Contact center AI has become prevalent in contact centers of all sizes in recent years. Contact centers have adopted AI-driven tools and processes to assist with everything from data mining to produce detailed insights for customer experience improvement to analyzing customer conversations across multiple channels to protect and grow brand experience.

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This guide explores the following topics:

• What is contact center AI?
• The history of contact center AI
• Key AI technologies in contact centers
• How AI addresses traditional challenges in contact centers
• Potential challenges of contact center AI
• The future of AI in the contact center – what to expect
• Frequently asked questions

What is contact center AI?

Contact center AI blends traditional contact center operations, like customer relationship-building and inbound and outbound calls, with tech-focused processes using artificial intelligence (AI) technologies. Contact center AI can refer to a multitude of tools and processes, including speech recognition, machine learning algorithms, data mining, chatbots, and real-time coaching and guidance.

AI-powered tools can assist contact centers with a virtually endless number of tasks, including:

• Understanding customer emotions and sentiment during calls, emails, and other contact modes
• Identifying agent behaviors that increase sales
• Automating routine tasks, like organizing call transcripts, scheduling agents, and responding to repetitive customer inquiries
• Reducing wait times by routing calls efficiently
• Providing personalized responses for customers based on their calling history or preferences
• Predicting customer needs
• Transcribing calls
• Removing sensitive information from customer call transcripts

Gartner predicts that generative AI, specifically, will become a crucial form of technology in 80% of customer support organizations by 2025.

The history of contact center AI

While AI has certainly become more sophisticated over the last few years, AI in call centers isn’t necessarily new. In fact, the term artificial intelligence dates back to the 1950s, although it would be a few more decades before AI became more mainstream in contact centers.

Automatic call distributors (ACD) were a more modest form of technology that allowed call centers to manage and assign calls based on an intelligent algorithm—essentially, an early form of the AI-driven call routing systems that we see today.

It wasn’t until the 90s and 00s that AI tools became more commonplace, leading to basic automation and chatbots, which eventually gave way to more complex AI systems and processes, including natural language processing (NLP) and conversation intelligence.

Milestones in contact center AI and technology development

Contact center AI looks much different today than it did even 10 or 20 years ago. Here’s a look at some of the major milestones of technology and AI development in the contact center:

• 1950s-1960s: ACD systems began cropping up, helping call systems direct calls to appropriate, available agents.
• 1963: The invention of the touch-tone phone, which allowed callers and agents to dial a number without first speaking to an operator.
• 1960s-1970s: Interactive voice response (IVR) technology became more widely available for contact centers, although it was still quite expensive for full integration.
• 1980s: Call centers started using predictive dialers, which place calls without agent intervention. These tools help to reduce idle time between calls and increase customer connections.
• 1980s: Machine learning began blending with natural language processing for deep learning to create data-driven insights, the basis for what contact centers use to learn more about customer behavior and analyze sentiment.
• 1993: Text messages went mainstream, eventually providing a new way for companies and customers to interact.
• Early 2000s: Chatbots began assisting call center agents with handling repetitive customer questions and requests, although the first chatbot, ELIZA, was created in 1966.
• 2022: Generative AI becomes a mainstream technology with the release of ChatGPT. Contact centers use generative AI to get quick answers for agents and customers.

Key AI technologies in contact centers

Today’s contact centers use multiple forms of AI to speed up their processes, learn more about customers, and help agents complete their tasks. The following are the common overarching AI technologies we see most in modern contact centers.

Natural language processing (NLP)

Natural language processing—better known as NLP—helps computers understand human language. In the contact center, NLP applies to both written and oral communication, so it can be used during phone conversations, transcripts, social media conversations, and more.

NLP is the driver behind contact center technologies like IVR. IVR systems use NLP to understand what a person says when they call a contact center, moving them through an automated system personalized to their responses.

NLP also allows chatbots to understand what humans say to respond appropriately and pull context from conversations to give agents the insight they need to assist customers. Sentiment analysis—a process that uncovers emotion and sentiment data from conversations—also relies on NLP.

Machine learning and predictive analytics

Machine learning and predictive analysis work hand-in-hand in the contact center to identify patterns in customer behavior and use those patterns to predict future behaviors and customer needs and wants.

• Machine learning is a type of AI consisting of data and algorithms. Machine learning studies data for patterns and trends and uses that data to make computers and software smarter through complex algorithms. When you see advertisements in your social media feeds for products you’ve bought or have considered buying, machine learning is working to personalize your feeds.
• Predictive analysis requires machine learning to work. Think of machine learning as the tool that uncovers customer behavior data, while predictive analysis makes sense of that data by making predictions for the future. Predictive analysis uses your data from hours, days, weeks, months, or years ago to forecast what your customers may need in the future.

Voice, speech, and text analytics

According to Statista data from 2022, more than half of customers rely on phone calls to resolve a customer service issue, while 38% prefer other digital methods and 8% prefer email communication. The fact is that customers use numerous methods to contact businesses, and contact centers need to be ready to analyze information from each channel.

Enter voice, speech, and text analytics, which allow contact centers to extract data from omnichannel conversations for monitoring, training, and predicting.

Text analytics works with text-based data, like phone call transcripts, emails, text messages, and social media conversations. Voice and speech data typically work together for spoken conversations. While voice analytics detects tone, speech analytics digs deeper into the actual words and phrases used.

Together, voice, speech, and text analytics monitor unstructured data to ensure compliance, learn how customers feel during a conversation, and provide deeper context for agents.

How AI addresses traditional challenges in contact centers

There’s no arguing that contact centers today are far more advanced than they were in the early days of call centers. Much of the transformation we’ve seen is thanks to AI and the technologies leading up to the modern AI tools and processes we see today.

Below, we detail how traditional challenges in call centers have become almost obsolete with the help of AI.

Customer experience

In early call centers, agents had to do all the work of figuring out customer wants and needs by actively listening to what customers say. Then, they had to try to address those needs using what they knew about the customer and providing their best solutions.

In today’s contact centers, agents still need to be able to understand and empathize with customers to address their needs appropriately. However, AI has made it possible to gather data during every conversation, organize that data, and present it to agents, marketers, and decision-makers to optimize customer experiences.

It’s because of AI, for example, that contact centers can quickly resolve questions and complaints by building personalized, interactive systems, offering omnichannel support, and providing knowledge bases and chatbots for customers to get the help they need when they need it.

AI also enhances customer privacy and data protection by keeping contact center processes compliant with relevant laws like HIPAA and GDPR. For instance, AI tools can detect and redact sensitive information in phone calls and transcripts and encrypt stored data.

Brand experience

Brand experience refers to customers' feelings and experiences toward and with your brand. Before modern, AI-driven contact centers became more common, brands used to have to manually monitor different channels, like review sites, blogs, emails, and social media, to gather customer opinions, present that data to the appropriate teams, and find solutions to improve brand experience.

Today’s call centers use AI for these tasks. AI processes continuously monitor all customer conversations, proactively seeking out mentions of a brand across multiple channels. Then, they organize all that data into detailed reports to allow all teams to access the necessary information.

Contact centers do more than just handle customer calls or chats. They deliver the positive #CX that drives loyalty, trust and satisfaction.

Head to the CallMiner blog to learn how the interactions happening in your contact center are driving brand experience.

— CallMiner, Inc. (@CallMiner) June 20, 2024

As a result, contact center agents don’t need to listen or watch for brand mentions, instead focusing solely on assisting their customers while AI works in the background to uncover brand experience data.

Operational efficiency

Call center agent jobs have transformed from their traditional multitasking roles. In more primitive, pre-AI call centers, agents were tasked with not just taking calls but also routing calls, taking after-call notes, escalating calls, memorizing scripts, and more.

Now, contact center agents are still multitasking professionals, but in a much different way. Modern agents spend the majority of their time communicating directly with customers to handle inquiries and issues rather than completing the tedious admin tasks that AI now takes over. AI systems route calls, provide transcripts of conversations for other agents to review as needed, and provide real-time scripts and suggestions for agents.

Essentially, AI has cut down on unnecessary operational costs by taking on the legwork of behind-the-scenes call center tasks. The benefit of this is two-fold:

1. Contact centers can reduce labor utilization by using AI to increase operational efficiency and revenue. Gartner predicts that AI will help cut contact center labor costs by as much as
2. Contact centers can improve agent experience by eliminating menial tasks and allowing agents to focus on their primary responsibility of assisting customers.

AI can provide agents with answers to their questions in real-time to avoid interruptions in a conversation, perform intelligent routing and resource allocation, automate repetitive tasks, and provide detailed, continuous analytics and reporting for consistent improvement.

Potential challenges of contact center AI

AI is still evolving; therefore, contact centers shouldn’t expect perfection. AI has its challenges, regardless of the size and industry of the organization using it. A few challenges of contact center AI include:

• Employee willingness: Not all contact center agents are on board with using AI. They have a valid concern that the very technology they use to help them could, in the not-so-distant future, replace them. McKinsey predicts that by 2030, about 30% of current work hours in the United States will be automated, reducing the need for the humans who typically complete them.
• Integration: Contact centers typically have systems they use every day to handle tasks, like organizing customers and conversations and increasing sales. New AI systems don’t always play well with existing systems, potentially interfering with security, workflows, and processes contact center agents have come to rely on.
• Privacy and security: Data breaches can happen in any company with any technology, old or new. However, the boom in contact center AI can create some additional security risks. Things are moving fast, and the onus is on contact centers to ensure compliance with relevant privacy laws at all times, with each new technology they implement.
• Human touch: Some customers prefer the human touch versus being greeted by and communicating with computers. AI has not yet evolved enough to replace the empathy and compassion human agents can provide.

The future of AI in the contact center – what to expect

AI is likely here to stay in contact centers, which are beginning to rely on the time-saving and cost-reducing benefits AI technologies offer. As AI progresses, we may begin to see it play a more significant role in many contact center applications, including hiring and training agents, providing more sophisticated location-based services, and using virtual reality to connect customers to brands even further.

Still, many unknowns remain regarding how AI will affect agent jobs and how customers will feel about potentially interacting more with technology than humans when they need an answer or have an issue to resolve. Download the CallMiner CX Landscape Report to learn the state of the CX industry and its trajectory.

Generative AI is set to redefine the contact center and customer service.

According to @forrester, #genAI will force contact center leaders to overhaul long-neglected tech, navigate an evolving tech ecosystem, and strategize for an AI-augmented future.

Read more ⬇️

— CallMiner, Inc. (@CallMiner) August 6, 2024

The CallMiner platform is a powerful AI-driven platform used across industries to increase sales effectiveness, improve the customer experience, and enhance agent performance. Request a demo to discover how CallMiner’s AI-powered coaching, contact center analytics, and other AI-powered tools can unlock your contact center’s success.

Frequently asked questions

How does AI improve customer service in contact centers?

AI has numerous cost- and time-saving benefits for contact centers, including helping customers find the answers they need quickly, reducing the workloads and improving the workflows of agents, and cutting down on tedious administrative tasks. AI can also help contact centers understand and predict customer behavior to provide personalized services.

Which AI technologies are most effective for enhancing contact center operations?

Contact center operations rely on several AI technologies, with natural language processing, machine learning, predictive analysis, and speech analysis among the most effective. These AI technologies work together to understand customer behavior and speech patterns by gathering insightful data during and after customer conversations. Contact centers use this data to personalize interactions and pinpoint solutions for customers.

What are the main challenges of implementing AI in contact centers?

As much as AI can assist contact centers, it isn’t without its share of challenges. Agent pushback is a primary concern for contact centers, as they may fear the technology they’re required to use will eventually take over their jobs. Data privacy and security can also be a challenge as new technologies are continuously reaching the market and quick implementation without proper auditing and monitoring could leave systems vulnerable to data breaches.