Nov 14 Leveraging AI and Machine Learning for Customer Service
Artificial intelligence (AI) is an exciting field right now, extending to all domains of the enterprise. According to Salesforce, 38% of enterprises use AI, and that number is expected to grow to 62% by next year.
Customer service is the second most common use of AI by companies after IT. According to Salesforce, by 2021, efficiencies driven by AI in customer relationship management will increase global revenues by $1.1 trillion, and lead to more than 800,000 net-new jobs, surpassing those lost to automation. But as we transition into an AI-enhanced customer service model, it is important to remember that its true value lies in the collaboration between people and AI.
How AI is changing customer service
With AI and machine learning more prevalent and embedded within everyday digital services, customers are expecting intelligent and differentiated experiences, even from customer service. For example, according to Microsoft’s 2016 State of Global Customer Service Report, 78% of young consumers expect a service agent to already know them and their product history and preferences when they contact them.
Today, AI is changing the work of customer service agents by assisting them to complete tasks or by taking on repetitive tasks entirely. This enables agents to focus their attention on issues that require a personal touch, creating new value that is human in a world that is increasingly digital. For example, a robot can take on issue requests and complete simple and routine tasks, while an agent can focus on understanding the customer better and on building a more meaningful relationship.
Today’s customer service experience often begins with the Google search bar and continues on to bots and virtual assistants that can help customers find information more easily and decrease escalated calls to a call center. Already, these technologies are becoming more personal, contextual, and highly relevant. Soon, they will be sophisticated enough to meet some customer needs before the customers even knew they had them, offering even more opportunities to create unique, delightful experiences.
As organizations are becoming more digital, AI-enabled operations are getting smarter by assisting with streamlining customer interactions, facilitating scheduling, and optimizing resources. Even more significantly, with access to large amounts of data, these technologies can grow and extend our knowledge. From big customer engagement data, AI can extract useful information and uncover new patterns that could lead to new services and new business models. Ideally, as Forrester predicts, “machine-learning algorithms used for business and customer intelligence find answers to questions that humans didn’t even know to ask.”
What to keep in mind when deploying AI
As more and more experiences are built with the integration of AI and machine learning, we still have a lot to learn about how to leverage the technology in meaningful ways and keep the experience human. Here are some things to keep in mind:
Robots aren’t human-centered: Humans are innately biased, and human-made algorithms inevitably include some of our biases as well. We still need to tell machines what to solve for and need to be able to define that by understanding and addressing human needs first. That means ethnography remains critical! It also means that in the same way we try to avoid bias by putting measures in place to ensure impartiality in our research, the same scrutiny should apply to creating algorithms.
Experience is still human: Currently, AI is still relatively inflexible compared to a humans’ ability to adapt, connect and translate, but it is advancing quickly. For example, soon voice biometrics may not only be used to authenticate users by their voice, but machines may learn how to read a customer’s emotions and adjust their tone accordingly. As the gap for artificial ‘emotional’ intelligence shrinks, it is important not to forget that humans perceive services as experiences and that increased access and understanding of a customer’s shopping habits and color preferences does not equal intimacy and personality. A good customer experience speaks to a customers’ values, not just their preferences, and while AI can be a means to an end, it’s not a cure-all solution.