Top customer service trends

Customer retention is increasingly fragile, driven by shifting behaviors, social influences and the ease of switching between brands. More than ever, organizations must stay on top of changes in the customer service experience to improve customer satisfaction, gather customer feedback and meet increased customer needs.

A 2025 Gartner study found that 51% of service and support leaders prioritized increasing sales revenue more than they did last year.¹ Customer service is a necessary component of this strategy. Good customer service can strengthen brand loyalty, while poor service can erode trust and retention. Providing great customer experience while respecting data privacy is the best way to maintain an organization’s competitive advantage.

Artificial intelligence (AI) is rapidly reshaping the customer service experience. Today’s consumers expect fast responses and seamless resolutions and are increasingly comfortable with, and sometimes even prefer, AI-powered support. Technologies such as intelligent chatbots, predictive analytics and sophisticated AI agents enable businesses to meet rising expectations with greater speed, personalized experiences and efficiency.

Top customer service demands more than responsiveness—it requires intelligence, personalization and foresight. Here are some of the latest trends and new technologies shaping the future of customer service.

AI enables faster responses, smarter 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

AI’s role in customer support is evolving rapidly, with a clear progression in capabilities. Basic chatbots continue to handle routine, rule-based inquiries, offering quick responses to simple questions. More advanced virtual assistants, powered by generative AI and large language models (LLMs), can deliver natural, context-aware responses and manage more complex single-step tasks.

At the leading edge is agentic AI—autonomous agents that go beyond conversation. AI agents can decide, design workflows and connect with external tools such as application programming interfaces (APIs), databases or even other AI agents to resolve complex issues through advanced algorithms. These specialized agents are optimized for specific functions—such as billing, scheduling or troubleshooting—and represent the next major leap in customer service automation.

Even in a tech-forward world, the human connection matters. Customers still turn to live agents for sensitive, urgent or nuanced issues. Training and empowering service agents with emotional intelligence and empathy is as important as optimizing tech stacks.

Rather than replacing customer service agents, AI is increasingly used to augment and enhance their performance. 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%.³

Today’s support platforms equip human agents with real-time recommendations, suggested responses and historical context based on sentiment analysis when it’s needed. By reducing cognitive load and streamlining decision-making, AI enables agents to focus on what they do best: showing empathy, exercising judgment and building stronger customer relationships.

Advanced AI systems can also assist with post-interaction tasks, such as summarizing conversations, auto-completing customer relationship management (CRM) records and flagging follow-ups. As a result, AI improves customer outcomes and elevates the role of support professionals, turning routine customer service interactions into opportunities for deeper customer engagement.

AI-powered voice recognition is transforming traditional phone support. Instead of navigating frustrating “press 1” menus, customers can now describe their issue in plain language. Modern interactive voice recognition (IVR) systems use conversational AI to understand and respond naturally and make the experience faster, more intuitive and less stressful.

These upgrades not only improve customer satisfaction but also optimize contact center operations. One major UK bank, for instance, saw a 150% increase in satisfaction for some answers after implementing natural language understanding in its support system.⁴

More personalized interactions reflect the future of customer service, where every moment is tailored to the individual. 66% of global customer service managers who are optimizing AI use generative AI to increase personalization.² Hyper-personalization is taking customer service to the next level by using real-time data and AI to make every interaction feel more relevant.

Unlike traditional personalization, which uses basic information from the past, hyper-personalization is smarter and more proactive. It goes beyond just using a customer’s name or past purchases—service teams now tap into customer preferences, browsing behavior and other live insights to offer timely help and suggestions. This approach helps companies offer support or recommendations right when they’re most helpful, builds stronger connections and makes personalized service feel more authentic.

Today’s customers don’t think in channels—they think in experiences. They might start with a web chat, follow up by email and finish a conversation on the phone but expect the entire interaction to feel like one continuous thread.

This means customer service teams need to orchestrate customer interactions across call centers, text, social media and email. Full context needs to be maintained at all these touchpoints so that customers don't have to repeat themselves or backtrack.

Achieving this level of fluidity requires more than just offering omnichannel support—it demands intelligent coordination and consistent tracking of metrics behind the scenes. Support systems must unify customer data, conversation histories and preferences in real time to allow both agents and AI tools to pick up where the last interaction left off. This orchestration boosts customer satisfaction and drives efficiency by reducing friction and redundancy across the customer journey.

While customers expect seamless experiences across channels, many now prefer messaging as their primary method of contact. Messaging offers the convenience of asynchronous communication—letting users start and stop conversations on their own time—without waiting on hold or navigating phone menus. Platforms like WhatsApp, SMS, Instagram DMs and in-app chat have become go-to channels, especially for mobile-first users seeking fast, natural interactions.

To meet these expectations, organizations must build infrastructure that supports real-time or near real-time engagement and fast response times across messaging platforms. AI-driven assistants now handle the bulk of initial conversations, offering quick answers, guiding users through workflows and escalating to human agents when needed. Combining automation with human support helps businesses respond quickly and follow up, building stronger customer relationships over time.

AI is transforming self-service into a smarter, more dynamic experience. Adaptive knowledge bases are replacing static FAQs, delivering more relevant and responsive support. Virtual agents guide users through multistep tasks and personalized portals that surface relevant content based on customer behavior, all without the need for a human agent.

Today’s customers are happy to help themselves—if the tools work. Modern self-service platforms now feature streamlined UX and intelligent backends that personalize results by using recommendation engines trained on search history, past behavior and customer intent. The result is faster resolution, greater customer satisfaction and reduced load on support teams.

Leading organizations are shifting from addressing problems to preventing them. Instead of waiting for customers to report issues, AI now spots early signs of trouble—like frustration, technical problems or less frequent use. These alerts help customer care experts step in earlier, whether by sharing helpful tips, warning about a possible outage or offering a quick fix.

By solving problems before they grow, companies make things smoother for customers and support agents. Proactive support is quickly becoming a way to stand out and deliver great customer experiences.

Customers have interactions with many different companies throughout their lifetimes. They can easily differentiate between organizations that provide good customer service and ones that undervalue or underinvest in it. Poor customer service is a major impediment to business growth and customer retention.

Providing excellent customer service requires organizations to keep up to date on emerging trends so they meet customer expectations. The pandemic accelerated digital transformation and raised the bar for fast, personalized support—making it even more important for brands to adapt.

More organizations are embracing advanced technologies such as generative AI, agentic AI and machine learning. In fact, 51% of all mature AI adopters and 71% of AI optimizers say that using AI in customer service has helped them grow revenue.²

IBM has been helping enterprises apply trusted AI in this space for more than a decade. Generative AI has further potential to significantly transform customer and field service with the ability to understand complex inquiries and generate more human-like, conversational responses.

IBM Consulting® offers end-to-end consulting capabilities in experience design and service, data and AI transformation. By using the IBM watsonx™ portfolio of AI products and IBM watsonx™ Assistant, a market-leading conversational AI solution, we collaborate with you through the AI value creation process. This approach enhances conversational AI, improves the agent experience and optimizes call center operations and data.