AI in Customer Service: How it’s Used and What’s Next (2025-2026)

The natural language processing (NLP) power of generative artificial intelligence (AI) means that the tech can directly interface with humans—through text, voice, and even video chat.

As such, customer-facing AI-powered support is a big focus within the customer service sector right now. This isn’t the only way to use AI in customer service operations, though.

Let’s take a look at how innovative companies are using AI to improve customer satisfaction, streamline work, and support human team members’ workflows.

What Kind of AI is Used in Customer Service?

Customer service teams are using tools that include non-generative machine learning as well as generative AI. Generative, conversational AI tools (think ChatGPT or Claude) are becoming increasingly popular thanks to how well the tech can replicate human speech patterns through a process called natural language processing (NLP).

You might find non-generative machine learning in use for data processing, with conversational generative large language models (LLMs) in customer-facing applications.

These LLMs analyze communications, provide real-time translations, segment users, and more.

How AI is Transforming Customer Service

AI is having a big impact on customer service right now, but it’s not eliminating the human touch. Many companies are implementing AI-powered tools in ways that make it easier for human support team members to answer questions and build customer relationships.

This is happening through the deployment of AI algorithms that rapidly crunch omnichannel customer data, generate knowledge base summaries for staff, adjust wait time predictions, and even handle routine tasks that don’t require a person.

Let’s take a closer look at some of the trends and tools that are disrupting the customer service sector now — or are poised to do so in the next year.

Sentiment Analysis

Customer surveys are common, but they only give you one part of the picture—how a customer felt after an interaction … if they bothered to fill out the survey.

NLP makes it easier than ever to assess whether a data point or customer interaction is positive, negative, or neutral—in real-time, too.

We’re forecasting that interest in AI tools for sentiment analysis is about to spike by over 3,000%, so it’s a great time to start exploring how this tech can streamline your own operations.

Quality Monitoring

Sentiment feedback can be combined with other data to get a better picture of overall call or chat quality, too. As a quality assurance (QA) aid, AI systems can evaluate data points related to:

• Conversation length
• Wait times
• Customer satisfaction
• Final resolution
• Repeat or follow-up support requests

Analyzing this data across multiple calls and chats can result in faster and more efficient identification of potential service gaps or roadblocks.

Personalized Experiences

Sales, service and marketing teams are also using AI for better personalization of the customer experience. They’re leveraging machine learning to gather and analyze data from sources like:

• Support chat messages
• Call recordings
• Social media interactions
• Customer service email exchanges
• Website visits

While there are a number of companies that provide AI-powered personalization tools, we’re seeing a noted spike in interest around a select few including Ortto.

Ortto merges marketing analytics, customer data, and support tools into one app that helps teams:

• Build personalized customer journeys and email sequences
• Start support chats and screen shares
• Delegate chats to other team members
• Build AI support chatbots
• Monitor where customers are in the product adoption process

Interest in Ortto has grown by 300% in recent years and we expect this growth to keep increasing over the next 12 months.

Intelligent Routing and Segmentation

Some AI-enabled software takes personalization one step further by automating the process of who should help a customer or contact.

Call routing technology has been in use for over 50 years, but AI takes the process one step further. Now, customer requests can be instantly routed to the to the best support staff based on things like:

• CRM data about past customer inquiries
• Customer and support agent interaction preferences
• Staff experience level and resolution rate
• Customer’s assessed sentiment level
• Issue urgency and priority
• Current support team response times
• Team members’ soft skills

More companies are realizing that AI integration can improve customer service personalization—and we’re seeing an uptick in searches for intelligent routing as a result.

Predictive Support

Sometimes, this routing begins before a customer makes contact with the company. By using AI to analyze data about when customers are most likely to need support, companies can preemptively reach out to buyers or users who meet certain criteria.

For instance, if an accounting software company notices that its small business customers typically reach out with questions 72 hours after purchasing a license, human support agents or AI chat systems can reach out within that time frame to share helpful resource articles, offer to set up a call, and more.

Correctly predicting the need for and delivering this support—especially when personalized—can help to build customer loyalty and boost satisfaction with a product or service.

Interest in predictive support is still low, but growing rapidly from interest levels at the start of the year. We expect to see interest keep growing as AI adoption rates go up.

How Will AI Continue to Improve Customer Service?

AI agents are poised to continue revolutionizing the customer service sector. More than a simple chatbot, agentic tools can execute multi-step, complex workflows, even delegating tasks to other AI agents.

Agentic AI systems made of multiple agents can also determine the steps that must be executed to reach a goal — allowing human strategists to be fairly hands-off while the AI works.

Companies won’t need to buy an expensive, single-purpose SaaS tool for access to AI agents, either. Multipurpose AI workflow builders like N8n make it possible to:

• Connect existing business apps
• Build task workflows
• Deploy custom AI apps across an organization
• Update and manage AI apps internally

For example, a company could build AI support agents that tap into CRM data, process prompts in Claude, access internal knowledge bases, and ping human workers with updates in Slack … all in moments.

This versatility is one of the reasons why we expect to see searches for N8n grow to over four million per month in 2026.

Pros and Cons of AI for Customer Service

Ultimately, adding AI technologies into the customer service process has the potential to streamline customer experiences, reduce human agents' productivity and workflows, optimize the way customer needs are addressed, and more.

When deployed correctly, it can bring benefits to both the person seeking and the employee providing support.

If used too heavily, though, one drawback of AI is that it can make the customer service process feel too robotic. This may not be an issue for some low-level support calls, but it's vital to identify when and how a human touch is required.

Another potential drawback of going all-in on AI is that some customer service and contact center teams may find their ability to use AI limited due to data privacy regulations.

In order to get customer insights out of an AI-driven tool, you have to feed customer data into the tool—and there can be restrictions around what may be shared. Sometimes, these restrictions are based on where a customer lives; other times, it’s due to industry norms or company policy.

Find Out What's Shaping the Customer Service Sector Next

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