AI in Customer Service: 11 Ways to Use it + Examples

This is where AI-enabled tools like Sprout level up your customer care tech stack. But writing tailored responses to every customer complaint and query isn’t sustainable especially when your team is managing customer requests from multiple channels. That’s why sales and marketing teams are teaming up with customer service to understand and overcome barriers to the traditional marketing funnel. According to The 2023 State of Social Media report, 93% of business leaders believe AI and ML capabilities will be critical for scaling customer care functions over the next three years. Lyro is able to identify, classify, and redirect popular topics and queries on autopilot.

But advanced AI from Zendesk is pre-trained with customer intent models and can understand industry-specific issues—including retail, software, and financial services. This saves your business time and money, so you can start seeing benefits from day one in just a few clicks. Rather than spending hours manually configuring your chatbots, you can set up an advanced bot in a few simple clicks.

ways to use AI in customer service

On a purely technical level, it is more challenging for a computer system to deal with voice than chat, as one needs to overcome challenges like background noise, unusual speech patterns, accents and poor pronunciation. Accelerate time-to-deployment with 200+ pre-built virtual agent conversation flows across several industries. Rapidly design and execute automated conversations, compatible with any existing technology partner. Currently based in Albuquerque, NM, Bryce Emley holds an MFA in Creative Writing from NC State and nearly a decade of writing and editing experience. When he isn’t writing content, poetry, or creative nonfiction, he enjoys traveling, baking, playing music, reliving his barista days in his own kitchen, camping, and being bad at carpentry.

When it comes to complex financial and technical questions, customers show a three-to-one preference for phone calls in such scenarios. Despite challenges, AI-first companies have successfully utilized AI to enhance the capabilities of their call center representatives by leveraging speech analytics and other call center technologies. While a few leading institutions are now transforming their customer service through apps, and new interfaces like artificial intelligence customer support social and easy payment systems, many across the industry are still playing catch-up. Institutions are finding that making the most of AI tools to transform customer service is not simply a case of deploying the latest technology. Customer service leaders face challenges ranging from selecting the most important use cases for AI to integrating technology with legacy systems and finding the right talent and organizational governance structures.

The Business Perspective on AI in Customer Service

Agent-to-agent CX has the potential to alleviate what, for users, can sometimes be a dizzying overabundance of choices and achieve a superior standard of convenience and service. AI can boost agent productivity and efficiency with tools and automations that simplify workflows. Chatbots for business can handle simple requests, while automated processes eliminate time-consuming, repetitive tasks.

By automatically identifying incoming service requests, Levity helps your customer care professionals to spend more time on essential clients. Sign up for Levity today and find out how you could improve your customer support with easy-to-use, no-code AI workflows. Your AI model is only as good as the data you feed it—knowing how you can use your data is the key to uncovering AI-powered insights. Let’s take a look at some real examples of how you can use automation tools in customer service. Artificial intelligence is the key to enabling real-time service for customer support platforms.

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This reduces your team’s workload and frees your agents to address high-value tasks and complex customer issues. AI tools like Sprout analyze tons of social listening data in minutes so you can make data-driven decisions based on the conversations happening around your brand and industry. For example, customer care teams can use social listening to get ahead of product defects or service issues if they see similar complaints across social. Of course, it made data analysis more efficient, however, it was still time-consuming and tedious. Modern day AI customer service tools are self-sufficient in learning from their customer interactions.