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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.

 

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.

 

Core AI Technologies in Call Centers

Recent advancements have given call centers the opportunity to leverage a wide array of AI-driven technologies to enhance efficiency, optimize customer service, and streamline operational workflows. Below are some of the key technologies in the call center space:

 

Natural Language Processing (NLP) and Natural Language Understanding (NLU)

NLP and NLU empower call centers to understand and interpret customer inquiries in a natural language that imitates the way humans actually talk. These technologies work in tandem to analyze both the meaning of spoken and written words, as well as the intent behind them.Customers are then given meaningful and actionable automated responses. NLP is what analyzes language structures, while NLU is the engine of comprehension that considers context and semantics. Together, NLP and NLU make chatbots and virtual assistants smarter through sentiment analysis.

 

Machine Learning (ML) Algorithms

Machine learning algorithms allow AI-powered systems to continuously learn from data to refine and improve autonomous responses and decision-making over time. While NLU/NLP helps AI understand how humans talk, machine learning helps AI understand how humans think. ML studies past consumer responses and behaviors to improve predictive analytics and to provide more personalized responses.  In call centers, machine learning algorithms identify customer behavior trends, improve self-service features like IVR,and route customers to the right agents, reducing frustration and wait times. ML can forecast future call volumes and peak call times for better agent scheduling and resource management, saving your business money and time.

 

Speech Recognition and Synthesis

Speech recognition technology transcribes spoken words into written text, allowing AI to interpret e voice-based directives and interactions in real time. Speech recognition lets customers  speak naturally and access resources without any human intervention.  Speech synthesis, or text-to-speech (TTS), generates verbal responses that feel conversational and “natural.” Customers can receive assistance immediately through smart responses. Speech synthesis is ideal for more common troubleshooting issues that don’t need a live agent.

 

Large Language Models (LLMs)

Large Language Models, like OpenAI’s GPT series, assist call centers by generating human-like text to assist with more complex customer service needs. LLMs are trained on vast amounts of data, can both understand and respond to input, and can understand and "communicate" via multiple communication channels, even if text is the most common. LLMs can take on more complex inquiries, generating detailed responses. Using existing customer information, it can also provide customized recommendations on demand with little to no training. LLMs are versatile and dynamic, providing rich customer interactions and advanced manager-facing insights, providing product/service recommendations, creating and collecting customer surveys, optimizing the customer journey, and offering insight into agent performance. LLMs are an essential crux to any organization seeking to bolster their AI-driven customer service.

 

Examples of 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
  • Chatbots and Virtual Agents
  • Interactive Voice Response (IVR) Systems
  • Predictive Analytics
  • Sentiment Analysis

 

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

 

Chatbots and Virtual Agents

AI-enabled chatbots and virtual agents enable call centers to provide 24/7 support, taking routine questions and routing customers to relevant resources. These tools leverage NLP to understand and respond to common customer issues like order status and account balance requests, appointment scheduling, or technical support.. Virtual agents can tackle complex interactions, collect contextual information, and escalate situations to human agents when needed. Virtual agents cut wait times, improve the overall customer journey, and provide a personalized experience.

 

Interactive Voice Response (IVR) Systems

AI-enabled IVR systems go one step further from traditional menu-based systems by using advanced speech recognition to comprehend open-ended responses from customers. Rather than follow a rigid or mechanical scripted menu, AI-powered IVR systems (sometimes called voicebots) let customers express their needs with their own words. These systems can better route or respond to calls based on customer intent, which leads to faster resolution times. Many IVR systems have built-in TTS abilities which allow for natural and tailored voice responses giving interactions a more engaging “human” and personalized feel.

 

Predictive Analytics

Predictive analytics uses AI to anticipate call center customer demands and optimize available agents. By looking at historical data, interaction histories, and customer behavior trends, machine learning models predict potential churn and peak times. Most importantly, predictive analytics powers predictive engagement: meaning an automated or live agent reaches out to meet a customer need before the customer asks for it themselves.. Predictive analytics bolsters workforce management by forecasting demand, enabling call centers to staff accordingly and match predicted call volumes, saving operations money and improving customer satisfaction.

 

Sentiment Analysis

Sentiment analysis gauges customers’ emotions during interactions, a powerful tool that accounts for language, tone, and even pauses during speech. Sentiment analysis can figure out if a customer is frustrated, happy, or neutral. Real time insights can help agents adjust their responses to be empathetic and even de-escalate situations when possible. Large volumes of customer data can be parsed to provide management with insights into the overall customer journey. Call centers are better able to strategize and train agents effectively while finding potential and present issues affecting customer satisfaction.

 

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

Incorporating AI in your call center leads to a more efficient and smooth workplace, stronger overall customer satisfaction, and significant cost savings. To best maximize the potential of AI, call centers should consider the following best practices:

 

Ensure Data Quality and Accuracy

Accurate and rich data is the lifeblood to a robust AI-powered system. Call centers must take steps to ensure customer data, interaction histories, and feedback remain accurate, up-to-date, and relevant. Any inaccuracies should signal maintenance and active interventions. Data cleaning and validation mechanisms can help cut down on errors and improve AI performance. Your system is only as good as its data, maintain it well to ensure that you receive accurate insights, recommendations, and responses.

 

Continuously Monitor and Evaluate

AI systems in call centers need ongoing monitoring and evaluation to keep them running smoothly and accurately. Regularly review your AI’s performance to ensure accuracy and reliability while identifying areas of improvement. Implementing feedback loops and setting key performance indicators (KPIs) is imperative to meeting your evolving customer and business needs.

 

Train Employees and Facilitate Adaptation

Employees and how well they understand AI tools is critical to ensuring the success of implementing it within your call center. Call centers need to provide comprehensive training to ensure staff knows how AI works, its role within their workflows, and how to use the tools correctly. Adaptation is eased through workshops, ongoing support from management and vendors, and building a culture that embraces the technology.

 

Balance Automation with Human Interaction

AI can automate tasks, but human interaction is irreplaceable–especially when it comes to delivering empathetic and exemplary customer service. Call centers need to balance automation with human touchpoints. Automation can handle routine inquiries efficiently but complex or data-sensitive issues should receive personalized human attention. Human agents cannot forget their role in the workplace, even as AI evolves and takes over more elementary concerns. Automation may be powerful but companies should always give customers the option to connect with a live agent during an automated interaction.

 

Adopt a Customer-Centric Approach

AI systems should be trained to leverage existing customer data to comprehend preferences, past behaviors, and individual needs. This approach allows for AI interactions to be personalized and tailored towards an engaging and effective customer journey. Personalization overall helps call centers build stronger long-term customer relationships and overall brand loyalty. It is important to integrate internal knowledge bases and third-party tools like CRM systems, helpdesk solutions and more to ensure accuracy and a quality experience.

 

Train AI and Update Systems

Training AI systems is a continuous process that never stops. Regularly updating and upgrading models, feeding systems new data, and retraining algorithms keeps AI systems current. Customer needs and industry trends evolve over time so updating systems to reflect new product offerings, services, or even company policies keeps your AI’s knowledge base relevant. Customers need accurate and up-to-date information, so ensure your AI is equipped to provide those.

 

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.