According to a frequently cited statistic from about 10 years ago, people spend an average of about 43 days on hold during their lifetimes. Not coincidentally, at around that same time, experts began to sound the death knell for brand loyalty — a state of affairs that continues to this day. A 2022 survey further confirmed the tenuous nature of the relationship between customer service and loyalty, finding that 43% of customers switched products or canceled a contract due to poor customer service.

But perhaps this tide is ready to turn. Recently, companies have begun to ride the crest of an almost dizzying wave of advancements in artificial intelligence (AI) technologies that could potentially redefine the customer service landscape. With the rise of generative AI and large language models, in particular, companies have been putting AI-driven chatbots, virtual assistants and predictive analytics to use to dramatically reduce customer response times and build personalized interactions that slash operational costs while also improving overall customer satisfaction and brand loyalty.

Of course, companies should not simply throw technology at customer service problems. AI solutions are ultimately only as good as the strategies that underlie their use. This article delves into the use of AI in customer service, its benefits and challenges, and how to build an implementation strategy that delivers a meaningful return on investment (ROI).

What Is AI for Customer Service?

In the past two years alone, AI has changed the way companies respond to customers across multiple channels, including email, phone, the web and social media. With AI, painfully long wait times and frustrating phone menus could soon become a thing of the past. Customers will be able to get the help they need instantly, across various platforms, driven by the speed, efficiency and personalization capabilities provided by AI technologies.

This recent rush of technologies has created a new customer service landscape brimming with possibilities. Among the most impactful AI technologies is natural language processing (NLP), which, when combined with generative AI, allows chatbots and virtual assistants to scour vast amounts of data to understand and respond to customer questions in real time, with humanlike accuracy and nuance. The result is a level of personalized, instant support that was previously unimaginable. In addition, machine learning (ML) algorithms can now power predictive analytics tools, allowing companies to store and process large datasets to anticipate customer needs and develop personalized interactions that drive engagement and brand loyalty. AI-driven sentiment analysis is another game-changer, helping companies better gauge the pulse of customer thoughts and opinions by analyzing customer feedback and interactions. As a result, businesses can adjust their responses to build stronger customer relationships and attain higher levels of satisfaction.

Beyond the benefits for customers, AI’s efficiency also reduces costs for companies by automating routine tasks, such as handling questions and processing transactions. This means that companies can provide better customer service without raising head count.

Key Takeaways

  • Recent advancements in AI could reverse a prolonged decline in brand loyalty.
  • NLP, generative AI, machine learning and predictive analytics tools are among the game-changing AI technologies fueling innovation in customer service.
  • AI technologies are helping companies become more efficient, lower costs and increase customer satisfaction by automating routine tasks and providing deep analyses of large volumes of customer data.
  • Using AI advantageously for customer service requires technical skills, particularly in integrating AI tools with existing systems.
  • Before embarking on an AI strategy, companies should first evaluate their business needs to make smart technology choices. Other considerations include data management and integration challenges.

AI for Customer Service Explained

The journey of AI in customer service has been one of continued evolution, but the past several years have seen the speed of AI innovation grow exponentially. Initially, in the late 1990s and early 2000s, AI applications were limited to basic chatbots with predefined responses, offering rudimentary support. However, as technology advanced, so did AI’s capabilities. By the early 2010s, NLP and ML technologies became more sophisticated, with chatbots able to respond to customer inquiries in a much more nuanced and humanlike manner. These systems can analyze vast amounts of data in real time, providing personalized recommendations and solutions tailored to each customer’s needs.

In the past decade, advancements have been even more rapid, with breakthroughs in deep-learning algorithms and big data that are paving the way for AI systems that can analyze large amounts of data in real time and provide personalized support. Looking ahead, the future of AI in customer service portends even greater innovation. Advancements in ML and deep-learning algorithms should allow AI systems to respond to customer inquiries and anticipate needs before they arise. Predictive analytics will only accelerate businesses’ ability to offer proactive support. Integrating AI with other emerging technologies, such as augmented reality and virtual reality, is another area with tremendous promise. Imagine a customer service experience where users interact with virtual agents in a simulated environment to easily troubleshoot complex issues.

How AI is Transforming Customer Service

Experts will likely look back on this moment in time as a golden age of customer service, with AI redefining how businesses connect with customers. With cutting-edge AI technologies at their disposal, companies can now provide far better and faster customer service without spending more money. Here are the top ways AI is making that possible.

  • Automation of Routine Tasks

    AI’s ability to automate tasks is at the heart of many of its benefits, allowing customer service agents to shift their focus from mundane responsibilities, such as addressing simple inquiries, processing transactions and providing basic support, to more complex and strategic tasks. In particular, the impact of NLP on customer service has been profound, with systems now able to comprehend and respond to customer questions in real time. NLP-powered chatbots and customer portals can engage customers in natural conversations that, for example, provide answers to frequently asked questions without human intervention. ML algorithms can also analyze massive amounts of data, including lessons from past interactions, to quickly identify patterns and trends that it can use to adapt responses on the fly.

  • Enhancing Customer Interactions

    There’s no doubt that personalized customer interactions encourage better engagement and lead to greater satisfaction. One way AI supports personalization is by creating more self-service options for customers. Intuitive chatbots and virtual assistants can now use AI to guide customers through steps to troubleshoot an issue, based on their feedback, thereby reducing wait times and helping customers resolve problems on their own. AI-powered sentiment analysis tools can help companies gauge customer emotions and tailor responses accordingly, as well as detect patterns and identify potential issues before they become larger problems. In addition, using data analytics and ML algorithms, AI can analyze customer behavior and preferences to deliver tailored recommendations and offers. For example, an ecommerce platform can use AI to suggest products based on a customer’s browsing history and purchase patterns.

  • Data Analysis and Insights

    While automation is a key part of AI technologies, the speed and sheer volume of data analysis AI provides has put unprecedented knowledge at the fingertips of customer service agents, who can deliver far better service, far faster, than ever before possible. Sentiment analysis, for example, examining customer feedback, social media posts and past interactions to gauge customer attitudes and identify trends. ML algorithms sift through large datasets in real time to identify emerging issues, sentiment shifts and areas for improvement. Predictive analytics allow companies to anticipate customers’ needs and behavior based on historical data and patterns. For example, AI algorithms can predict customer churn by analyzing purchase history, engagement metrics and sentiment scores. Armed with this knowledge, companies can take proactive measures, such as personalized incentives or targeted marketing campaigns, to keep customers from defecting.

Benefits of AI in Customer Service

One of the reasons spending on AI technologies is exploding — global retail spend on chatbots alone is forecast to reach $72 billion by 2028 — is that the benefits are so tantalizing. With AI, the concept of “doing more with less” has never seemed so attainable. Here are the most powerful benefits of AI in customer service.

  • Increased efficiency and speed: AI technologies can handle multiple customer inquiries at once, providing instantaneous or nearly instantaneous responses. Chatbots, for example, can quickly analyze customer messages, provide relevant information and even resolve common issues without human intervention or associated wait times.

  • Cost reduction: AI technologies and other automation capabilities make it possible for companies to do more without having to add any customer service resources. Going back to our chatbots, by handling a significant volume of inquiries at once, these tools allow human agents to focus on more complex, value-added tasks. AI can also optimize workflows to improve efficiency and reduce operational expenses.

  • Improved customer satisfaction: AI technologies can enhance customer satisfaction by providing tailored experiences. For example, sentiment analysis can probe customer feedback to detect indications that they are unhappy and then address those issues immediately. In addition, personalized recommendation engines can boost engagement by using customer data, like browsing history, to suggest products or services related to viewing behavior.

Challenges of AI in Customer Service

Despite the potential for transformative benefits, AI strategies for handling customer service require meticulous planning to succeed. Consider these potential hurdles:

  • Technical challenges: AI in customer service often involves data integration, sophisticated algorithms and scalable systems, any of which can pose complexities. For example, integrating data from disparate sources, such as customer relationship management (CRM) systems, social media platforms and website analytics tools, into an AI system mandates precise data management processes to ensure data quality and consistency. AI algorithms also require continuous monitoring, feedback loops and retraining on datasets to understand and respond to customer inquiries accurately. Finally, an AI system must be able to scale to handle increasing service volume, requiring flexible infrastructures and periodic system assessments.

  • Ethical and privacy concerns: AI in customer service can also create challenges related to data security, algorithmic bias and customer consent. The need to protect customer data from unauthorized access is well documented, and it’s particularly relevant given the large volumes of sensitive data required for AI processing. In addition, because AI algorithms rely on historical data, they can sometimes reflect societal biases that can affect certain groups unfairly. To combat this, companies should use diverse datasets and bias detection tools and conduct regular audits. Finally, companies must be sure they’ve obtained proper customer consent to use data for AI-driven customer interactions.

  • Resistance to change: Anytime new technologies are introduced, employees may fear for their jobs, making them hesitant to adapt. That’s a particular concern with AI in customer service, where much has been written about the technology’s potential to replace human interaction. Companies can overcome this resistance to change by fostering an open and transparent culture that offers comprehensive training and support for employees making the transition to AI-driven processes. It’s also important to highlight AI’s strengths in enhancing roles rather than replacing them.

7 Examples of AI in Customer Service

Although some AI technologies, like generative AI, are relatively new, AI has been used in customer service for decades. For example, interactive voice response systems that automatically route customer service calls have been around since the 1980s. The following list depicts some of today’s most common — and powerful — examples of AI in customer service.

  1. Chatbots and Virtual Assistants

    In the hospitality industry, a hotel chain might use chatbots and virtual assistants to improve its customer experience. For example, using a chatbot that’s integrated into a mobile app, guests could request room service, housekeeping or local restaurant recommendations. In addition, voice-activated virtual assistants installed in the rooms could provide instant responses to common guest questions, such as Wi-Fi passwords or checkout procedures, that historically have required a call to the front desk. The hotel can use data from the chatbots and assistants, along with any other data on guest preferences and feedback, to improve future interactions.

  2. Automated Customer Support

    Automated customer support can dramatically simplify workflows for service inquiries. In the telecommunications industry, for example, when a customer calls a provider with a technical issue, an AI-powered system could use NLP to understand and quickly diagnose the problem. It can then provide preprogrammed solutions, such as troubleshooting steps or account adjustments, significantly reducing call resolution times. The kind of immediate assistance and consistent responses of automated support workflows contrasts sharply with aid offered by traditional call centers, where customers often encounter long wait times. Automated support can also be based on each customer’s specific needs to personalize responses.

  3. Email Automation

    Whereas traditional email management systems are often manual and time-consuming to use, email automation, driven by AI algorithms, can help companies automatically categorize and respond to basic requests. In the banking industry, categories could be set up for balance inquiries, transaction disputes or account changes. This convenience reduces response times and provides consistency and efficiency when handling customers’ needs, ultimately driving greater customer satisfaction and improving overall service quality by reducing human error.

  4. Personalization Engines

    Personalization engines are vital in customer service because they foster deeper customer connections that, in turn, build loyalty. Imagine a luxury hotel that uses an AI-powered personalization engine to tailor guest experiences by analyzing their past preferences and interactions, such as room temperature, preferred amenities, dining choices and leisure activities. Using ML algorithms, NLP and predictive analytics, hotels can anticipate guests’ needs and offer personalized recommendations before guests even think of them. The hotel also benefits from greater efficiency from simpler workflows, less manual intervention and better resource allocation.

  5. Voice-Activated Customer Service

    Voice-activated customer service tools offer a variation on the automated customer support tools described above. Automated customer support systems often rely on keypad inputs or prerecorded menu options, while voice-activated customer service offers a more intuitive and conversational experience across various touchpoints, including phone calls, chatbots and smart speakers. In the telecommunications industry, for example, a customer might call their provider to inquire about their bill. The voice assistant can use NLP to decipher the customer’s questions, such as “What’s my current data usage?” or “Why is my internet connection slow?” The assistant then quickly retrieves relevant information from a database in real time to provide accurate responses.

  6. Predictive Customer Service

    Predictive customer service leverages AI algorithms to anticipate customer issues before they arise. In the retail industry, for example, predictive analytics software can forecast demand patterns so companies can stock enough products to avoid stockouts, reducing customer frustration and creating sales opportunities. Combining ML algorithms with data analytics tools and CRM systems, companies can analyze historical data, customer interactions and market trends to generate better product recommendations or optimize service. Predictive analytics tools also help companies allocate resources more efficiently to reduce costs.

  7. Sentiment Analysis

    Superior customer service is rooted in understanding and addressing customers’ emotions. That’s where sentiment analysis comes in. In the hospitality sector, for example, analyzing guest feedback can unearth attitudes — both positive and negative — regarding service quality, amenities and overall experience, which can help hotels evaluate what’s working, rework what’s not and elevate the overall customer experience. NLP algorithms and ML techniques in sentiment analysis tools make this process much faster than manual methods by automatically processing and classifying customer feedback. As a result, companies can use data-driven decisions to proactively improve their products or services.

Implementation Considerations for AI in Customer Service

Any experienced business professional, IT or not, understands that a technology implementation without proper planning is a recipe for disaster. When it comes to customer service AI, the following steps can help ensure that the right technology is deployed for the right mission.

  • Understand Business Needs

    The first step in building an AI strategy for customer service is to identify the specific pain points and challenges a company faces in delivering exceptional customer service. For example, a retail company may find that a long hold time when responding to customer questions destroys retention rates. But implementing AI chatbots without first addressing the fundamental reasons for those wait times, such as insufficient staffing, inefficient call routing or outdated customer service processes, may fail to assuage customer frustration.

    Understanding business needs requires a multifaceted approach. This includes conducting stakeholder interviews with customer service representatives, managers and executives to distill their perspectives on root causes and desired outcomes. Analyzing existing customer service data, like call logs and chat transcripts, can also provide insights, as can collaboration with frontline staff, who often possess firsthand knowledge of customer behaviors and can offer valuable input about the challenges and opportunities for improvement.

  • Choose the Right Technology

    AI is all about technology, but it’s important to recognize that technological choices must align with the business needs outlined in the previous step. Failure to select the appropriate technology can lead to wasting resources on solutions that don’t address pain points, not to mention potential data security breaches or regulatory noncompliance issues.

    In broad terms, companies should look for AI solutions that offer the scalability to handle future growth and that are equipped with customization and personalization capabilities that can be adapted to specific industry or regulatory needs. Seamless integration with CRMs, help-desk solutions and communication tools is also paramount (more on this soon), as are real-time analytics and security capabilities. As with business needs assessment, relevant stakeholders should be involved in the decision-making process, including customer service managers, IT professionals and legal advisers for security and compliance concerns.

  • Prioritize Data Management

    When companies can’t ensure the accuracy, completeness and integrity of the data used to fuel their AI models, the risks are considerable, ranging from inaccurate conclusions and predictions to a poor overall customer experience. Lax data management practices, such as deficient encryption, weak access controls or improper data storage, can also expose customer data to unauthorized access, breaches or compliance violations.

    Rigorous data management should start with a comprehensive data audit to assess the quality, relevance and accessibility of existing data sources. This should include identifying data gaps, inconsistencies and redundancies, and evaluation of data governance policies and compliance requirements. In addition, comprehensive data infrastructure, such as data storage, processing and analytics capabilities, represents an important investment toward supporting data integrity, confidentiality and availability throughout the AI life cycle. Appointing data stewards and custodians to oversee governance can also help enforce data policies and address data-related issues proactively. Finally, consider emerging technologies, such as blockchain for secure data transactions and federated learning for privacy-preserving AI model training.

  • Ensure Integration With Existing Systems

    Customer service teams often rely on multiple systems to deliver top-quality service, including CRM platforms, ticketing systems, knowledge bases, communication channels and analytics tools. If those systems remain siloed, companies will likely experience redundant efforts, inefficient workflows and disjointed customer experiences.

    After assessing current infrastructure and identifying integration points and dependencies, companies should prioritize the compatibility, scalability and flexibility of AI solutions and integration tools. With clear integration objectives and requirements in place, data mapping, transformation rules and API specifications for data exchange and interoperability among systems can be outlined. Middleware, application programming interfaces (APIs) and integration platforms can help with data synchronization, workflow automation and real-time communication between AI-driven customer service applications and existing systems. Industry standards and best practices, such as RESTful APIs, webhooks and event-driven architectures, that connect systems can also simplify the complexity of integration and maintain scalability and agility. As always, data security, privacy and compliance concerns, such as encryption, access controls and auditing, are paramount.

  • Consider User Experience

    How customers receive responses to their inquiries is just as important as the specific answers they receive. The design of AI user interfaces (UI), the clarity of communication between AI systems and customers, the ease of navigating AI-powered self-service options and the level of personalization offered by AI algorithms often spell the difference between customer bliss and frustration, confusion and dissatisfaction.

    To prioritize user experience (UX), companies should involve UX/UI designers and conduct thorough usability testing to identify pain points and refine AI-driven interactions. Feedback from customers throughout the implementation process can also provide valuable insights for improvement. Additional tips: Use NLP technology to build AI interactions that mimic human conversation, integrate sentiment analysis to gauge customer emotions and tailor responses, and implement proactive measures to anticipate customer needs and provide preemptive support.

  • Perform Testing and Evaluation

    Rigorous testing helps companies identify and resolve issues before they deploy AI systems for customer service. Without proper testing, AI systems may produce inaccurate responses, misinterpret customer inquiries or fail to adequately address customer needs.

    A company’s first step should be to define clear testing objectives. Next, relevant testing scenarios that mimic real-world interactions and use diverse datasets to train AI models can be selected. It’s also important to establish comprehensive testing criteria, including performance metrics for accuracy, response time and error rates. Conducting stress-testing to assess AI system performance under peak load conditions can help companies identify potential bottlenecks or performance issues that could arise during periods of high customer demand. This also makes it possible to optimize system scalability to maintain seamless customer interactions. In addition, feedback loops should be incorporated to systematically improve AI algorithms based on real-time data. Finally, user acceptance testing can help gather end-user insights to refine AI interactions.

  • Train Staff

    For AI-driven customer service to succeed, employees must understand how the tools work, what they can and can’t do, and how to use them to meet their full potential. Without proper training, AI tools could remain unused or underemployed, decreasing productivity and sabotaging customer satisfaction strategies. An AI training program should educate teams about new software interfaces, data management practices and customer service strategies that are tailored to AI-driven workflows.

    After conducting initial AI awareness sessions to familiarize employees with AI concepts, companies should provide hands-on training with AI software and tools, and offer ongoing support and feedback to reinforce learning and address any questions. Indeed, an AI training program should be part of a continuous learning and experimentation culture, where employees are encouraged to explore AI features independently and companies recognize and reward early adopters to inspire more engagement and enthusiasm.

  • Balance Cost Implications

    AI tools aren’t free, so it’s important to carefully consider various costs before and after implementation, including initial setup expenses, ongoing maintenance fees and potential training and integration costs. Disciplined cost control can prevent budget overruns, inefficient resource allocation and diminishing returns on AI investments.

    A thorough cost-benefit analysis can help evaluate the financial impact of AI adoption, including identifying cost-saving opportunities from process optimization and automation and taking advantage of flexible pricing models to manage expenses. Cloud-based AI software can offer significant cost benefits by providing scalable solutions on a pay-as-you-go basis, reducing the need for large upfront capital investments. Prioritizing transparency and communication with regard to cost implications can also help align departmental expectations and highlight accountability. In addition, companies may want to consider open-source or community-driven AI solutions to help reduce up-front costs, as well as the opportunity to implement scalable AI infrastructure that can handle future growth without significant additional investment.

Improve Your Customer Service Experience With AI and NetSuite

NetSuite Customer Service Management is an intelligent, AI-powered solution for seamless customer interactions, providing personalized recommendations, automated responses and predictive insights into customers’ needs. Because it’s integrated with NetSuite’s comprehensive cloud-based ecosystem for sales, marketing and service channels, it can mine large, companywide datasets in real time to help businesses tailor their service offerings and anticipate customer preferences proactively.

NetSuite Customer Service Management also integrates with the company’s SuiteCommerce module, which uses AI to optimize ecommerce experiences. ML algorithms deliver personalized product recommendations and simplify the purchasing process for customers. And NetSuite’s AI-driven chatbots deliver around-the-clock support to resolve issues immediately and escalate more complex issues to human agents as necessary.

NetSuite’s comprehensive suite of scalable and flexible solutions offers a holistic understanding of customer interactions, which in turn drives more informed decision-making and targeted engagement strategies.

While it might be tempting to view AI technologies as a miracle cure for all the challenges customer service organizations have faced over the years, it’s crucial for companies to take a breath and first focus on developing thoughtful strategies that match these innovations with their specific business needs. By following the steps outlined here, companies can better align their expectations with realistic outcomes and pave the way for a more efficient and customer-centric future.

AI for Customer Service FAQs

How is AI used in customer service?

In customer service, artificial intelligence (AI) automates repetitive tasks, such as answering common questions through chatbots, to free up human agents so they can handle more complex issues. It also analyzes customer data to personalize interactions and offer tailored recommendations and support. AI powers sentiment analysis, too, to gauge customer emotions and prioritize and address concerns quickly and efficiently. AI-driven predictive analytics tools can also anticipate customer needs so companies can proactively resolve problems.

Is AI replacing customer service?

No, artificial intelligence (AI) isn’t replacing customer service; it’s augmenting it. While AI automates routine tasks and improves efficiency, human agents are still needed to handle complex issues and provide personalized support. AI complements human engagement by simplifying processes and improving customers’ experiences, but it can’t completely replace the empathy and understanding humans can offer. In fact, the combination of AI and human agents often leads to more effective, positive customer service outcomes.

What is the future of customer service with AI?

In the future, artificial intelligence (AI) holds promise for even more personalized and efficient customer service interactions. It’ll continue to evolve to help businesses anticipate customer needs, provide proactive support and deliver hyper-personalized experiences. In particular, advancements in natural language processing and machine learning should make AI-driven customer interactions more humanlike. AI could also combine with other technologies, like virtual reality (VR), to redefine customer service. For example, AI systems could integrate with VR environments to allow customers to interact with lifelike AI avatars for highly immersive and personalized support experiences.

Will AI replace customer success?

In a nutshell, it’s not likely. Customer success requires human empathy and intuition to secure strong customer relationships — two traits that aren’t yet highly developed in today’s artificial intelligence (AI) solutions. As in other cases, AI will likely augment successful customer interactions by providing valuable insights, automating repetitive tasks and improving efficiency.

What companies are using AI to improve customer service?

While many leading companies use AI to enhance customer service, retail powerhouses like Walmart and Target, for example, have been at the forefront of solutions, using AI-powered chatbots for answering customers’ questions and offering personalized recommendations. Financial institutions like Bank of America and JPMorgan Chase have also been leaders in using AI for fraud detection and personalized financial advice.

What is the customer support system in AI?

The customer support system in AI includes several technologies and tools intended to enhance customer service experiences. These often include chatbots, virtual assistants, sentiment analysis algorithms and predictive analytics. These AI-driven solutions automate repetitive tasks, provide quick responses to customer questions, analyze customer feedback to improve service quality, and predict customer needs or issues before they arise. The overall goal of customer support systems in AI is to simplify operations, increase efficiency and deliver more personalized customer support.