Every day it seems there’s a new opportunity for AI and machine learning to transform people’s lives. A lot of the AI coverage is focused on futuristic advances in automation like Google Duplex and autonomous vehicles. But aside from these ideas, what are some practical uses for AI technology to improve the customer experience?
1. Ask the right questions
What does your team or firm hope to gain by deploying AI and machine learning (an application of AI) technology in a customer experience capacity? Is it to speed up processes, increase customer satisfaction, reduce costs, or maybe a combination of all three? And what will the success metrics be? Once these questions have been answered, it’s important to understand which tasks a machine learning algorithm is best suited for.
Machine learning works best when it’s repeatedly used to make a clearly defined decision with a set number of variables, and where errors can be quickly identified and corrected. For example, “when are customers most likely to churn” is a better question than “what’s an innovative way to drive growth?”
Other examples include looking for database anomalies to assist IT and cybersecurity teams in spotting security weaknesses or breaches, or a manufacturer using AI and sensor systems to capture and analyze data from the supply chain to optimize workflows and increase efficiency.
2. Understand AI’s strengths and weaknesses in the CX space
AI algorithms can scan vast amounts of data and spot patterns far quicker than humans can. As such, AI-powered chatbots can automatically respond to frequently asked questions, resulting in faster service, increased customer satisfaction, and lower costs.
But chatbots have yet to understand complex or unpredictable questions. Such bots are difficult “to do well at scale unless they’re very narrowly conceived,” writes Tom Austin, VP and fellow at Gartner, in a blog post. “No one outside of the biggest of the big tech companies are fielding ‘know-it-all bots’…at least not yet.”
A smarter approach would be to use AI’s analytical prowess to help employees engage with customers more efficiently and effectively. For example, AI engines can analyze calls in real time while coaching associates on how they can improve the conversation with customers. Using machine learning, the system could identify the next best question the associate should ask or quickly retrieve information to improve the customer interaction. Managers also gain insight into live calls to better manage outcomes.
3. Have a backup plan for customer interactions
For all their advances, AI systems can fail or be compromised. It’s essential that companies monitor the systems and have a clear contingency plan. For instance, human associates should be able to easily step in when a chatbot encounters an obstacle. And yet, about 60 percent of chatbot deployments still don’t have effective live-associate safety nets attached to web chat sessions, according to a recent Forrester report.
4. Communicate, communicate, communicate
The assumption is that AI, employees, and customers will interact seamlessly from the get-go. However, smart leaders know that AI projects must include a plan for communicating the impact of AI in their business and how it affects employees, customers, and other stakeholders. Additionally, it’s critical to explain how expectations, related skill sets, and customer experiences will evolve in relation to AI.
Artificial intelligence and machine learning offer a dazzling number of uses that have some companies racing to deploy as quickly as possible. This is also a sure way to waste resources. Identifying goals and success metrics based on the technology’s strengths and drawbacks, and communicating those plans with the rest of the organization is a smarter way of ensuring that the company is reaping AI’s advantages for the customer’s sake.
