Competing on customer experience (CX) is a focal point for many companies today. If your business is looking for an innovative way to deliver excellent, consistent CX across channels, you may be exploring the use of artificial intelligence (AI) in customer service.
AI-powered systems can process immense volumes of data at speed, extract meaning from unstructured content, and learn from interactions. And when AI is combined with process automation solutions, you can delegate a greater variety of tasks to machines.
On that premise, investment in AI is growing. Forrester reports that brands will spend $8 billion more on customer service agents in 2020 than they did during 2019.
Yet, no matter how much is invested in AI, it’s important to understand that it’s not a magic bullet. Customer service will always require the human touch—and thus a big part of leveraging AI in this area is with a focus on strengthening human-to-human connections.
Before we explore this concept further, let’s touch on the type of AI tools that are available to your business today.
Where to use AI in customer service
AI is an umbrella term for a range of technologies that mimic aspects of human intelligence. Examples include:
- Machine learning (ML):
ML software algorithms analyze huge volumes of data (typically unstructured) to identify and apply patterns, without being explicitly told to do so. A central goal, achieved through different approaches, is to learn and improve through experience.
- Natural language processing (NLP):
NLP allows systems to understand and synthesize natural language content, both text and speech-based. This field of AI is often used to power intelligent virtual assistants and enable intelligent optical character recognition (OCR) systems. These extract relevant information from scanned documents and convert this into machine-readable data.
These are just a few examples of the AI capabilities that can be pulled into your processes. By doing so, you can solve a wider range of business problems without human input—and ideally redirect human skills to areas where they add more value.
A few use cases are outlined below.
Understand customers better
Machine learning can be used to build your analytics capability, enabling you to understand your customers in more detail. As it can analyze data at high volume, in real-time, ML can pull relevant information together from diverse sources and make inferences that help you create a single, detailed view of the customer.
This intelligence then needs to be made accessible to everyone across the enterprise, in compliance with relevant data protection and privacy laws. Intelligent process automation (IPA) platforms can be used to securely connect AI tools with your existing systems of record. This way, customer-facing staff can use IPA applications to surface the right customer intelligence, at the right time, to serve customers better and faster.
For example, a sales agent serving a customer in a store would do well to understand the products or services purchased by this customer before, across any channel; as well as other purchase behavior patterns.
See an example of using intelligent process automation to manage customer data more efficiently in the video below.