reasonable limits, ultimately resulting in call drop-off. Businesses have to expand their headcount with more staff to keep up, further driving up costs.

AI-automated contact centers overcome these limitations. They extract historical data from CRM, understand customer intent, and pass context to a human agent. As a result, CX teams can resolve more requests on first contact and cut wait times.

The three ElevenAgents deployments below demonstrate just how impactful call center automation is across different sectors.

Klarna: Up to 10x faster resolution for 35 million US customers

Klarna launched a voice AI agent built with ElevenAgents as the first line of phone support for its US customers, available to all 35 million of them. A large share of Klarna's inbound calls are informational: product guidance, payment status, or next steps.

Customers whose queries the agent handles get resolutions up to 10x faster, phone queues shrink for simple requests, and human agents are freed up for complex cases. The agent engages customers through low-latency, natural dialogue and hands over to a human agent when a call needs one. Klarna selected ElevenLabs for enterprise-grade scale, security, and performance, including GDPR-ready controls and data residency options.

Rohlik: 90% of customer communications handled automatically 

Rohlik serves 3 million customers across the Czech Republic, Germany, Hungary, Romania, and Austria. Support was fully human-led, and as Rohlik scaled across five markets and six languages, peak hours meant longer waits and there was no live coverage overnight or in the early morning. Rohlik deployed its agent, Maia, on ElevenAgents across phone, web, mobile app, and WhatsApp. Maia handles 90% of hotline calls, and 90% of all customer communications are now handled automatically, with a large portion resolved without a human. 

Maia is connected to Rohlik's backend through Model Context Protocol. It performs more than 30 actions, including checking order status, modifying orders, issuing credits, and filing support requests for the customer. When it cannot resolve an issue, or a customer asks for a person, the call routes immediately with the full transcript, so the customer never has to repeat themselves. Rohlik reports over 2x faster resolution, 24/7 availability across five markets and six languages, and is on track to cut operational costs by 50% while maintaining CSAT.

Getmobil: 75% first contact resolution and 60% lower contact center costs

Getmobil is Turkey's leading marketplace for refurbished electronics, with a network of more than 32,000 merchants. Its customer service was fully manual, so every order status query, return, and product question needed a human agent, and costs grew with the marketplace. Today, ElevenAgents handles 100% of inbound interactions across voice, web chat, and WhatsApp. Nuanced queries escalate to human representatives. 

Getmobil reports that 75% of conversations are resolved end to end without escalation, contact center costs are down 60%, and response times have improved threefold. ElevenAgents supports all three channels natively, so the team did not need custom integration work for each one. The team is now extending the platform to outbound use cases such as proactive follow-up, vendor onboarding calls, and collection scheduling.

Customer results shared with permission. Individual outcomes may vary.

How AI contact centers automate customer service 

With AI-automated contact center software, customers can be tended to immediately, 24/7. Instead of navigating the menu maze, an AI voice agent automatically converses with customers, identifies key concerns, references the internal knowledge base, and selects an appropriate response. 

Let’s say a customer calls the contact center. Instead of having a human agent pick up the call, AI answers it. Highly advanced AI systems can speak naturally, using appropriate intonation, so the conversation sounds natural. Depending on the customer’s response, AI decides the next course of action. 

Below is the workflow of an AI contact center.

Responding

First, the AI responds to communications that customers initiate. Depending on the medium, the AI system may use voice or text to answer specific requests. Many AI agents also offer multilingual support, allowing them to switch between languages. 

Understanding

Next, AI agents use advanced natural language understanding technologies to identify the context, sentiment, and nuances associated with a customer’s request. This enables the AI system to craft a more coherent, precise, and relevant response compared to traditional contact center solutions. 

Knowledge base retrieval

A modern AI contact center connects directly to an organization’s internal knowledge base so AI agents can respond more accurately. Modern voice agents and chatbots are trained to converse naturally. However, they often lack the specific product, service, or organizational information needed to answer certain queries. By retrieving organization-specific data, automated AI contact agents can provide the same answers a human agent would. 

Executing actions

After consolidating the required knowledge, the AI agent takes the appropriate step to respond to customers. For common cases, the agent responds directly with the answer. For support that requires complex resolution, the agent might update a database record, retrieve more information, escalate to a human agent, or initiate other business processes. 

Analytics

Automated contact center software collects interaction data, guided by privacy policies, to help operations teams improve their support workflow. For example, it lets you visualize common patterns in customer issues to help you identify workflow bottlenecks. 

Continuous learning

Once deployed, AI-powered support agents can continuously learn from past interactions. These agents will refine their reasoning capabilities, allowing them to adapt to different situations more agilely. 

AI voice agents vs. interactive voice response systems

For years, customer support teams relied on interactive voice response (IVR) systems to direct callers to the respective human agents. Customers listen to a voice menu, select an option, and repeat the process until a human agent answers.

While IVR reduces human intervention, it isn’t efficient. Plus, customers dislike the hassle of going through IVR. In 2019, McKinsey published a report, citing how customers were trying to circumvent or avoid IVR directly. For customers, IVR can feel like an endless maze of menus they have to navigate before an agent answers. 

AI voice agents solve the challenges operations teams face with IVRs. These agents answer calls immediately, respond intelligently, and keep ticket queues moving. AI voice agents can work alongside an intelligent IVR that routes callers to ensure customers don’t have to battle through inflexible menus.

Where voice AI agents and intelligent IVR add value 

A voice AI agent is trained to understand accents, speak in natural languages, and adapt to customers’ changing intent. In a contact center, these agents help you automate common requests, such as checking account status, rescheduling appointments, and answering frequently asked questions.

Meanwhile, IVRs that were once reliant on rigid rules now use natural language understanding to route callers to the right agent. Without needing a specific keyword, an intelligent IVR can route calls by recognizing phrases like “I’d like to talk to your manager.”

Customer service automation use cases to start with 

Leading AI voice agents and chatbots are changing how contact centers operate. According to Statista, 35% of contact centers already automate their workflow with generative AI in one way or another. 

To help you get started, here are several customer support workflows that will benefit from AI automation:

• Account inquiries: AI agents can handle self-service routines, like updating passwords, tracking orders, and updating customer profiles. 
• Real-time assistance: Customers seeking support can receive immediate answers as agents search the internal knowledge base to fulfill their requests. 
• Inter-department routing: By analyzing the customer’s intent, AI voice agents can escalate the call to the right department if necessary. More importantly, AI agents pass context so customers don’t have to repeat themselves. 
• Call summarization: Instead of manual transcription, voice agents automatically transcribe and summarize the conversations. 
• Sentiment analysis: Voice agents can continuously assess the customer’s sentiment to detect if they’re satisfied or dissatisfied with the call. This allows early intervention and reduces customer churn. 
• AI-assisted replies: Even after a human agent takes over, AI can still help draft courteous, compliant, and professional replies. 

Beyond routine customer service tasks, AI agents can also help with complex conversations. For example, in heated disputes, AI agents can simulate different scenarios in real time and offer human agents a script to de-escalate the situation. 

Choosing contact center software for AI automation 

When choosing AI-powered contact center software to automate your customer service workflow, consider these aspects. 

• CRM integration: Look for AI contact center solutions that integrate seamlessly with your CRM. This lets AI pull customer data directly via API calls to support conversations. 
• Omnichannel support. Customers expect timely responses across websites, social media, phone, and other channels. 
• Unified knowledge base. Consider contact center software that consolidates your organization’s knowledge into a single source to prevent AI context switching. 
• Seamless AI intervention. In some cases, teams could benefit from AI agents when tending to complex cases. Choose an AI agent that intervenes without being intrusive. 
• Escalation to human agents. Customers may want to speak to a human if they feel it's necessary. Look for agents that pick up nuances and can direct communications promptly. 

ElevenAgents is a single platform that lets enterprises build conversational AI agents to support their customer service workflow. It lets you configure agents based on your brand guidelines, compliance requirements, and procedures. Then, you deploy and scale the support agents across voice, chat, and email. 

For small and medium businesses that need a simpler agent workflow, choose Reception by ElevenAgents. It’s an AI receptionist that picks up calls, answers inquiries, schedules appointments, and more, with a natural-sounding voice.

How to implement contact center automation responsibly 

With increased adoption of contact center automation, organizations should be mindful of best practices, including governance, privacy, and operational factors, to ensure smooth implementation. 

1) Start with a narrow use case

Don’t integrate AI tools with the entire support workflow from day one. Doing so adds multiple interaction points that require equal attention, which can spread your resources too thin. Instead, start with narrow, high-volume requests such as informational calls or order status, then expand. This lets your developers remediate issues, verify agent performance, and ensure policy adherence. 

For example, Insurely, an open finance provider, keeps an AI agent’s role narrow to improve its performance. It also connects the agents with an updated knowledge base. Explore other best practices that Insurely uses when integrating AI agents into their customer service systems. 

2) Provide a human-escalation path 

Even with state-of-the-art AI agents, human experience is still important, especially when managing disputes or handling complex cases. Therefore, offer options for customers to connect with a human agent. Make the option clear before the conversation begins, and train the AI agent to identify signs of distress so it can enable early human intervention. 

3) Disclose AI agent usage

Customers should know they are speaking or texting with an AI agent. Laws like the EU AI Act require organizations to disclose AI usage upfront. A simple, non-ambiguous opening statement like “Hi, I’m an AI agent” helps you remain compliant and build trust with customers. 

4) Publish data privacy policies

Voice agents and chatbots might use conversational data for training. Customers have the right to know if what they share will become part of the training datasets. Therefore, when communicating, organizations should provide a disclaimer about how the AI agent collects, stores, and uses customer data. 

What to look for in AI tools for call centers 

An AI tool should let call centers sound human, respond promptly, and escalate to a human when needed. These characteristics depend directly on the underlying voice agents or chatbots. Focus on these areas when choosing a call center AI tool. 

Get started with ElevenAgents for customer service 

ElevenAgents simplifies contact center automation with purpose-built, human-sounding agents that support conversations in 90+ languages. Instead of replacing your existing customer service platform, you can build and deploy conversational AI agents that work cohesively with it. It also lets you apply brand guidelines, security guardrails, and track performance across interactions.

Create an AI agent today and explore how other brands use ElevenAgents to automate their CX workflow. 

Frequently asked questions about contact center automation