Modern customers expect quick, intelligent, and personalized service across all touchpoints—be it phone, web, or mobile. Even small delays or miscommunications can negatively impact customer satisfaction and damage your brand’s reputation. 

AI-powered customer service can help address this challenge by assisting human agents in handling frequent and simple questions. In fact, a 2023 Gartner study notes that 80% of companies are seeking to implement AI-powered chatbots to level up their customer service by 2025.

This blog post will explore the capabilities of AI in customer support, including benefits and use cases. You’ll also learn to implement AI in customer service to enhance customer experience.

What is AI in customer support?

AI in customer support uses technology such as ML, NLP and text analysis to manage customer interactions and optimize the customer experience.

The key idea is to address customer requests swiftly, streamline internal workflows and anticipate customer needs proactively.

Benefits of AI in customer support

A report by Deloitte reveals that 15% of contact centers use Gen AI to drive faster customer authentication, summarize pre-interaction journeys and provide automated call summaries and follow-up recommendations. This reduces human error, saves time, and enables management to make unbiased decisions.

Let’s understand how implementing AI in customer service can further help in boosting efficiency and enhancing customer customer experience:

Round-the-clock support

Unlike humans, chatbots and virtual assistants can operate 24/7 to provide consistent customer service. This means customers don’t have to wait for business hours to receive support. They can connect at any time of the day and have their issues solved, significantly improving the customer experience.

Enhanced customer support workflows

AI can streamline internal workflows in various ways, such as:

• Automate routine tasks
• Prioritize, categorize and route tickets to the appropriate agents based on urgency and complexity 
• Analyze customer history to provide tailored solutions

Data-driven insights

AI’s expertise lies in processing and analyzing large volumes of data. This helps businesses get in-depth insights into customer behavior and preferences.

Based on these insights, business leaders can make informed decisions about product development, marketing strategies and overall customer service improvements.

Reduced employee burnout

Since AI takes over repetitive and mundane tasks, agents have time to focus on more challenging and innovative tasks, such as handling complex queries and tailoring support experiences. This not only improves customer service job satisfaction but also leads to better-quality customer interactions. 

Personalized customer interactions

AI can provide tailored recommendations and solutions by analyzing customer behavior and data, leading to a more personalized support experience. Your team can tailor interactions before and after purchase to ensure customers receive the best experience.

Prediction of customer behavior

Using real-time data, businesses can use AI to determine customer behavior based on their purchase history, buying habits, and personal preferences. This helps you identify customer needs even before they say it and proactively reach out to serve customers in the best way possible.

Challenges of AI in customer support

While AI has several remarkable advantages, it is not free of challenges. Businesses must be aware of its challenges to have a well-balanced and holistic approach to using AI. This will help maximize the benefits of AI while minimizing associated risks.

Here are some common threats associated with AI:

Data privacy and security

Protecting customer data is essential to maintaining customer trust and security. Businesses must comply with data protection regulations like the General Data Protection Regulation (GDPR) to maintain high-security standards and protect sensitive customer information. They must also communicate transparently with customers about processing and protecting their data.

Implementation

Implementing AI customer service solutions requires huge investment in technology, training, and integration. To ensure alignment with existing processes, businesses must meticulously plan and execute the deployment of these systems.

Gartner emphasizes that aligning AI tools with current workflows requires careful analysis of existing systems. Their research shows that 58% of companies experience delays in AI deployments due to challenges with integration into legacy systems.

Additionally, AI depends on large volumes of data to process and analyze behaviors. Inconsistent or incomplete data can lead to inaccurate predictions, which can result in poor customer experiences. 

Inability to handle complex customer issues

Despite being advanced, agile and adaptive, AI still struggles with complex customer queries that require nuanced responses. This can frustrate customers who expect quick and personalized answers. 

To ensure comprehensive customer service, the precision and speed of AI should be combined with empathetic and compassionate responses from human agents.

Adaptability to changes

Businesses need to continuously update and train AI systems to keep them relevant. For example, a predictive analytics tool must be apprised of current market trends and customer behavior to forecast accurately. This need for flexibility can pose a threat, especially for rapidly changing industries.

Lack of emotional intelligence

Interacting with highly automated systems eliminates the human touch of care and empathy. This can make customers feel detached from the brand. Additionally, customers may get frustrated when they receive generic responses for sensitive or complex queries.

Ways to use AI in customer support 

Now, let’s understand some of the most effective ways companies are implementing AI in customer support:

Chatbots

Chatbots are built for real-time assistance to manage large volumes of messages while creating a human-like experience. It can handle basic customer queries, provide instant responses, and assist with tasks such as product recommendation, order tracking, and troubleshooting.

Omnichannel customer service platform Plivo CX's self-service chatbot, developed and powered by OpenAI, can support businesses to resolve customer queries quickly.

The chatbot is suitable for businesses of all sizes and has these stand-apart features:

• In-app customer service enables customers to send and receive photos and documents easily
• 24/7 availability to resolve  queries instantly, allowing customers to pause and resume conversation at their convenience
• Complete customer context and interaction history from multiple channels available in a single platform
• Can be easily updated with new product information, promotions or policy changes
• Summarizes and transfers complex queries to human agents with complete context
• Accurately reflects your brand identity while adhering to the company’s policies and guidelines 

Self-service resources

AI can synthesize existing information and create answers for popular customer inquiry topics. This can be used to develop self-service resources to help customers find answers independently. Some of the common self-service resources include:

• Knowledge bases featuring house articles, how-to-guides, troubleshooting tips, and more
• Frequently Asked Questions (FAQ) pages containing answers to customer’s most common queries
• Interactive Voice Response (IVR) to provide pre-recorded answers 

Sentiment analysis

Sentiment analysis can uncover insights from customer conversations across channels to tailor responses based on the client’s needs and emotions. It can monitor and analyze customer feedback, reviews, and interactions to identify areas of improvement, respond appropriately and provide personalized experiences.

Speech analysis

AI-powered speech analysis can provide insights into customer interactions by evaluating vocal tones, pitch, and speech patterns to detect emotions like frustration, anger, or satisfaction during a call.  This helps agents tailor their responses in real time and show empathy and care during critical situations.

Omnichannel routing

Customers are usually frustrated when they have to repeat their problem every time they switch channels. Omnichannel routing is the best way to ensure customers have a consistent experience across all channels. It provides a complete history of customer interactions so that reps can easily handle every communication with continued context.

As a true omnichannel contact center, Plivo CX features a unified agent desktop, allowing your team to manage interactions in every channel (live chat, voice calls, SMS, email, or WhatsApp) from a single platform, eliminating the need to switch between channels. This can help simplify the communication process and improve response times. 

Predictive analytics

Predictive analytics can analyze large volumes of data and draw actionable insights to forecast trends, anticipate customer needs, and solve future problems. This helps businesses reach out to customers proactively and solve possible problems. For example, if a shipment delay affects the delivery schedule, you can proactively communicate and even offer a discount coupon to appease the customer.

Automated call transcription

Call transcriptions are essential to review customer conversations, quality assurance and training, and maintain compliance with legal requirements. It makes it easier for teams to reference past conversations, particularly in cases of follow-ups or escalations and identify trends and recurring issues. 

Analyzing call transcriptions can also help businesses identify opportunities for upselling.

Routing and prioritization

AI in customer service can analyze incoming inquiries and automatically direct them to the most appropriate agents or teams. This is done by evaluating factors such as urgency, customer history, and issue complexity.

AI can also identify high-priority cases to address them promptly while managing routine inquiries efficiently. 

Real-time data and analytics

Automating customer service with AI can help you get granular insights into critical customer interaction metrics such as AHT, FCR, and CSAT.

Businesses can use this data to evaluate agent performance, find areas of improvement and tailor strategies accordingly.

Steps to implement AI in customer service

A report by IBM revealed that 42% of enterprise-scale organizations with over 1,000 employees actively use AI in their business, and 59% of enterprises already working with AI want to increase their investment in the technology. 

If you are yet to implement AI in customer service, follow these key steps:

1. Evaluate business needs

Evaluate your existing customer service processes to identify pain points, areas that require faster response times or staffing needs. This will help you pinpoint areas where AI can be most effective, providing a solid base for implementing AI in customer service.

2. Define goals and objectives

Based on your business needs and requirements, set clear and measurable goals. These might include goals like increasing first-contact resolution, reducing average response times or improving overall customer satisfaction scores. Specific goals help you pick the right AI tools and measure their impact effectively.

3. Choose the right AI software

Once you’ve sorted your needs and objectives, select a tool that aligns with your requirements. Consider scalability, integration capabilities, user reviews, pricing, long-term viability and support when evaluating different options.

4. Integrate with existing solutions

The tools you choose must work well with your existing current customer systems, which may include API integrations, data sharing, or other technical setups. Ensure to conduct pilot tests to identify and address any integration issues before full deployment. This will help you resolve potential problems early in the implementation process.

5. Train your employees 

Deliver in-depth training for your customer service representatives on operating and integrating the AI tools into their workflows. Focus on covering technical functionalities and how to partner with AI to provide the best customer experience. Offer ongoing training and guidance and encourage feedback on the new processes.

6. Ensure compliance and data privacy

Ensure the AI tools can securely handle and store customer data by maintaining strict compliance with data protection and privacy regulations. Also, regularly review and update your privacy policies and practices to align with evolving regulations. 

7. Monitor and optimize frequently

Lastly, continuously review the performance of your AI strategy. You can track performance by assessing relevant metrics such as response times, CSAT scores, and resolution rates. Based on data insights, make necessary changes and adjustments to improve your AI systems and processes to meet customer needs better.

Examples of AI in customer support 

From chatbots, personalized recommendation engines, to more innovative uses, let’s discuss how some brands are implementing AI in customer support to offer superior experiences: 

Encore

Encore uses a GPT-4 model to allow users to find and buy second-hand items. The idea is simple, you have to type your query and get results from multiple resources, including Poshmark, Grailed, Etsy, and eBay.

For example, if you are looking for outfits like Carrie Bradshaw, typing “Carrie Bradshaw” in the search bar will give you an AI summary of the fashion statement along with all possible product recommendations.

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Stitch Fix

Stitch Fix uses machine learning algorithms alongside human stylists to tailor recommendations for its 3.5 million customers. Their AI systems analyze customer preferences and broader fashion trends to refine suggestions over time.

Additionally, the platform utilizes generative AI, specifically GPT-3, to automate the creation of high-quality, branded product descriptions. This process enables the company to generate 10,000 descriptions in 30 minutes, helping scale their catalog management and improve search engine visibility. 

Shein

Shein uses AI to create rapid design by analyzing global fashion trends, with new styles developed in just 25 days. A smart design system streamlines production, while AI-powered personalization enhances customer experiences through tailored product recommendations.

It also uses AI to automate warehouse management and delivery scheduling. This strategic integration of AI has allowed Shein to scale globally, redefine fast fashion, and establish itself as a tech-driven leader in modern e-commerce.

Plivo CX: The future of AI in customer support 

Plivo CX is an all-in-one, cloud-based omnichannel platform designed to help businesses provide top-notch customer support across various channels. It is built to integrate effortlessly into your business and support mechanisms.

The platform also enables businesses to train OpenAI-powered chatbots using unique datasets, ensuring customer queries are answered with the most relevant and accurate information.

Some of its top features include:

• Omnichannel capabilities: Unify, track, and manage customer conversations across channels, including email, voice, SMS, WhatsApp, and live chat
  
  
• Visual workflow builder: Create custom workflows and use automations using a simple no-code drag-and-drop builder

• Integration: Integrate seamlessly with your existing tools, such as homegrown systems, ERP, CRM, and helpdesk tools

• Real-time analytics: Analyze real-time customer interactions to identify areas of improvement  and create targeted training for agents

• More productivity: Streamline agent workflows with message templates and internal notes to facilitate efficient conversation, transfers and task management
  
  
• Agent coaching tools: Train your support agents to monitor real-time performance with features such as call recording, barge, and whisper

• Security and compliance:  Offers enterprise-grade security features complying with SOC-2, GDPR, HIPAA, and PCI DSS. This ensures your customer data is safeguarded and your support processes align with industry regulations

‍Book a demo to learn more.