AI Based Call Center: Advantages, AI Features & Providers

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In recent years, call center software has heavily integrated artificial intelligence (AI) to automate time-consuming business processes, improve customer experience, and streamline communication across all channels and touchpoints.

AI-based call centers use Natural Language Understanding (NLP) and machine learning to enable 24/7 customer self-service, improve the sales process, and decrease average handle time (AHT.) AI forecasting optimizes the agent scheduling process, while speech and text analytics provide insight into employee performance and customer sentiment.

What other benefits does an AI call center offer, and which features should you look for? What are the top call center platforms powered by AI technology? Keep reading to find out.

Impact of AI
How AI is Used
Advantages
Key Features
Best Practices
Providers
Future of AI Call Centers

The Impact of AI in Call Centers

The surge of AI, machine learning, and conversational intelligence has radically transformed contact centers. Today’s contact center software solutions utilize AI in multiple forms: predictive omnichannel routing, in-call agent assist, outbound dialers, forecasting, and interaction analytics.

Modern chatbots, virtual agents, and voice bots use conversational AI to mimic realistic interactions across voice and digital channels (VoIP calling, web chat, SMS, email, social media messaging, etc.) These conversational technologies understand many customer inputs and respond naturally, guiding callers through processes like appointment scheduling, bill payment, routing, and basic customer support.

AI generates real-time transcripts for call center interactions. These transcripts power live agent feedback and interaction analytics including customer sentiment analysis, CSAT scores, and utterance monitoring. AI-based call centers lead to a major productivity boost across touchpoints for both agents and supervisors.

How is AI Used in Call Centers?

AI-based call centers integrate machine learning across multiple touchpoints and services. Here are the main ways that today’s call centers use AI:

Routing
Customer support
Sales support
Supervisor support
Live-agent feedback
Coaching and evaluations
Workforce management

Routing

Call center software uses machine learning to identify the caller’s needs and route them to the best available agent. AI-based call center software uses CRM-extracted information, like the customer’s recent purchases and interaction history, to determine which agent has the training to assist them.

Predictive routing with machine learning ranks agents by customizable KPIs like average handle time, performance score, or task resolution rate. ACD then routes calls to the highest-ranking agent.

Customer Support and Self-Service

Contact centers use AI to power customer self-service through multiple mediums and touchpoints: chatbots, intelligent virtual agents (IVAs), and voicebots, and knowledge bases. AI enables these technologies to automate conversational interactions, determine customer intent and sentiment, answer questions, and provide rich services.

Chatbots and IVA systems use AI-based natural language understanding (NLU) to determine customer intent. Over time, chatbots can change their messaging patterns based on how customers speak, using generative AI to develop a more conversational style. AI bots can answer follow-up questions, link knowledge-base articles, and schedule sales or service appointments.

Sales Support and Lead Qualification

Contact center platforms use AI for multiple sales purposes: determining a potential customer’s likelihood of making a purchase, qualifying leads and updating lead status, analyzing conversation transcripts to determine customer sentiment, and more. These machines detect behaviors that indicate positive or negative lead status, providing valuable insights for sales teams.

Outbound dialers, like predictive dialers, determine when to place the next call by balancing each agent’s call duration with the likelihood of a call recipient answering the phone.

Supervisor Support

AI assists supervisors with call monitoring and call center staffing. Contact centers transcribe and analyze customer interactions in real time, identifying keywords, conversation topics, and customer sentiment scores. If a call triggers a concerning sentiment level or topic, the software notifies supervisors, who can join the interaction to support the agent.

Live-Agent Feedback

CCaaS platforms give agents automated feedback and guidance during live interactions with customers. This technology provides a live transcript, language translation, customer-sentiment monitoring, and suggested agent responses.

AI can populate the agent’s workspace with relevant customer information like recent orders or relevant knowledge base articles triggered by keywords from the interaction. Canned response suggestions can help agents make sales, answer questions, and de-escalate negative customer sentiment.

five9 ai agent assist

Coaching and Evaluations

Machine-learning algorithms analyze agent performance, identify behavior patterns, and pinpoint specific areas for improvement. AI evaluates agents based on KPIs like sentiment scores, handle time, task resolution rate, and more. The software uses this information to complete agent scorecards and feedback forms, automating the performance review process and automatically assigning relevant training modules to agents.

Workforce Management

AI helps contact center managers with agent scheduling, employee engagement, quality management, and other workforce optimization functionalities. AI combines historical trend analysis with predictive analytics to improve long-term workforce planning and short-term agent scheduling. Automated shift bidding/swapping, intraday management, forecasting, and adherence monitoring ensure SLA compliance and give agents control over their schedules.

What Are the Advantages of an AI-Based Call Center?

As companies compete to provide optimal customer service, AI can provide your call center with several advantages:

Improved customer service
Efficient staff
Better agent training
Shorter wait times
Lower costs
Better understanding of your customers
More effective sales efforts

Improved Customer Service

AI processes boost customer convenience through 24/7 self-service. Customers can contact support teams on their preferred channel at any time. AI chatbots provide interactive responses, rich services, and accurate routing–all based on the customer’s background.

Live agents have a better understanding of customer sentiment and support needs thanks to AI analysis, and AI-based coaching helps human agents develop better customer support practices.

Efficient Staff

Artificial intelligence improves contact center agent and supervisor efficiency via automations and real-time support. AI forecasting and scheduling help managers structure shifts for staff, and automated evaluation forms help supervisors give quick feedback to more agents.

Automated call summaries and action items expedite agent post-call work. AI-powered call routing tools like predictive routing and IVA systems navigate calls to the right agent on the first attempt, leading to quicker query resolution.

Better Agent Training

Multiple AI tools work together to help agents learn quickly. Speech coaching, AI-generated checklists, and response suggestions coach agents during live interactions. Automated CSAT and customer experience scores help agents learn how their choices impact customers. AI-generated transcriptions and feedback forms offer training.

These tools benefit scaling teams that frequently add new staff or teams with a high turnover rate.

Shorter Wait Times

Efficient self-service capabilities handle customer needs without overburdening agents. Customers can find answers to questions, receive recommended knowledge base articles, handle billing issues, and more. Predictive routing ensures that customers reach agents best suited to help, considering availability and KPIs like average handle time. Together, these tools reduce queue wait times and staffing needs.

Lower Costs

AI provides customer self-service, and machine learning tools make agents and supervisors more efficient. These capabilities dramatically reduce call center staffing needs, lowering costs without sacrificing service.

Better Understanding of Your Customers

AI-based tools like interaction analytics, customer satisfaction, sentiment scores, and keyword detection strengthen your understanding of the customer experience.

AI-based analytics, like data about your customers’ frequent interactive voice response (IVR) choices and reasons for calling, help you understand what your customers want and how to serve them better. These tools inform smarter business decisions about your products, queues, and customer support.

More Effective Sales Efforts

AI-based sales tools like predictive auto dialers and lead qualification support stronger and more efficient sales campaigns. Predictive dialers automatically dial calls while agents complete their current ones, optimizing a sales agent’s on-call time.

AI lead qualification software analyzes transcripts and customer behavior patterns to segment leads, ensuring that sales teams pursue the optimal candidates.

AI Call Center Key Features

An AI call center typically combines several automated features, across channels. Here are the key features to look for in an AI call center:

Live agent assistance
AI call summaries
Customer sentiment detection
Real-time call transcription
Chatbots, voicebots, and virtual agents
Schedule forecasting
Agent coaching
Predictive routing
Interaction analytics

Live Agent Assistance

AI provides agents real-time guidance during interactions across all channels, helping call center reps provide better support. Machine learning determines the call’s purpose and monitors the transcript to create checklists, displaying them on the agent’s dashboard to ensure that reps remember everything they need to cover.

Conversational AI technologies listen to calls, analyzing customer intent and tracking keywords. This triggers pop-ups and agent reminders, including relevant promotions, HIPAA-compliance checklists, and suggested next steps. AI displays relevant customer data like recent interactions, customer location, survey responses–even the weather in the caller’s location. Some software offers canned responses that directly coach agents through interactions without involving a live supervisor.

AI Call Summaries

Artificial intelligence uses call transcripts and large language models (LLMs) to generate a summary after each call. These summaries include post-call insights like key action items and discussion highlights, reducing agents’ post-call work time.

Administrators can review these summaries quickly, saving time that they’d otherwise spend examining each day’s transcripts and call recordings.

Customer Sentiment Detection

Call center software utilizes AI and natural language processing (NLP) to determine customer emotions, satisfaction levels, and overall experience. NLP algorithms are generally trained on large models of sample conversations and continuously learn from each customer interaction, refining the ability to determine how the customer feels. Sentiment detection works for all CCaaS channels–voice, web chat, SMS, and social media.

These sentiment scores can help companies understand how agents perform, how customers feel about particular products or issues, and how customers generally feel when interacting with the company.

Real-Time Call Transcription

Machine learning algorithms and language detection software power live call and video transcriptions. Call center software can translate transcriptions into different languages, helping agents converse across language barriers. Transcriptions support customer sentiment scores, give summaries and real-time insights for agents, and enable supervisors to monitor all active conversations.

Chatbots, Voicebots, and Virtual Assistants

While traditional chatbots have been around for a while, AI-based chatbots provide more realistic, dynamic interactions that respond to more customer inputs. Intelligent virtual agents (IVAs) and voicebots are similar to chatbots–automated technologies that engage with customers to provide self-service features like routing, question response, bill pay, and appointment booking.

AI chatbots and voicebots incorporate machine learning and language learning models to understand a wide variety of inputs and respond with more human-like text or voice. This intelligence enables AI bots to switch conversations instantly. These tools can operate across all channels and pull information from a variety of sources: CRM systems, databases, and customer journey histories.

Schedule Forecasting

AI identifies call center activity trends to predict staffing needs by season, month, and day of the week. Based on these predictions, AI-based contact center software generates suggested staff work schedules. Agents can submit their shift preferences, FTE, and availability–the software algorithm factors this in when determining schedules.

AI-based scheduling saves effort for managers and gives agents more control over their work hours.

wfm forecasting genesys

Agent Coaching and Performance Evaluation

Contact centers feature several AI-powered tools that enhance a supervisor’s ability to coach, offer feedback, and evaluate agent performance. Create custom AI scorecards for each interaction, combining metrics like response scores, keyword usage, an agent’s greeting style and tone, and more. AI tracks these metrics alongside the call summary to automatically score agents during an interaction, providing automated feedback afterward.

The same principles apply to performance evaluations. Create evaluation forms that combine metrics and KPIs to evaluate agents. AI will comb interactions, transcripts, sentiment scores, and agent behaviors to complete the evaluation form and offer feedback to agents. These AI tools help agents improve and save time for managers.

Predictive Routing

Predictive routing scores each agent in a queue by a custom KPI, selecting the best-suited agent for each inbound caller that reaches the queue. Administrators select a KPI that they want to optimize within a queue–such as sentiment score, resolution rate, or resolution speed. AI then evaluates agents according to this KPI. When a customer reaches that particular queue, predictive routing sends the customer to the highest-scoring available agent.

Predictive routing helps call centers to match inbound callers with the agent best suited to help.

Interaction Analytics

Contact center AI analyzes all customer-agent interactions across channels, tracking interaction trends and metrics. Supervisors can access these interaction analytics in out-of-the-box dashboards, customizing them by agent, department, or viewing metrics for the whole contact center.

Contact analysis AI tracks keywords, IVR selection, and chatbot conversations to determine the most frequent reasons for customer contact. Conversation insights track metrics like customer experience score, customer sentiment, and overall satisfaction. AI also scours IVR and routing systems to find opportunities for automation.

Best Practices for Implementing AI in a Call Center

When setting up an AI-based call center for your company, note the following guidelines:

Define goals
Dont take on too much at once
Utilize customer feedback
Monitor effectiveness with analytics
Train agents
Utilize free trials

Define Goals

Before selecting a call center plan with AI features, define your business goals for AI. Which metrics and KPIs do you want to improve? Selecting measurable, concrete KPIs makes it easier to track your AI strategies' effectiveness.

Don’t Take on Too Much at Once

Most higher-tier CCaaS plans bundle several AI features across domains: scheduling, agent assistance, supervisor support, analytics, chatbots and IVA, and more. While these features are designed to work together, it’s generally better to implement just a few at a time. Adopting too many new technologies simultaneously can overwhelm users and skew the results of your metrics and goals.

Focusing on a few AI capabilities at a time gives a better idea of each technology's impact. When users feel comfortable with one, branch out and add in more.

Utilize Customer Feedback

AI technologies provide dozens of ways to elicit customer feedback and evaluate current call center flows:

Monitor the sentiment scores across your channels, including chatbot interactions
Examine the most common keywords, IVR choices, and queues that customers visit
Analyze automated post-call surveys.

Together, these data points provide a solid picture of how well your AI-informed support and service strategies are serving customers.

Monitor Effectiveness by Tracking Metrics

Continually track carefully selected metrics to monitor the effectiveness of your AI strategies. For example, you may aim to improve by a certain percentage in one of the following KPIs: customer satisfaction score, resolution rate, queue wait time, call volume, lead conversion, and IVR containment rate.

Train Agents to Use the AI Technologies

The learning curve with new AI tools can be steep–especially those that require live use, such as agent assistance and predictive dialers. Most call center software platforms include training modules and demos that help staff learn to use features, like AI tools. Take advantage of these tutorials, or have one tech-savvy staff member teach other agents how to use them.

Take Advantage of Free Trials

Most CCaaS providers offer free trial periods, which last anywhere from 7 to 30 days. When comparing providers, test out several options. Get a feel for the interface, dashboard, and AI tools. Consult with your team about their experience. This helps you make a more informed decision when you finally subscribe to a contact center software.

The Future of AI Call Centers

Moving forward, AI will continue to play a larger role in call center operations. Here’s what to expect in the future of AI call centers:

Continued omnichannel adoption: Convenience is a top priority for customers, especially the younger generation. Omnichannel service meets this need by providing multiple touchpoints for your customers to reach you. Omnichannel software is expected to grow with a 13.6% annual growth rate through 2030.
Increased adoption of conversational AI: With the advent and intelligence of ChatGPT, companies have become increasingly interested in using generative AI for self-service. 68% of executives believe that the benefits of generative AI outweigh the risks, and we expect this number to increase over time.
Reduced labor costs for businesses: As companies integrate machine learning into their contact center operations, reduced staffing needs and increased efficiency will reduce labor costs. Gartner predicts AI will reduce labor costs by $80 billion in 2026.
Less demand for customer-support staff: Conversational AI is expected to automate many of the customer-service interactions that used to be handled by live staff. Gartner expects AI to lead to a 20-30% reduction in customer support agents by 2026.
Phone will remain the centerpiece: While omnichannel and multichannel service will play increasingly large roles in contact center software, calling is expected to remain the primary that customers use for self-service. 86% of customers still prefer using telephony to contact customer support.

AI-based call center software makes it easier for your staff to provide excellent customer support. Omnichannel software with machine learning will support call center agents and supervisors while providing you with the metrics and KPIs to track progress toward business goals.

AI Based Call Center: Advantages, AI Features & Providers

The Impact of AI in Call Centers

How is AI Used in Call Centers?

Routing

Customer Support and Self-Service

Sales Support and Lead Qualification

Supervisor Support

Live-Agent Feedback

Coaching and Evaluations

Workforce Management

What Are the Advantages of an AI-Based Call Center?

Improved Customer Service

Efficient Staff

Better Agent Training

Shorter Wait Times

Lower Costs

Better Understanding of Your Customers

More Effective Sales Efforts

AI Call Center Key Features

Live Agent Assistance

AI Call Summaries

Customer Sentiment Detection

Real-Time Call Transcription

Chatbots, Voicebots, and Virtual Assistants

Schedule Forecasting

Agent Coaching and Performance Evaluation

Predictive Routing

Interaction Analytics

Best Practices for Implementing AI in a Call Center

Define Goals

Don’t Take on Too Much at Once

Utilize Customer Feedback

Monitor Effectiveness by Tracking Metrics

Train Agents to Use the AI Technologies

Take Advantage of Free Trials

Top Contact Center Providers with AI Solutions

Dialpad

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Five9

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Talkdesk

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Genesys Cloud CX

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NICE CXone

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The Future of AI Call Centers