How to Use AI & Automation in Your Contact Center 

First, let’s define the difference between a chatbot and virtual agent. A chatbot relies on user-configured rules and is simpler than a virtual agent. A virtual agent uses conversational AI, NLP, machine learning (ML) and other related technologies to understand human speech and intent. They’re more capable of handing complex interactions. They have been used in voice, chat, and messaging apps successfully and are a major part of self-service. Operationally, chatbots and virtual agents allow for 24/7 support and can handle customer interactions simultaneously. 

Many customers prefer self-service options so they can successfully solve their own issues or easily transfer to an agent for additional assistance. A user-centered design and management supported by AI majorly contributed to self-service channels having higher first contact resolution (FCR) rate than agent-assisted methods in 2020. Natural language processing (NLP) has transformed the way consumers interact with IVR systems by no longer having to press specific numbers for specific departments. Instead, consumers can say what they’re looking for, be guided through the appropriate process, or connect them to an agent if necessary. The development of AI in self-service has created a more natural consumer experience that improves customer satisfaction and first contact resolution. 

The soft skills an agent possesses can greatly influence the outcome of a call with a customer. Real-time interaction guidance leverages AI to listen and analyze a call as it’s happening and then provide real-time feedback. For example, if the interaction guidance determines that a customer is upset, it could remind the agent to show empathy and point them in the right direction of how to answer. This real-time, AI-enabled coaching can deliver feedback, correct behavior, and help rescue uncomfortable interactions. AI can also help agents with problem-solving. Listening to and understanding voice interactions points an AI assistant to automatically gather relevant knowledge (like articles, videos, etc.) to help the agent become better informed with helping the customer. 

Managers and supervisors already have a lot of work on their plate. Using AI-powered interaction analytics and quality management analytics can increase their proactivity and efficiency in managing their contact center. Interaction analytics can rapidly analyze voice and digital interactions, developing insights on customer sentiment, trending topics, contact drivers, emerging problems, and more. Quality management analytics improves the accuracy of the quality monitoring process by analyzing and categorizing interactions so the right ones to evaluate are pinpointed. 

Workforce management tools with AI capabilities can be a huge help when identifying the best algorithm to use for forecasting and scheduling. There are many algorithms to choose from, so leveraging workforce management software infused with AI to select the best algorithm for your contact center’s unique characteristics can be a huge help. AI’s full-body view of contact center operations from analytics provide a significant asset that takes a daunting responsibility off management and improves the overall customer and agent experience.