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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

Key Takeaways:

  • Leaders say that conversational AI will reduce contact center labor costs by $80 billion by the end of the decade, with one in ten agent interactions being automated.[*]
  • The Voice AI Agents market, on a global scale, is said to climb up to $47.5 billion by 2034, up from $2.4 billion in 2024. This is huge: a projected 34.8% compound annual growth rate that proves it's here to stay.[*]
  • 87% of U.S. consumers express frustration with traditional customer service transfers. AI agents significantly reduce pain points and improve resolution efficiency.[*]
  • The Banking, Financial Services, and Insurance (BFSI) sector leads adoption at 32.9% market share. 70% of healthcare organizations credit voice AI with improved outcomes on an operational level.[*]
  • Companies who say they adopted AI call routing report significant cuts in queue times and queue abandonment, with intelligent routing and real-time resolution raising the ceiling on CSAT scores.

 

AI-powered contact centers transplant and rebuild over outmoded manual agent workflows via intelligent systems which prove they handle routine inquiries, route complex issues deftly, and optimize operations through data-driven methodology. This takes away the outdated IVR-led frustration customers will leave you over.

Updated and smart conversational AI guides customers through natural, context-rich conversations that span through both your voice and digital channels. Real-time transcripts inform your sentiment analysis, improve your CSAT scoring, and keep performance monitoring in focus. All the while, your omnichannel routing and AI-enabled agent assist toolkits and training keep things running resource-savvy.

Organizations who work with and not against AI contact centers gain these immediate boosts: slashed operational costs, quicker resolution times, renewed customer satisfaction, and the power to scale support effortlessly as demand grows on an inevitable scale.

 

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 give the AI a chance to understand and parse together how customers truly speak. This aids it in finding out what they want. NLP will look into the structure of sentences and words people say, while NLU pieces together the meaning behind words.

Together, these technologies keep chatbots and virtual assistants cognizant about emotions using sentiment analysis. This allows the AI to respond with appropriate empathy or urgency.

Real-world example: A customer says, "my wifi connection is a total mess right now, how do I get it back up?" NLP takes in the sentence structure, while NLU spot-checks and realizes the customer is reporting a connectivity issue. It then routes them to the best available support team or troubleshooting workflow so they can get their connection back running.

 

Machine Learning (ML) Algorithms

Machine learning helps AI systems improve over time by studying past interactions and customer behavior patterns. ML is not bound to rules that feel rigid or robotic. Instead, the algorithms adapt and learn over time.

ML can even take the guesswork out of forecasting call volume patterns which lets managers schedule the right number of agents for peak times. On top of that, any information can be used to bolster self-service features like phone menu systems (IVR) based on which options customers actually use.

Real-world example: An ML system analyzes historical call data and discovers that customers with account age over 5 years combined with recent billing inquiries typically need to speak with a senior agent. The system automatically routes these calls accordingly, reducing frustration and wait times.

 

Speech Recognition and Synthesis

Speech recognition takes in spoken words and transfigures it into text so AI can process voice commands as things happen. Speech synthesis (text-to-speech) converts responses back into natural-sounding speech.

These technologies, when combined, are especially effective for common issues like balance inquiries, password resets, and basic troubleshooting that don't require humans coming in to clean things up.

Real-world example: A customer calls and declares that they want to check their account balance. Speech recognition speedily converts all of their speech to text. Within just moments, help arrives. The AI processes the request, retrieves the balance, and speech synthesis gives them a read answer: "your current balance is $450." All without waiting for a live agent.

 

Large Language Models (LLMs)

LLMs like GPT are advanced AI systems trained on vast amounts of text data. They understand complex questions and generate detailed, conversational responses that feel human.

LLMs help the backoffice by drafting customer survey questions, analyzing agent performance trends, recommending products, and providing managers with actionable insights into customer satisfaction patterns. The best part is there's very little setup involved.

Real-world example: A frustrated customer has a gnarly billing dispute involving multiple services and promotional codes. Rather than a boring script, an LLM reads their account history then generates a personalized resolution offer that addresses their specific situation.

 

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
  • AI Voice Agents
  • AI Receptionists
  • Customer support
  • Sales support
  • Supervisor support
  • Live-agent feedback
  • Interactive Voice Response (IVR) Systems
  • Predictive Analytics

 

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.

 

AI Voice Agents

AI voice agents can handle conversations with the empathy and fluency customers can expect from human service agents. They represent systems that don’t just respond to queries but also adapt to emotional cues and comprehend context to resolve issues faster than ever before.

AI voice agents are bridging the gap between opportunity cost and recovering lost profit. 85% of customers who reach voicemail will not call you back; in just the home service sector, a lost call can represent up to $1,200 in lost revenue.[*]

 

AI Receptionists

AI receptionists are virtual assistants that use artificial intelligence to tackle your incoming calls, book customer appointments, and qualify potential leads without blinking an eye or needing sleep around the clock 24/7/365. Gartner predicts that by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention, leading to a 30% reduction in operational costs for service organizations. [*]

 

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

 

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.

 

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: AI gets customers their much-wanted 24/7 self-service capabilities. Interactive chatbot responses become based on customer backgrounds. Live agents better comprehend how customers feel because of AI analysis and AI-based coaching helps human agents develop the best strategies to handle inquiries. The end-result: an omnichannel experience spanning communication channels customers actually want that slays the competition. 87.2% of consumers have had neutral or positive experiences with chatbots[*]
  • Efficient staff: AI-powered automations and real-time support tools make it so that agent and supervisor efficiency happens in tandem. Predictive routing systems ensure the right agent gets the right call on the first attempt. Automated evaluation keeps supervisors agile to give quick feedback at scale. AI even scales back your post-call work via helpful automated summaries and next-best action optioning. Support agents say when they let AI handle things, they can service up to 13.8% more customer inquiries each hour[*]
  • Better agent training: Multiple AI tools coalesce into one machine. Think about speech coaching, AI-enabled checklists, and response suggestions that coach agents as they handle customers in real-time. Automate your CSAT scores and customer experience feedback to give agents a look into how they impact customer satisfaction to scale up teams and combat revenue-sucking high turnover rates. Customer service teams say they can scrap back up to 2 hours and 20 minutes daily when armed with AI-driven tools[*]
  • Shorter wait times: AI-powered self-service handles routine customer needs without overburdening agents, including FAQs, billing issues, and knowledge base recommendations. Predictive routing sends over your best staff to the right customer at the right time to slash down queue wait times and help optimize your at-large staffing needs. Chatbots, if the experts are right, save 2.5 billion hours for businesses and their loyal customers[*]
  • Lower costs: AI-driven customer self-service and machine learning tools make agents and supervisors dramatically more efficient, reducing call center staffing needs without sacrificing service quality. For every $1 invested in AI, businesses say they got back $3.50 on average, with some organizations saying they got up to $8. Companies using AI agents report halving their total cost per call while raising their customer satisfaction score ceilings[*]
  • Better understanding of your customers: AI-based tools like interaction analytics, sentiment analysis, and keyword detection provide deeper insights into the customer experience. Analytics on customer IVR choices, call reasons, and behavioral patterns inform smarter business decisions about products, queue management, and support strategies. By the end of the decade, an estimated 70% of customer inquiries will be handled by AI systems without human involvement[*]
  • More effective sales efforts: AI-powered predictive dialers automatically place calls while agents complete their current ones, optimizing agent talk time. Lead qualification software analyzes transcripts and behavior patterns to segment leads, ensuring sales teams pursue optimal candidates and run stronger, more efficient campaigns. AI-powered systems have led to a 31.5% boost in customer satisfaction scores and a 24.8% increase in customer retention[*]

 

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.

Key features:

  • HIPAA-compliance checklists and promotional suggestions
  • Customer data display (recent interactions, location, local weather)
  • Canned response coaching without supervisor involvement

What you get: Agents stay on track without constant manual oversight.

 

AI Call Summaries

Call transcripts are automatically summarized using large language models, capturing key action items and discussion highlights. This cuts post-call work time by up to 40%.

Benefits:

  • Administrators review summaries instead of hours of recordings
  • Faster quality assurance and compliance checks
  • Reduced administrative burden on the team

Result: Your team spends less time on paperwork, more time serving customers.

 

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 

AI-powered bots handle self-service interactions with human-like responses. They understand varied customer inputs and switch contexts instantly.

Capabilities:

  • Routing, question response, bill pay, appointment booking
  • Pull data from CRM systems, databases, customer histories
  • Operate across all channels seamlessly

What happens next: Customers get instant help 24/7 while agents focus on complex issues.

 

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 tracks all customer-agent interactions across channels and surfaces trends through customizable dashboards. Filter by agent, department, or view the entire operation at once.

Key metrics tracked:

  • Keyword frequency and IVR selections
  • Most common reasons for contact
  • Customer experience and satisfaction scores
  • Chatbot conversation patterns
  • Automation opportunities in your workflow

What you get: Your team gets to see patterns humans would miss and identify where automation can have the biggest impact.

 

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 data is everything. Implement cleaning and validation mechanisms to catch errors early.

Some priorities:

  • Keep customer data current and relevant
  • Flag inaccuracies for immediate intervention
  • Regular maintenance over time
  • Test accuracy of interaction histories and feedback

Takeaway: Your AI solutions and overall call center quality is only as good as its data, so maintain it obsessively.

 

Continuously Monitor and Evaluate

AI systems need ongoing review to stay accurate and reliable. Set clear KPIs and feedback loops.

What to do:

  • Review performance regularly
  • Identify improvement areas
  • Adjust as customer needs evolve
  • Set and track KPIs against business goals

Result: Your system stays aligned with reality, not just initial configuration.

 

Train Employees and Facilitate Adaptation

Staff understanding of AI tools is critical to success. Provide comprehensive training on how AI works, where it fits in workflows, and how to use it correctly.

Support strategies:

  • Workshops and hands-on training
  • Ongoing support from management and vendors
  • Build a culture that embraces new tech
  • Address concerns openly

Result: Adoption sticks and your team becomes proficient quickly and builds a culture that embraces the technology.

 

Balance Automation with Human Interaction

Automation handles routine inquiries efficiently. Complex and sensitive issues need human agents. Always give customers the option to speak to a live person.

Do not forget these key tenets:

  • Automation is powerful but not complete
  • Empathy and nuance require humans
  • Customers value choice
  • Agents remain essential to your operation

Result: You get efficiency gains without sacrificing service quality or customer trust.

 

Adopt a Customer-Centric Approach

Train AI to use customer data for personalization. Reference past behaviors, preferences, and individual needs in every interaction.

Integration points:

  • Internal knowledge bases
  • CRM systems and helpdesk solutions
  • Third-party tools and databases
  • Customer journey history

Result: Personalized experiences drive loyalty and stronger long-term relationships.

 

Train AI and Update Systems

AI training is continuous. Regularly update models, feed new data, and retrain algorithms to keep knowledge current.

What changes:

  • New product offerings and services
  • Company policies and processes
  • Customer needs and preferences
  • Industry trends and best practices

The end-result: Your AI stays relevant and customers always get accurate, up-to-date information. A continuous engine of improvement and customer satisfaction.

 

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