Home Think Topics Contact Center AI What is contact center AI?
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Published: 10 July 2024
Contributor: Keith O'Brien, Amanda Downie

What is contact center AI?

Contact center AI (CCAI) means that a business uses advanced artificial intelligence (AI) technologies to automate several customer service functions and provide valuable tools to customer service representatives.

Organizations are increasingly using AI as a workforce management solution to power the customer experience. AI-powered contact centers automate a company’s ability to resolve basic customer issues while freeing up customer care professionals to manage more advanced customer interactions.

A modern customer service experience means providing customers with the ability to use self-service options to get quick answers. For example, customers might not want to wait to speak to a live agent if they can get a good answer quicker through a chatbot. AI technology can help optimize every customer support touchpoint on the customer journey.

In addition, customer service calls are expensive to manage. Estimates vary, but every customer call can cost several dollars1(link resides outside ibm.com) in labor and resources.

Contact center solutions that are powered by AI involve two separate functions. One involves eliminating time-consuming tasks from human agents so they can focus on larger issues that need their attention. The other involves human agents that use generative AI in the call center to look up answers and get suggested answers. Doing so improves agent performance and likely leads to improved employee satisfaction.

Customer service has surpassed other business functions as CEOs’ top generative AI priority. As more organizations prioritize their AI-driven contact center operations, the competitive advantages arise for those organizations who best marry human agents with generative AI technologies.

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Why contact center AI solutions are important

Several reasons for contact centers to embrace an AI platform and technology include:

Replaces manual and repetitive tasks: Organizations have been using customer service chatbots and virtual agents for some time, but new organizations can now implement generative AI-powered technologies that use conversational AI. Conversational AI uses customer data, machine learning and natural language processing (NLP) to recognize human speech and text inputs, which in turn, it can use to respond in a similar language.

Powers real-time remediation: Contact center agents no longer need to stick to static scripts that do not consider an individual’s specific needs. Giving contact center employees access to generative AI-powered dashboards can boost agent efficiency and decrease incorrect or flawed answers. That creates an opportunity for representatives to chat with customers more confidently, knowing that they have as much real-time information as possible.

Taps into other aspects of the business: Business intelligence tools can use APIs to pull in customer service data to make more informed decisions about product and marketing decisions. AI, especially NLP, can take call transcripts and identify actionable insights that can help the product and marketing teams. The data can be added to customer relationship management (CRM) or business intelligence databases. For example, many people having issues with device setup might imply that the product instructions are unclear. People who keep pressing the wrong button on a product might suggest to the product team they should consider a revised design.

Creates seamless omnichannel transitions: Many customers who begin with a self-service tool ultimately need to talk to a live representative. Equipping that employee with the right information so customers do not need to repeat themselves can be the difference between a happy or a disappointed customer. A Gartner study2 (link resides outside ibm.com) found that nearly two in three customers that had a seamless transition from self-service to a live agent will return to self-service next time.

Creates personalized customer journeys: Contact center AI can create workflows that easily unearth previous conversations with and needs of a specific customer. This data allows the AI and human agents to provide more specific and helpful answers to the customer.

Powers predictive analytics: Organizations can use AI to analyze historical data to predict future likelihoods of increased call volume. It can also anticipate which issues can require more attention based on emerging customer queries.

AI use cases for the contact center

There are several ways contact centers use AI to improve the efficacy and efficiency of their operations.

Customer-facing chatbots: Organizations can take the pressure off the call center by allowing customers to submit their questions to AI-powered bots and virtual agents that provide intelligent responses. This type of AI-powered customer engagement relieves pressure on call center representatives by handling simple queries online.

Interactive voice response (IVR): Organizations can use automated telephone system technology called IVR where callers request information that uses voice or menu inputs. The technology historically used a dual-tone multifrequency (DTMF) interface to produce prerecorded messaging or text-to-speech technology. AI, specifically NLP can increase different ways callers can interact with computers on the phone. AI-driven IVR systems can better understand and respond to inquiries in real time.

Intelligent call routing (ICR): Organizations need a way to send specific calls to the right customer service representatives or continue servicing with IVR or other automated needs. ICR systems use algorithms that are trained on caller details to send requests to the right agent. It can be especially valuable for organizations that have specialized services or products—where certain agents train on specific focus areas. The first representative can help the customer on the line instead of needing to transfer them to another representative.

Customer sentiment analysis: AI can help organizations better understand how customers feel about their products or services. AI can help understand the language that is used in customer interactions to know whether customers are frustrated or happy with the support they have received. Organizations can analyze everything, including social media, emails, feedback forms, customer call transcripts chats, online reviews and comments left on knowledge bases. Sentiment analysis helps organizations deliver a great customer experience and improve their brand reputation.

Real-time agent assist: This technology pulls information from customer conversations in real time through speech analytics or NLP, providing customer service representatives with key information. For example, a customer can discuss an issue with a setting that has come up in the past. AI tools can identify that common complaint and provide guidance on what feedback has helped in the past.

Benefits of contact center AI

AI helps organizations meet business needs across the enterprise, and the customer experience is no different. There are several key benefits contact centers can realize by using AI.

Improves customer satisfaction: Almost 90% of customers value the experience a company provides as much as products or services, according to Salesforce3 (link resides outside ibm.com). Organizations that invest in AI technologies should expect improvements in key metrics like customer satisfaction scores (CSAT). Good CSAT scores can often demonstrate that the customer service offer meets customers’ needs.

Increases operational efficiency: Organizations that use AI in contact centers streamline how they attend to customers’ needs. Improving agent productivity means that the organization can answer more consumers' basic questions quicker and allow those employees to focus on more important or complicated matters.

Drives employee satisfaction: Improving the agent experience likely improves overall job satisfaction; for businesses today, this approach includes the use of AI tools to do their jobs better. Also, organizations can use AI to remove unnecessary manual tasks from employee workloads.

Reduces wait times: AI-powered call centers are more likely to resolve issues quicker, either through automation or more effective agents. By reducing the average handle time, customers are happier, employees encounter less stressful scenarios, and the organization can resolve more requests for help.

Reduced call volume: Offering customers ways to solve their customer service issues online without talking to a customer representative decreases the volume of calls. That means more people resolve their smaller issues online. The remaining calls are customers with larger problems, and they wait less time to talk to a customer service agent.

Eliminate customer churn: A call to the contact center can either strengthen or jeopardize a relationship with a customer. Those customers who get their questions answered quickly and correctly are more likely to remain loyal to a company from which they already buy.

 

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Footnotes

1 Managing Your Cost Per Call (link resides outside ibm.com), Curtis Barry & Company
Gartner Survey Finds 62% of Customer Service Channel Transitions are “High-Effort” (link resides outside ibm.com), Gartner, 11, July 2023
3 What Is Customer Service? (link resides outside ibm.com), Salesforce