5 Use Cases of Artificial Intelligence (AI) In a Call Center

Artificial Intelligence is the latest buzzword in the business world, as it has the potential to transform business processes and outcomes.

However, AI can make significant improvements to business outcomes when the right AI solution is tailored to the right task. Organizations are currently successfully using AI for process automation, data analysis, and to interact with customers and employees.

These three uses are highly relevant to customer service operations; making call centers the ideal place to test the potential of AI. Contact centers have large amounts of complex data that can benefit from the insights provided by AI-powered analytics software. In addition, Artificial Intelligence has applications to improve CX and agent productivity and efficiency

5 Use Cases of Artificial Intelligence (AI) in a Call Center

Today, AI is used to make more accurate forecasts, help leaders proactively identify and manage problems, and offer customers self-service solutions, among others.

1) AI Use Case # 1: Self-service

These days, customer-focused organizations see self-service as a channel that can deliver satisfying experiences. They design and optimize self-service so that customers can successfully resolve their own issues or easily hand them over to an agent for additional assistance. This focus on making, for example, IVR (Interactive Voice Response) more effective and customer-friendly is paying off. A 2020 investigation revealed that, for the first time, self-service channels had higher First Contact Resolution (FCR) rates than agent-assisted methods: 62% vs. 55%.

Furthermore, natural language processing (NLP), a form of AI, has transformed the way users interact with IVR systems. Callers no longer have to “press 1”, they can simply say what they need. The NPL enables the system to understand what customers are saying and respond to them to answer their question, guide them through self-service steps, or connect them with an agent. Thanks to AI, this is a more natural experience than pressing buttons on a phone keypad.

To take IVR self-service to the next level, companies can also integrate AI-powered virtual agents into their IVRs to create smarter experiences.

2) AI Use Case # 2: Agent Assistance

The real-time interaction guide takes advantage of Artificial Intelligence to listen and analyze each call as it occurs. That is, it provides real-time feedback to agents on their social skills. For example, if the interaction guidance tool determines that a caller is stressed, it could remind the agent to show empathy. And if the agent repeatedly interrupts a customer, the system could tell the agent to use active listening skills. AI-enabled real-time coaching can help redirect tense interactions, provide immediate feedback, and can help correct suboptimal behavior before it has a chance to perpetuate itself.

The positive impact AI can have on agent performance is not limited to real-time engagement guidance. The ability to listen to and understand voice interactions means that AI-powered solutions can also help agents solve problems. For example, the Artificial Intelligence assistant could hear that a caller has a question about the software’s functionality and automatically search for the relevant knowledge base article for the agent. Smart assistants therefore help improve first contact resolution rates, increase accuracy and reduce handling times, thus improving the CX.

3) AI Use Case # 3: Forecasting and Scheduling

Forecasting and scheduling may not be the first things that come to mind when you think of contact center AI, but this particular use case makes perfect sense considering that Artificial Intelligence is well suited for analyzing large amounts of data in order to identify patterns and make predictions.

Forecasts are usually based on a couple of years of historical volume data. The forecasting process can get quite complex when you take into account historical data by time and channel increment, and also try to determine the best forecasting algorithm to apply.

Choosing the correct algorithm may require a good understanding of data science. This is where AI comes in. Artificial Intelligence workforce management software selects the best algorithm for the unique characteristics of the call center. It is a great example of how AI can increase the skills of a human team.

Scheduling software leveraging AI meets customer and business needs by ensuring the right number of trained agents are scheduled at the right times while adapting to agent preferences. And thanks to Artificial Intelligence, the schedules improve with each execution.

4) AI Use Case # 4: Chatbots and Virtual Agents

For many people, the contact center is AI = chatbots and virtual agents. When properly designed and applied to the right tasks, chatbots and virtual agents can provide substantial benefits to operations and CX.

What is the difference between a chatbot and a virtual agent?

A chatbot is simpler than a virtual agent and is based on user-configured rules. For example, a chatbot will be able to interact with a person by presenting two or three options to click. The selection will determine the next set of options or answers, and so on. A chatbot may or may not use AI.

In contrast, virtual agents use Artificial Intelligence, natural language processing, machine learning, and related technologies to understand human speech and intention. This makes virtual agents capable of handling more complex interactions than a rule-based chatbot.

Chatbots and virtual agents are being used successfully across multiple channels, including voice, chat, and messaging applications. They are more successful in handling well-defined transactions such as answering frequently asked questions, scheduling appointments, resetting passwords, and providing order status. Chatbots and virtual agents offering 24/7/365 service can handle hundreds or thousands of simultaneous interactions, allowing contact centers to rapidly increase their capacity.

5) AI Use Case # 5: Supervisor Productivity and Efficiency

AI-powered engagement analytics can be the data analyst every supervisor needs. Interaction analytics software can quickly combine and analyze all voice and digital channel interactions, developing insights into customer sentiment, trending topics, contact drivers, and emerging issues, among others. This allows supervisors, for example, to proactively resolve a new problem before it becomes another “fire” that they must put out.

Additionally, quality management analyzes can make the quality monitoring process more efficient and accurate for supervisors who play a role in the extraction and evaluation of interaction samples. This software analyzes and classifies interactions, making it easy to identify the correct ones to evaluate. Additionally, this analytics solution enables efficient problem solving by allowing supervisors to focus on analyzing specific types of interaction, for example, short calls.

AI-infused scheduling and forecasting capabilities will also reduce time-consuming monitoring tasks. More accurate forecasts will help ensure teams are adequately staffed so supervisors don’t have to deal with agent burnout.

Gartner predicts a 25% efficiency gain for customer service organizations adopting AI. That can include the use of Artificial Intelligence to help supervisors be more productive.