AI in customer service: How to use it effectively

Learn how AI is impacting customer service, real-time benefits, challenges, best practices and a great case study.

Issac Thomas
July 12, 2023
9 min read

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Table of Contents

What is AI in customer service?   

AI (Artificial Intelligence) in customer service refers to the use of AI to enhance and improve customer experience. Organizations leverage AI systems to automate and augment various customer service tasks and interactions by deploying technologies like: 

  • Chatbots 

  • Sentiment analysis 

  • Predictive analytics 

  • Natural language processing (NLP) 

  • And other AI capabilities 

The adoption of AI in customer service is rapidly expanding. Businesses have realized the power and potential of AI when it comes to streamlining their support processes, reducing costs and enhancing customer satisfaction. 

The global AI in customer service market is projected to reach a value of $1345.2 billion by 2030. 

Why do you need AI in customer service?  

 AI-powered customer service is the way forward for savvy businesses. Let us take a closer look at all the top business benefits.  

Reduce your average handling time 

AI-powered customer service tools like chatbots and virtual assistants can improve your average handling time and other customer service metrics in a big way.  

Plus, there are AI-powered agent assistance tools that empower your agents to reply quickly by means of suggested responses, content recommendations and automated workflows – leading to better customer satisfaction and retention.  

Interesting read: 3 bolts of wisdom for lightning-fast customer response times 

Manage huge data volumes 

With AI, it is possible to process enormous customer data intelligently, with minimal human errors. AI can quickly gather and organize data across multiple touchpoints and can centralize all information regarding each customer and their interactions. Businesses can then offer a personalized customer experience powered by an omnichannel approach.  

Accurate prediction of customer behavior 

Since AI is trained on historical data, it enables agents to make predictions about the customer's future actions or preferences. AI monitors fluctuations in customer sentiment and guides agents with content recommendations and the next best steps, allowing them to control the conversation more effectively. 

Also read: What is sentiment analysis? Definition, Tools & Benefits  

Cost savings 

With AI algorithms segregating and prioritizing engageable messages, your resources are not wasted on responding to spam. AI can differentiate between messages that warrant a response and those that can be classified as noise. 

Filter out noise from channels using our super-power AI engine

Top 4 ways of using AI in customer service  

AI has the potential to improve customer service in many ways. Combining AI's efficiency with human agents' empathy and problem-solving skills can result in a more comprehensive customer experience and give real-time benefits in four main ways. 

1. AI-augmented human customer service 

By leveraging AI in conjunction with human agents, businesses can create a seamless transition towards a more optimized customer service system. With AI to handle simple tasks like case management and call routing, human agents are free to focus on their core strengths, which results in higher agent productivity and morale. 

AI can augment customer service in several ways. Here are a few of them.  

  • AI can maintain context. AI systems provide agents with context by retrieving customer information quickly. For example: they can scour your CRM, sales and marketing records to quickly retrieve information about previous customer interactions or purchase histories so agents can pick up the conversation from where it was left. 

    Pro tip: For best results, use omnichannel chatbots that can recall customer information from all the channels a customer has used during their brand journey. This way, your customer need not repeat their story every time, which goes a long way in enhancing their brand experience.   

    Provide conversational self-service with omnichannel bots.

  • AI can handle FAQs or routine queries independently. By redirecting routine questions to FAQ chatbots and other self-serve tools, AI reduces your call inflow drastically, which lightens your agents’ caseload.  

  • AI can assist with intelligent routing. With AI at your support front end, customer queries are filtered and routed to the agent/team with the right skill set to solve them efficiently. AI also assesses your agents’ availability and workload before assigning them cases – giving your overworked support teams a welcome respite. 

  • AI can give timely recommendations. An AI-powered knowledge base can serve your agents with the right content and product recommendations during calls. This allows agents instant access to relevant information such as product details or troubleshooting steps.  

2. AI-augmented messaging 

AI-augmented messaging approach acknowledges that generalized bots may not address all customer inquiries adequately, but they can identify opportunities where human agents can step in and elevate the support experience. Studies reveal that half of all customer service interactions are suitable for AI-powered bots. To optimize customer support, a system has been developed where bots and human agents collaborate seamlessly.  This is how it can work: 

  • Bots handle basic queries, and as conversations become more complex, they intelligently transfer interactions to human agents.  

  • Human agents then address intricate issues and, if necessary, delegate the remaining simple tasks back to the bots.  

With this strategy, you maximize the utilization of human agents' expertise for nuanced tasks while minimizing their involvement in routine inquiries that can be efficiently handled by bots. Plus, your customers are always assured of human touch whenever they seek it. 

Point to note: One notable benefit of this approach is the ability for a single agent to manage multiple interactions simultaneously, thanks to the asynchronous nature of chatbot-driven conversations.

A human agent taking over a customer support AI conversation.

3. AI-organized email inquiries  

Managing and comprehending every email received by a company can be a time-consuming task when relying solely on human effort. To expedite this process, companies are leveraging AI technology in the following ways.  

  • AI-powered tools scan and tag emails, ensuring they are directed to the appropriate department for timely resolutions.

    Get real ROI from email customer support

  • AI can auto-suggest responses that have appealed to the customer in the past. 

  • Last, it can clean inboxes of spam and promotional messages that take your agents away from answering critical messages. 

All in all, AI-powered email tools offer the capability to expedite your email customer support, delighting customers who prefer email to interact with your brand. 

4. AI-enhanced customer phone calls 

AI is harder to deploy for voice-based communications. 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.  

Then why bother? 

Because call centers are still the go-to choice for customer support. When it comes to complex financial and technical questions. Customers show a three-to-one preference for phone calls in such scenarios. Despite these challenges, some companies have successfully utilized AI to enhance the capabilities of their call center representatives by leveraging speech analytics and other call center technologies.

Generic model of an automatic speech recognition (ASR) system.
  • The process begins with capturing the user's spoken input, where automated speech recognition (ASR) technology filters out irrelevant sounds and focuses on demystifying the customer's intent. To ensure clarity, background noise is removed using neural networks that can differentiate between the message and ambient sounds. 

  • Once the input is captured, AI-driven voice bots process the information to find a logical response. They utilize trained NLP and natural language understanding (NLU) models to identify the meaning behind the message, taking into account user intent, sentiment analysis and industry-specific use cases. 

  • After narrowing down potential responses, the voice bot evaluates and filters the options to select the most accurate and objective solution for the customer's inquiry. The chosen response is converted into an audio format using a text-to-speech system, allowing the voice bot to provide the answer audibly.  

What are the challenges and limitations of using AI in customer service? 

AI can handle simple queries efficiently, but when the complexity of a query increases, human intervention is important. AI, though much advanced in 2023, still has a lot of challenges and limitations. Let’s discuss them in detail. 

😐Limited contextual understanding 
AI systems may struggle to fully comprehend complex or nuanced customer inquiries, especially those involving sarcasm, irony or ambiguous language. Understanding context accurately can be challenging for AI algorithms, leading to potential misinterpretations and incorrect responses. 

Recommended reading: Importance of speech analytics in call centers 

🤔Lack of emotional intelligence 
AI often falls short in recognizing and appropriately responding to human emotions during customer interactions. Empathy and emotional support are crucial in customer service, and AI may struggle to provide the level of understanding and compassion that human agents can offer. 

Also read: How to preserve the human touch in customer service 

🌐Data privacy and security 
Effective AI in customer service relies on the availability of large amounts of customer data. However, handling sensitive customer information raises concerns about data privacy and security. Organizations must ensure they adhere to GDPR and CCPA to protect customer data from unauthorized access or breaches. 

🚵Training and maintenance 
AI systems require continuous training and fine-tuning to remain accurate and up-to-date. This process involves significant effort and resources, including labeling data, retraining models and addressing biases. Regular maintenance and monitoring are essential to ensure optimal performance and mitigate potential issues. 

≠ Integration challenges 
Integrating AI into existing customer service infrastructure and workflows can be complex. Legacy systems may not be compatible with AI technologies, requiring additional investments in infrastructure or significant modifications to existing processes. Seamless integration and smooth transitions are necessary for effective AI implementation. 

How to get started with AI in customer service?  

Encompassing technologies like Machine Learning and Computer Vision, AI allows you to create and train models. By importing and labeling data, you teach machines to understand it. In the realm of customer service, we often refer to natural language processing within machine learning. 

When it comes to applying AI in customer support, the data being processed can be categorized as structured, unstructured or semi-structured. 

  • Structured data, well-organized and quantitative, can be easily analyzed by data analytics software. Examples include NPS scores, analytics information and closed-ended questionnaires. 

  • Unstructured data lacks a predetermined framework and includes audio, video, photos, and text like open-ended responses and online reviews. 

  • Semi-structured data lies between the two categories, offering a flexible organizing principle. For instance, messages from customers in a CRM tool may have structured elements related to specific processes or features but contain unstructured content. 

Your AI model is as good as the data you feed it. It follows the age-old proverb, garbage in, garbage out. So, the first thing you need to do is structure your data. Then categorize it using labels and train your AI model with your existing data. Also, remember to leverage the power of human review in evaluating your model’s performance. Lastly, when your model is ready, integrate it with your workflows to ensure smooth data flow and streamlined processing. 

Real-world examples of AI-powered customer service  

Artificial intelligence (AI) is being leveraged by organizations to transform their customer service and the way they interact with customers. From chatbots to virtual agents, there are a plethora of options brands are using to complement the human agent. Let’s dive into some real-world examples. 

Amazon Alexa: Processes voice commands with AI 
Amazon's voice assistant, Alexa, utilizes AI and natural language processing to provide customer support and perform tasks. Customers can ask Alexa for product information, track orders, play music, control smart home devices and more, all through voice commands. 

Uber: Shortens response time with AI 
Uber utilizes AI algorithms to handle customer support inquiries and resolve issues. Its AI system can automatically analyze and categorize customer messages, allowing for faster response times and efficient resolution of common queries. 

Read the full story here 

Sephora: Be your own makeup artist 
Sephora, a cosmetics retailer, developed the Virtual Artist app powered by AI and augmented reality. Customers can virtually try on makeup products, get personalized recommendations and receive beauty tutorials, enhancing the customer shopping experience. 

Future of AI in customer service and the impact on customer experience   

The future of AI in customer service holds immense potential for transforming the customer experience. Here are some key trends in AI customer support to expect in coming years and their potential impacts:  

Generative AI tools will disrupt customer support

AI-powered tools will find a place in customer service departments to prioritize the voice of the customer, allowing businesses to gather feedback and automatically analyze it. Through generative AI, customer feedback is processed, providing valuable insights such as overall sentiment and top pain points experienced by customers. This information is conveniently delivered to the inbox, enabling support agents to address customer concerns swiftly. 

Human touch will remain vital

In the age of AI advancements, it is easy to envision a future where customer support is solely automated. However, the reality is that the human touch will remain vital in customer support. There are fundamental qualities and skills that humans possess, which cannot be replicated by AI alone.  One crucial aspect is customer empathy. Human agents have the innate ability to understand and connect with customers on an emotional level, especially during challenging situations, to create a personalized experience that goes beyond mere transactional interactions. 

Also read: How to show empathy in customer service  

AI tools will be armed with emotional intelligence

AI systems are evolving to better understand and respond to human emotions. Emotionally intelligent AI will be able to detect customer sentiment, gauge their emotions and respond empathetically. This capability will enable AI systems to provide more compassionate and personalized support, fostering stronger emotional connections with customers. 

Need an example?

Sprinklr AI can uncover hidden emotions through language analysis. It delves into email and chat messages, detecting negative emotions like anger or sadness. By leveraging it, leaders gain valuable insights into their team members' emotional states and can respond more effectively. 

Final thoughts 

Consumers are changing the way businesses engage with them. They're moving their conversations online, and they expect digital support. If you're in the customer service business, that means you need AI. Sprinklr Service combines specialized AI models with OpenAI's Generative AI capabilities to enhance productivity, decision-making and customer experiences for customer-facing teams. It utilizes a unified AI layer to streamline data processing across channels, to help you: 

  • Maintain consistent cross-channel experiences 

  • Foster collaboration across teams 

  • Empower agents with AI assist tools  

  • Improve response speed with AI-powered Conversational Bots 

  • Effortlessly build and maintain your Knowledge Base  

Impressive, right? 

There’s a lot more to AI-led customer service than what meets the eye. From use cases to tools, there’s some new advancement in the domain every day. To keep you abreast, we’ve compiled a comprehensive (and free) e-book on the topic for you to download. Grab your copy now! 

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