Artificial Intelligence (AI) is going to be a defining trend for contact centers in 2023, and IT leaders must be prepared to think broadly about the possibilities. When it comes to AI and customer service, automation usually comes to mind. While that’s an important driver, it’s not the only use case. Chatbots are the most common way to automate self-service, but to get fuller value, a wider spectrum needs to be considered.

That consideration will only come by shifting focus from customer service to customer experience (CX). The legacy approach to customer service is largely reactive, where agents respond to incoming inquiries and are not involved with what happens before or after the inquiry. Contact center technologies have long been built around that model, but as digital transformation impacts all facets of work and life, customer expectations have evolved.

CX has gained currency because it’s a more accurate reflection of what’s important to customers and how they want to be treated. Conventional forms of customer service can be very effective at addressing problems in the moment, but not the broader spectrum that is the customer journey.

In that context, the use cases for AI expand as the contact center would now be taking more of an end-to-end approach with customers. This means there will be touch points to consider, including before the interaction, during the interaction and after the interaction.

AI Use Case 1: CX Before an Agent Interaction

This is where the use of AI can be totally transparent to customers and can make a world of difference to CX. Intelligent call routing is a great use case as AI leverages its inherent strengths to make a multitude of decisions in real-time to best match each inquiry with the right agent. Legacy forms of call routing can be effective at a high level, but AI applications can process vastly more data – both structured and unstructured – at wire speed, resulting in higher accuracy of matching.

At a base level, AI applications can draw from CRMs to match the customer profile with the right agent. If the inquiry starts with an AI-driven chatbot, other AI applications can determine when (and if) to escalate to an agent. Once the agent takes over that interaction, all the details and context will follow, allowing the interaction to continue without missing a beat.

On a more granular level, AI-driven chatbots can use sentiment analysis to gauge the customer’s mood or intent, and might determine which agent should take the call. The same holds for language, or even dialect, where the chatbot can route the call to an agent who can converse natively with the customer.

AI Use Case 2: CX During an Agent Interaction

AI can support agents in many ways during interactions with customers and CX will depend on how effectively the agent interacts with the customer. Setting the stage is important, but until that interaction unfolds, the agent cannot predict the outcome.

To get the best outcome possible, AI can play many roles. Sentiment analysis come into play again, but this time it will be based on speech not text. AI applications are constantly improving their ability to accurately detect sentiment from speech, and customer behavior based on the agent’s response. AI applications can coach agents in real-time to better handle difficult situations. Supervisors can still play that role, but they cannot respond nearly as fast, nor can they provide that coaching for every customer interaction.

AI Use Case 3: CX After an Agent Interaction

The agent’s job may be done once the interaction ends, but that’s not where CX stops. It is critical for contact center leaders to think in terms of customers’ value to the business. Addressing customer problems remains essential, but success today is based on understanding and nurturing the entire relationship with customers. When CX is good, customers will stay loyal, they’ll buy more and will refer others to become customers as well. According to a survey from Emplifi, 86% of customers will leave a brand they were loyal to after two to three bad experiences.

For customers to remain loyal they need to feel satisfied and valued. AI can play a leading role in the post-call experience, which can often be the best time to show customers you care. One way is with a follow-up survey or poll. Aside from getting valuable feedback for improvement, these surveys provide more data to help fine-tune the AI algorithms.

While not all customers will respond to surveys, AI can fully automate the process so customers automatically receive surveys after an interaction. This is a big step forward from what legacy systems can support, but for truly customer-centric businesses, AI can take things even further. When integrated with a Customer Data Platform (CDP), AI can generate highly personalized follow-up offers or promotions that can drive revenue growth. Whether tailored as a make-good for an unhappy customer, or a long-tail offer for a happy customer to try something new, this helps create an end-to-end connection that elevates CX well beyond customer service.