AI in customer service

AI-powered tools make service faster, more personalized and more efficient. AI assistants, chatbots, virtual agents and smart routing systems use natural language processing (NLP) and machine learning (ML) to understand what customers need. These tools work together to provide smoother, more responsive customer service experiences by responding in real time and continuously improving by learning from every interaction.

Globally, 62% of executives say that generative AI can disrupt how their organization designs experiences—and personalization is at the core of this evolution.¹ Generative AI for customer service allows companies to move beyond simple answers and deliver proactive suggestions, tailored recommendations and even solve customer issues before they happen.

AI tools can be integrated with customer relationship management (CRM) systems to help companies offer truly personalized support while reducing operational costs.

Effective use of AI in customer service requires maintaining a level of humanity. Customers can tell when interactions feel robotic or overly scripted. Rather than replacing human customer service agents and reps, many businesses choose to use AI assist tools to support them and augment their capabilities. The best results come from combining the speed and data insights of AI with the empathy and critical thinking people can provide.

It’s also important for organizations to be open. Letting customers know when AI is being used, and being clear about how their data is handled, helps build trust and keeps the experience respectful and responsible.

AI customer service is getting more clever. Features such as real-time sentiment analysis, voice AI and more advanced generative models are making it possible to handle issues faster and more intuitively. These innovations are helping companies shift from reacting to problems to building long-term loyalty through thoughtful, effective support.

Today’s customers expect real time, personalized support across digital channels and are less tolerant of delays or disjointed experiences. Traditional support models, which rely heavily on human agents, often struggle to deliver on these expectations. This deficiency results in long wait times and inconsistent service.

AI helps companies meet modern demands by delivering intelligent, always-available assistance that quickly resolves issues while easing the load on human customer service teams.

AI is reshaping customer service into a strategic advantage, as customer experience often determines loyalty. AI gives businesses a competitive edge by analyzing customer behavior in real time, anticipating needs, improving decision-making and resolving issues before they escalate.

These abilities make customer service operations both faster and more clever, shifting it from a cost center into a proactive customer engagement strategy that supports business growth and resilience.

AI enables faster responses, cleverer support and more personalized experiences. In fact, mature AI adopters (organizations operating or optimizing AI into their customer service functions) reported a 17% higher customer satisfaction percentage.²

Here are examples and use cases of how businesses use AI to improve service and the AI technologies and tools that power each one:

When you ask a question on a website and get an answer right away that’s usually a chatbot. AI-powered chatbots provide immediate answers to common customer queries, walk users through steps or help troubleshoot problems any time of day. Mature AI adopters reported a 38% lower average inbound call handling time.²

Chatbots are built by using natural language processing (NLP)—which allows them to understand and respond to human language—and machine learning (ML). The NLP helps them learn from past customer interactions and improve over time without manual updates.

VCAs are more advanced than basic chatbots. Often used in e-commerce, VCAs are found in mobile apps or smart devices that use conversational AI, which combines NLP and ML to create human-like interactions. A virtual AI agent can handle more complex tasks like placing orders, resolving account issues or offering product advice, often through both voice and text.

AI can automatically sort customer inquiries and route them to the best person or team. Machine learning analyzes past behaviors and outcomes while predictive analytics uses data patterns to forecast the urgency or topic of a message and immediately send it to the right destination.

For example, when a global camping company implemented a cognitive IBM tool to modernize its contact center, it resulted in a 33% increase in agent efficiency and an average wait time of just 33 seconds.³

AI can spot when something is off—like unusual account activity or a service that’s about to lapse—and step in with help customers before they realize it. Predictive analytics looks at your past behavior and compares it to real-time patterns to figure out what you might need next, such as a subscription renewal reminder or help with a product.

AI tools can read the tone and emotion in a customer’s message. Using sentiment analysis technology, they evaluate language cues to understand how someone feels, whether they’re angry, frustrated or happy. This ability helps teams respond faster to unhappy customers and handle tough conversations with more care.

Instead of a customer digging through endless help pages or FAQs, AI can suggest the exact guide, video or solution they need based on what they searched for, viewed or purchased. These systems rely on recommendation engines, which are algorithms trained to recognize preferences and suggest relevant resources.

AI can scan, tag and organize large libraries of support content, creating a knowledge base to help both customers and support agents find accurate answers quickly. Using machine learning, it learns which articles are most helpful. Some systems use generative AI to instantly create tailored help content or summaries.

After a support interaction, Robotic Process Automation (RPA) can send follow-up emails, satisfaction surveys, summaries or case updates automatically, without human input. RPA focuses on automating rules-based repetitive tasks to streamline operations and free up agents for more complex issues.

AI reviews support conversations in real time to flag potential issues, such as policy violations or dissatisfied customers. Using real-time analytics and machine learning, these systems help managers coach agents and fix problems as they happen.

AI-powered voice recognition enables automated phone systems to understand spoken language. Interactive voice response (IVR) systems allow users to describe their issue naturally instead of forcing them through endless “press 1, press 2” menus. Combined with conversational AI, these systems create a more intuitive and less frustrating phone support experience and improve contact center efficiency.

For example, a major UK retail and commercial bank in the United Kingdom has adopted an AI system that can take the natural language questions posed by users and proactively answer them within the chat. This implementation resulted in a 150% boost in satisfaction for some answers.⁴

Agent empowerment: By handling simple tasks, AI supports human agent productivity, allowing them to focus on more complex or emotionally sensitive issues. It can also provide agents with real-time insights and suggested next steps during conversations.

Research by the National Bureau of Economic Research (NBER) shows that when customer support professionals were given access to AI agents, their productivity increased by an average of 14%.⁵

All-day availability: AI-powered systems such as chatbots and virtual assistants are available around the clock, giving customers support whenever they need it whether through a website, mobile app or traditional call center. For example, the modernization of the global camping company mentioned earlier resulted in a 40% increase in customer engagement on all platforms.³

Better emotional intelligence: With sentiment analysis, AI can detect emotions like frustration or satisfaction in customer communications, allowing companies to respond more thoughtfully and prioritize urgent cases.

Consistency across channels: AI helps ensure that customers get consistent answers and experiences across omnichannel platforms, including chat, email, social media or phone.

Cost efficiency: AI automation lowers costs by reducing reliance on extra staff for routine inquiries and repetitive tasks.

Enhanced quality control: AI can monitor service interactions in real time and help identify coaching opportunities for agents or flag conversations that need a second look, improving service quality continuously.

Faster response times: AI can instantly reply to customer inquiries, reducing wait times dramatically and making the support experience quicker and more efficient.

Greater accessibility: AI tools such as voice assistants and multilingual chatbots make it easier for customers with disabilities or language barriers to access support services.

Improved customer insights: AI tools gather and analyze huge amounts of customer data, helping businesses better understand customer behavior, preferences and challenges to improve products and services.

Personalized experiences: By analyzing customer data and behaviors, AI can deliver highly personalized recommendations, responses and support journeys tailored to individual customer needs.

For example, IBM collaborated with a German media company to implement a generative AI-powered assistant aimed at enhancing customer service and product recommendations. As a result, customers now receive personalized product suggestions that align precisely with their preferences—at a speed 10 times faster than before. This innovation has led to a roughly 15% increase in customer satisfaction.⁶

Proactive support: AI can predict when customers might encounter problems and offer solutions before they even realize there’s an issue, increasing customer satisfaction and loyalty.

Scalability: AI allows businesses to handle large volumes of customer requests simultaneously and streamline operations without needing to hire huge support teams, making it easier to grow without sacrificing service quality.

Considerations and suggested approaches for implementing AI in customer service include:

Before introducing an AI solution, businesses should identify specific goals—whether it’s reducing response times, scaling support or improving personalization—so the implementation aligns with measurable outcomes.

AI should enhance, not replace, human support. It's best used for routine tasks, while complex, emotional or sensitive cases use human interaction from agents who can provide nuance and empathy.

Customers should know when they are interacting with an AI and when a human is available. Clarity builds trust and sets expectations, especially in high-stakes or emotional situations.

Effective AI depends on clean, accurate and representative customer data. Poor data can lead to irrelevant responses or biased outputs, so regular auditing and updates are important.

AI models should learn not just from historical data, but also from real-time customer feedback and agent input to refine their performance over time.

AI should work in harmony with current customer service platforms (such as CRM systems), so agents have full context and customers enjoy a seamless experience across channels.

AI should use available customer data to personalize interactions. Customers appreciate feeling recognized and receiving relevant responses rather than generic answers.

Consider ethical implications like privacy, transparency and bias. AI should respect data privacy laws (like GDPR) and avoid reinforcing harmful assumptions or stereotypes.

Even advanced AI can make mistakes or generate biased responses. Regular testing, human oversight and built-in review mechanisms help reduce these risks.

Employees need to understand how AI tools work and how to collaborate with them. Training should focus on when to step in, how to interpret AI recommendations and how to manage hybrid workflows.

AI systems should be able to grow with the business and adapt to changing needs, customer volumes and service strategies without needing constant reengineering.

Track AI performance through key performance indicators (KPIs) such as resolution rates, customer satisfaction (CSAT) and escalation frequency. Use these insights to regularly refine and optimize AI strategies.