5 important contact center AI features and their benefits

2 key types of contact center AI

The first category of AI that typically comes to mind for contact center use cases is conversational AI, which uses large language model (LLM) algorithms. This technology lets customers converse with voice- and text-based interactive voice response (IVR) systems, chatbots and virtual assistants.

LLMs employ natural language processing capabilities that let the contact center software understand the various nuances of written and verbal communication. This capability makes conversational AI a perfect fit for contact center use cases that want to bolster the customer service engagement and service fulfillment process without increasing staffing levels. The ability of conversational AI to analyze, retrieve, predict and pass on information in multiple written or spoken formats helps take the customer contact center experience to a more efficient level with little Opex overhead.

The second type of contact center AI uses data analysis to sift through various statistics and KPIs to make suggestions on how to improve performance or increase customer satisfaction. This type of AI helps contact center operators keep up with their performance goals without having to manually sift through and analyze data using manual or semiautomated processes.

5 popular contact center AI features

Let's look at the leading types of AI technology being integrated into contact center platforms and the benefits they deliver across five key operational areas.

1. Interactive voice response

Most contact centers use IVR systems in one capacity or another. These are speech-enabled, automated systems that use voice prompts to help callers navigate call tree menus or access information without the need for a human operator. With the advent of AI-backed IVR, however, these automated voice systems are lowering call center wait times, assisting with unique caller problems and improving overall customer contact center efficiency rates. AI accomplishes this by analyzing past customer interactions and using extrapolative analysis to predict the wants and desires of a customer. Additionally, AI integrated into an IVR system can tap into contact center agent training data to become familiar with how to handle routine tasks and typical customer inquiries. AI can then direct callers to the information they require or the customer agent that can best handle their needs.

2. Self-service chatbots and virtual agents

IM and live chat products have been around for decades. The difference between contact center chatbots using AI vs. traditional methods is that human agents aren't required. While recent surveys show that contact center users still prefer to work with a human agent, this preference is quickly trending downward. Much of this has to do with getting customers comfortable with virtual agent interactions, but conversational AI chatbots and virtual agents are also at a level of sophistication where they can handle highly granular and complex customer service requests more accurately and in far less time.

3. Real-time agent coaching and performance monitoring

At this stage, most contact centers still use a combination of AI IVR, chatbots, virtual assistants and human agents. When it comes to the human aspect of the contact center, however, a different form of AI is helping to improve the customer service experience. Today, nearly every aspect of a human agent's contact with customers can be analyzed. Examples of collected metrics include call and chat logs, handle times, time-to-service resolution, queue times, hold times and customer survey results. All this information is collected and analyzed to see how customer satisfaction can increase, while simultaneously decreasing time-to-service resolution. AI is used to track these statistics, formulate performance profiles and make automated coaching suggestions to agents.

4. Automatic call insights with predictive analysis

CRM software is commonly used in conjunction with contact center platforms. CRM systems store a wealth of customer-related data, including contact information, which products and services a customer has purchased, purchase preferences and any interaction touchpoints the business has had with the customer in the past. Using this information, relevant CRM data can be intelligently fed to human agents or chatbots to provide additional context and predictive analysis recommendations that are at the ready as soon as a customer connects via a contact center communications channel.

5. AI-generated transcription and post-call/chat summaries

A combination of automated scripts, LLM algorithms and customer analysis techniques can be used to transcribe, organize and analyze post-call and post-chat summaries. This data can then be input into a CRM system and further analyzed to determine various aspects of the customer interaction, including their overall satisfaction, likelihood of purchasing products and services in the future, brand loyalty, and which targeted marketing and sales methods are most likely to translate into future sales.

Andrew Froehlich is founder of InfraMomentum, an enterprise IT research and analyst firm, and president of West Gate Networks, an IT consulting company. He has been involved in enterprise IT for more than 20 years.