5 AI Use Cases Every Customer Service Leader Should Be Scaling
AI is reshaping the future of customer service, no doubt. Explore five key applications making support faster, smarter, and more human.
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One thing most customer service conversations have in common? A frustrated customer on one end, and a support agent eager to help on the other. But turning frustration into satisfaction isn’t easy. Customers aren’t just looking for quick fixes—they want smart, empathetic solutions to their problems. Brands around the world are turning to AI as their trusted ally.
According to a recent report by Cequens and Martechvibe, 82% of marketing leaders are looking to invest in genAI tools, indicating a strong willingness to adopt advanced AI technologies to enhance customer interactions and service efficiency.
The results of the study show that customer service stands at the forefront of AI adoption. The momentum is high—77% of marketing leaders are beyond the initial adoption phase and are now looking to scale up and expand their AI capabilities within their customer service operations.
Clearly, AI has found a multitude of applications in the domain of customer service. While these vary from broader terms like automation of tasks to more specific applications like sentiment scores, here are five top areas of implementation for AI in customer service.
Automation: Query Routing, Faster Response
AI can automate repetitive tasks and free up agents for handling complex issues that need human assistance. It also helps in routing queries, saving plenty of time for agents. As per the study by Cequens, 15% of marketing leaders plan to use AI to automate processes like triage in cases of medical emergency.
Routing of queries to the right agent depending on the customer’s channel is also helpful in ensuring a satisfactory customer experience. “Containing customers in their channel of choice is crucial. Whilst there is always an element of risk associated with ‘non-conventional’ channels, as an organisation, we have to solve this and eliminate the risk by upgrading our infrastructure rather than put our customer through inconveniences and hurdles,” says Asma Beljaflah, Head of Emirates Islamic Contact Centre at Tanfeeth.
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Further, when the number of incoming queries is high, agents can become overwhelmed. Customers expect a fast response time to their tickets and ensuring timely responses to customer issues is a significant challenge for 57% of customer support agents. AI can help ensure customers are responded to in a timely manner.
Workforce Management Optimisation
The contact centre is one of the most populated places one can think of—hundreds of customer support agents on numerous calls, assisting thousands of customers. For efficiently managing the massive volume of incoming calls and queries, there is a requirement for a well-managed workforce schedule too.
AI can help support teams here by analysing historical data over years to assess what time of the year, month or week requires what kind of support. By predicting staffing needs, AI-powered workforce management can automatically create team schedules and hence reduce overtime costs. Marketing leaders are increasingly adapting this application of AI for the contact centre, with 74% having already added workforce management and productivity tools in their current tech stack for customer service.
Real-Time Assistance
Communicating with a customer who is disappointed and looking for answers can be overwhelming. AI agents can be a great aide by providing customer data in real-time or offering relevant information from past interactions to tailor the communication. Human support agents can use this data to personalise their conversations.
Further, AI can even help draft quick responses keeping customer’s preferences in mind, helping in quick resolution. Over 66% of marketing leaders are looking to invest in predictive analytics tools, suggesting a proactive approach to using AI for anticipating customer needs and behaviours, thus improving service strategies and outcomes.
Customer Intent and Sentiment Analysis
When customers reach out to customer service, they usually are experiencing anger, frustration and disappointment. At such a time, they need polite, careful, helpful handling. Handling customers’ extreme emotions is a considerable challenge for 46% of customer support agents. They can use AI to understand the customer’s intent and also get a sentiment analysis, which can help them tailor their communication to fulfill the customer’s needs, keeping their emotional state in mind.
Currently, 27% of marketers plan to use AI to process natural language and understanding during communications and 42% plan to use it to support human agents with real-time information and sentiment scores. Interestingly, 88% are also aiming to add voice recognition for customer interactions, highlighting readiness to integrate sophisticated AI functionalities that can streamline communication.
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Resolution with Self-Service
AI has been a huge supporter in helping quickly resolve routine queries that don’t need detailed human assistance, and by doing so, it helps increase first-contact resolution, which is a priority for 89% of marketing leaders.
AI can help guide customers through self-service options and resolve the simpler issues that require following a basic step-by-step process. A well-executed self-service strategy can help improve customer service significantly. But if gone wrong, it can be equally detrimental.
“Businesses overlook the silent detriment of self-service—the erosion of customer
trust. Every time a customer encounters a self-service dead-end, it’s not just
a moment of frustration; it’s a micro-fracture in the relationship,” says Dennis Wakabayashi, Global Voice of CX, explaining how this isn’t just about solving problems, but about how customers feel during the process.
“People don’t just want solutions; they want to be heard and valued,” adds Wakabayashi. Leveraging AI for guiding customers through self-service can make the process efficient and reduce the chances of failure.
There also is a gap in using AI for self-service; only 43% of marketing leaders have adopted self-service technologies—there is a clear opportunity for businesses to empower customers to resolve issues independently, reducing the load on customer service agents.
These five use cases aren’t just futuristic concepts—they’re here, they’re proven, and they’re reshaping the way customer service teams operate. What’s next is a long-term plan to scale these functions.
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