Key takeaways

  • Humans develop the algorithms and train the AI models that allow chatbots and virtual assistants to mimic human conversations and respond appropriately to user inquiries.
  • Artificial intelligence chatbots and virtual assistants are trained using data models from historical business data. Therefore, the correct dataset must be used and properly cleansed.
  • Chatbots and virtual assistants are not ethically aware of sensitive personnel data or mandated compliance laws or regulations.

Artificial intelligence can optimize and improve customer-facing systems or processes for businesses that provide customer service or a support function like a customer relationship management (CRM) system to customers. A call center is specifically created to support customers for a company that requires satisfied customers to be successful. Artificial intelligence enhances the abilities of a customer service or support function to meet or exceed the customer support or service requirement.

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What is an AI call center?

An artificial intelligence (AI) call center uses AI technologies like machine language (ML), voice recognition, and natural language processing (NLP) to improve the customers’ experiences by automating and optimizing routine tasks, such as intelligent routing calls to the right agent, answering FAQs, and scheduling appointments.

These routine tasks free up call agents to address outlier issues like identifying the root cause of a recurring problem, following up with a customer to ensure the issue is resolved, or answering specific questions about service, products, or billing. Artificial intelligence reduces wait times and provides faster response times, improving customer satisfaction.

Benefits and challenges of AI in call centers

Benefits of AI

The artificial intelligence resources used in a business function or process typically improve a business’s decision-making using data-driven insight. A call center using AI capabilities improves responsiveness and tailors interactions to meet customer needs. Using AI technologies is beneficial to call centers by enhancing the following functions or processes:

  • Predictive analytics: AI can predict customer behavior for a specific season by analyzing the previous year’s historical seasonal data or addressing a potential issue before it becomes problematic through an outreach problem resolution effort.
  • Cost reduction: Automating routine tasks reduces the workforce needed in a call center
  • Increased efficiency: AI’s ability to handle routine tasks allows call agents to focus on complex issues, reducing resolution times and improving productivity.
  • Improved customer service: AI-powered Chatbots and virtual assistants provide immediate assistance and can answer basic questions 24/7.
  • Personalized customer experiences: AI can tailor a specific response to customers by analyzing their data and providing a recommendation or suggestion that meets their needs.
  • Data-driven insight: AI can analyze historical call records and customer interactions to identify patterns or trends a business can use to make data-driven decisions.

Challenges of AI

The potential benefits of AI are significant, but it does come with a number of steep, but manageable, challenges.

  • Natural Language Processing (NLP) misinterpretation: Due to a mispronounced word or slang, NLP can send a customer an incorrect response.
  • Lack of human empathy: AI responses cannot detect emotions and are void of emotional reactions to a frustrated customer with a recurring problem.
  • Privacy issues: analyzing customer data using AI can potentially be a Personally Identifiable Information (PII) issue requiring human intervention before further action is taken.
  • Data Quality Concerns: For AI to be effective, the data must be high-quality data, which can be a challenge to collect and maintain without a robust data governance program.
  • AI training and maintenance: AI models may require continual re-training for any new customer service updates, issues, or customer complaints to be effective 24/7.
  • Integration difficulty: Implementing AI into an existing call center system may require hiring a contractor specializing in integrating AI technology into an existing business system.

How to use AI in Call Centers

Artificial intelligence is used in a call center to execute routine tasks, such as data entry, call routing, and responding to basic customer inquiries. Call centers use a knowledge database that agents can use to quickly access accurate information about common customer issues, allowing every agent to provide the same up-to-date information consistently.

Artificial intelligence can help keep the knowledge database updated with the most relevant information agents can provide to customers asking the same question. This is done by analyzing customer interactions in real-time, flagging outdated content, and identifying missing or gaps in information. After analyzing the customer interactions, AI can create new content using NLP that generates new knowledge articles based on common customer inquiries that are turned into Frequently Asked Questions (FAQs). 

On top of that, artificial intelligence can also analyze feedback from surveys and post-call interactions; it can also identify which knowledge-based articles need to be updated based on a rating system. It can also be used to perform content quality checks on active knowledge base articles to identify obsolete information that must be updated. An added benefit of having this knowledge base is that it reduces operational and training costs by providing accurate and readily available information for new agents. 

Call agents’ efficiency improves by spending less time searching for information when AI is used. The onboarding process and training for new agents are reduced when AI automatically updates the knowledge database. Faster resolution times combined with customers receiving accurate information improves customer satisfaction. Artificial intelligence can monitor agents’ calls to assess their performance and identify areas for improvement.

Best Practices for Implementing AI in a Call Center 

When deciding on an AI system to integrate into a call center, businesses must clearly define the goals and objectives the AI system will accomplish. There are several AI call center metrics to consider, such as average speed of answer, first contact resolution (FCR), average handle time (AHT), abandonment call rate (ACR), and escalation rate. These are all examples of metrics a business can use to determine the success of an implemented AI system.

To implement a successful AI system, follow these recommended best practices:

  • Set clear goals: Depending on what a business wants to accomplish, determine the Key Performance Indicator (KPI) metrics a company will use, such as improving customer satisfaction, reducing ACR or call handling time, or improving agent productivity by evaluating agents’ performance using the AI system.
  • Integrate with existing system: Use project management principles with a charter approved by leadership and consider hiring a contractor specialist with AI system integration experience if this skill set is not in-house.
  • Data security and privacy: Ensure the AI system complies with relevant regulations and customer information safeguards to prevent breaches.
  • Performance monitoring and quality control: Constantly strive for improvement using AI to monitor call quality while providing feedback to agents for improvement that enhances the overall call center system.
  • Intelligent routing: Configure AI to route calls to the most qualified agents based on customer needs or the type of call received.
  • Predictive analytics: Use AI to analyze historical data to predict future call volume and staff your call center to adequately meet call demands.
  • Agent training and support: Routinely provide agent training on the best practices for effectively using the AI system and its capabilities.
  • Customer feedback analysis: Provide customer feedback opportunities that can be analyzed to optimize the AI system further, leading to an improved customer experience. 

When appropriately used, artificial intelligence enhances any business function or process, and call centers are no different. Listed here are some of the current AI trends that enhance the abilities of call centers to be more efficient:

  • Automated tasks: Automated tasks streamline operations and reduce costs by relieving call agents from doing routine tasks and allowing agents to focus on outlier issues from customers that an AI system cannot handle.
  • Personalized responses and support: AI tools can analyze large amounts of customer data using NLP and ML to understand the context of a conversation and provide relevant responses or solutions.
  • Omnichannel communication: AI-powered systems integrate a customer’s chats, voice calls, emails, and social media channels into a single platform, allowing customers to switch between communication channels easily.
  • Real-time analytics: Help sales agents understand customer behavior in real time.
  • Sentiment mapping: AI helps determine the emotional tone of a conversation.
  • AI Quality Assurance (QA) systems: Automatically analyze calls between agents and customer interaction using speech recognition to identify trends, evaluate call agents’ performance, and provide real-time feedback.
  • Interactive voice response (IVR): An automated telephone system that uses speech recognition and NLP to allow callers to interact with a business through voice commands and provide self-service options using a voice menu without speaking to a live agent.
  • Predictive call routing: Uses ML to identify the best call agent to serve a customer.

VoIP cloud systems to consider

Two popular phone-based cloud systems that can enhance call centers with AI functionality are RingCentral and Nextiva.

RingCentral

RingCentral logo.

RingCentral features all the AI tools to automate tasks and help new or current agents receive real-time assistance. RingCentral can extract relevant information from FAQs applicable to a customer’s issue. This product also helps avoid compliance issues by sending reminder policies, standard operating procedures (SOPs) practices, solutions, and other relevant options as pop-up messages during real-time customer conversations when required.

Try RingCentral

Nextiva

Nextiva logo.

Nextiva’s conversational AI chatbots and sentiment analysis feature allow the chatbot to consider multiple factors before responding to a customer’s complex issue. The customizable scripting feature can tailor call flows based on a customer’s unique situation. The detailed call analytics AI resource can summarize an agent’s performance, call metrics, and customer trends to optimize call center operations. Nextiva’s advanced AI-driven routine uses ML to route calls to the most qualified agent based on a customer issue.

Try Nextiva

Frequently Asked Questions (FAQ)

Artificial intelligence can perform customer service actions, such as responding to routine inquiries and tasks, improving agent productivity by focusing on customer outlier issues rather than routine tasks, and identifying trends or patterns in customer emails or messages. Additionally, AI can analyze customer behavior, preferences, and purchase history to make personal recommendations or for future seasonal sales.

To enable AI on an iPhone, ensure you have the latest iOS version, iOS 18.1 beta. If you do, go to settings, then click Apple Intelligence & Siri. If it is inactive, you may have to join a waitlist to use Apple Intelligence.

To enable AI on an Android phone, go to your settings, find Google Assistance or something similar, like AI assistance, and toggle the switch to on.

You need the following hardware to use AI:

  • CPU: A central processor unit (CPU) and graphics processing units (GPUs) to use ML.
  • Memory: AI operations use GPU memory
  • Connectivity: A computer must come with I/O ports, thunderbolt four ports, USB type-A ports, an HDMI port, and most importantly, fast internet connectivity with the latest Wi-Fi technology.
  • Power supply unit (PSU): A PSU with enough wattage to support the entire system (i.e. CPU and GPUs)

The outline for creating an AI call agent is the following:

  1. Plan and design stage
    1. Identify what the call agent does, such as providing product information or answering basic customer questions.
    2. Collect large amounts of relevant data applicable to what you want the AI agent to do, such as answering FAQs or gathering product details
    3. Select a cloud-based AI platform like Amazon Lex, Google Diagflow, or Microsoft Azure Bot Service.
  2. Data Preparation stage
    1. Clean the data by removing errors, inconsistencies, and irrelevant data from your training data.
    2. Label key elements in the data, such as customer actions or unique details like product names, depending on what the AI agent is tasked to do
  3. Build conversational flows
    1. Build different actions a customer might take and the specific details the customer might provide for a customer issue or product
    2. Structure the type of conversation (FAQ or product) into logical steps that AI may ask to gather relevant information so that AI can respond appropriately.
    3. Create human-like responses for each scenario based on information collected from 3b.
  4. Training the AI model
    1. Upload the cleansed training data into your selected AI platform from 1c above. 
    2. Execute the model training by allowing the AI to learn patterns and relationships within the data to generate accurate responses.
    3. Fine-tune the results by continuously adjusting and refining the model based on performance metrics and feedback.
  5. Integrate with the phone system
    1. Integrate your AI platform into your existing phone system to enable seamless call routing and interaction using an API connection
    2. Define how calls should be directed to the AI agent based on customer input or specific criteria
  6. Testing and refinement
    1. Implement controlled testing with a small group of users to help identify areas that need improvement 
    2. Optimize the AI agent by analyzing the results, updating training data, and refining the conversational flow to increase the accuracy of the AI agent’s performance. Continue the optimization process until the results are consistently correct.
Don Hall Avatar
Don Hall
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Don Hall is a contributing writer to Technology Advice and Enterprise Storage Forum, where he covers data storage technology, storage hardware and software, and data networking. He worked for more than two decades as an IT Supervisor for the federal government and as IT Operations Supervisor for an IT Military Command managing programmers, cybersecurity staff, and infrastructure and networking personnel. Previously he worked as an application programmer. Don earned a B.S. in Business Information Systems from San Diego State University and has certificates in Technical Communication and web development with an emphasis in Java/Open Source. He has also had an active CompTIA Security + (ce) since 2011, and a Network +(ce) since 2015.