The power of natural language processing (NLP) in artificial intelligence (AI) means that technology can interface directly with humans through text, voice, and even video chat.
As such, AI-driven support for customers is currently a major focus within the customer service sector. However, this is not the only way to use AI in customer service operations.
Let's see how innovative companies use AI to improve customer satisfaction, streamline work, and support the workflow of human team members.
What types of AI are used in customer service?
Our customer service teams use tools that include generative machine learning and generator AI. Generic, conversational AI tools (think ChatGpt or Claude) are becoming increasingly popular thanks to the extent that Tech can replicate human speech patterns through a process known as natural language processing (NLP).
You may find non-generated machine learning used for data processing with large-scale language models (LLM) that generate conversations in customer applications.
These LLMs analyze communications, provide real-time translation, segment users, and more.
How AI can transform customer service
AI is currently having a major impact on customer service, but it doesn't eliminate human touch. Many companies implement AI-powered tools in a way that makes it easier for human support team members to answer questions and build customer relationships.
This is done through the deployment of AI algorithms that quickly calculate Omnichannel customer data, generate a staff knowledge base overview, adjust latency predictions, and handle routine tasks that do not require human resources.
Let's take a closer look at some of the trends and tools currently disrupt the customer service sector.
Emotional analysis
Customer surveys are common, but only give a portion of the photo. How customers felt after the interaction…If you are troubled by the investigation.
NLP makes it easier than ever to assess in real time whether a data point or customer interaction is positive, negative, or neutral.
With the forecast that interest in AI tools for sentiment analysis is about to surge by over 3,000%, it is the perfect time to explore ways that this technology can streamline its own operations.
Quality monitoring
Combine sentiment feedback with other data to get a better picture of overall call and chat quality. As a quality assurance (QA) aid, AI systems can evaluate data points related to:
- The length of conversation
- Waiting time
- Customer satisfaction
- Final resolution
- Repeat or follow up support requests
Analyzing this data over multiple calls and chats will allow you to identify potential service gaps or obstacles faster and more efficiently.
Tip: Internet search and AI chat prompts are another good indicator of the problems customers may be experiencing, or frustration they face in relation to support channels. The Semrush AI SEO Toolkit is a great way to discover this information.
The app shows how a brand is mentioned in ChatGPT responses (along with those from other AI tools), analyzes sentiment and voice, identify what people related to the brand are searching, and tracks how certain prompts are displayed.
AI refers to the next strategic move
Analyze how LLM can work with ChatGPT features and get actionable recommendations to improve business strategy, product and market location.
A personalized experience
Our sales, service and marketing teams use AI to better personalize the customer experience. They use machine learning to collect and analyze data from sources such as:
- Chat messaging support
- Call the recording
- Social Media Interactions
- Customer Service Email Exchange
- Visiting the website
There are many companies that offer AI-powered personalization tools, but the interesting spikes around a small number of people, including Ortto, are gaining attention.
Ortto merges marketing analytics, customer data and support tools into one app that helps your team.
- Build personalized customer journeys and email sequences
- Start support chat and screen sharing
- Delegating chat with other team members
- Build an AI Support Chatbot
- Monitor where customers are in the product recruitment process
Interest in ORTTO has increased by 300% in recent years, and we expect this growth to continue to increase in the next 12 months.
Intelligent Routing and Segmentation
Some AI-enabled software takes personalization a step further by automating the processes of customers and those who help them communicate.
Call Routing Technology has been in use for over 50 years, but AI is taking the process a step further. Currently, customer requests can be routed to the best support staff immediately based on:
- CRM data on past customer inquiries
- Customer and Support Agent Interaction Preferences
- Staff experience level and resolution rate
- Customer's rated emotion level
- Issuing urgency and priorities
- Current Support Team Response Time
- Soft skills of team members
More businesses are aware that AI integration can improve customer service personalization. As a result, we see an increase in searches for intelligent routing.
Predictive Support
Sometimes this routing begins before the customer contacts the company. Using AI, businesses can reach out to buyers or users who meet certain criteria by analyzing data about when customers are most likely to need support.
For example, if an accounting software company notices that a small business customer is normally reaching out with a question 72 hours after purchasing a license, within that time frame, a human support agent or AI chat system can share articles of useful resources, set up a phone, and more.
It will correctly predict this need for support, and help build customer loyalty and increase satisfaction with your product or service, especially when personalized.
Interest in predictive support is still low, but by the beginning of the year it will grow rapidly from its interest level. We expect interest to grow as AI adoption rates rise.
How does AI continue to improve customer service?
AI agents are poised to continue revolutionizing the customer service sector. More than a simple chatbot, agent tools can run complex, multi-step workflows and delegate tasks to other AI agents.
Agent AI systems made up of multiple agents can also determine the steps that need to be taken to reach the goal.
Companies don't need to buy expensive, single purpose SaaS tools to access AI agents. Multipurpose AI workflow builders like N8N allow you to:
- Connect your existing business app
- Build a task workflow
- Deploy custom AI apps across your organization
- Update and manage your AI apps internally
For example, businesses can access human workers with AI support agents leveraging CRM data, Claude process prompts, access to internal knowledge bases, and Slack updates.
This versatility is one of the reasons why we hope that N8N searches will grow to over 4 million people a month in 2026.
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Pros and Cons of AI for Customer Service
Ultimately, adding AI technology to your customer service process can streamline customer experiences, reduce human agent productivity and workflow, and optimize how customers need are addressed.
When deployed correctly, it can benefit both the people they want and the employees who provide support.
However, when used too heavily, one of the drawbacks of AI is that it can make the customer service process feel too robotic. This may not be a problem with some low-level support calls, but it is important to identify when and how human touch is needed.
Another potential drawback of being all-in with AI is that some customer service and contact centre teams may find the ability to use AI Limited for data privacy regulations.
To gain customer insights from AI-driven tools, you need to feed customer data Inside Tools – and there may be restrictions on what may be shared. Sometimes these restrictions are based on where the client lives. Otherwise, it is due to industry norms and company policies.
Next, check the formation of the customer service sector
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