• Artificial Intelligence & Customer Service Innovation

The Role of Artificial Intelligence in Revolutionizing Customer Service

  • Felix Rose-Collins
  • 6 min read
The Role of Artificial Intelligence in Revolutionizing Customer Service


The generative AI and customer service have formed a duo that leads the digital transformation today. Clumsy bots that didn't understand our requests and long response time have given place to a completely new approach. With deep learning algorithms and large language models, we're now transforming the customer service, increasing operations, but more importantly, responding to customers' needs.

It's the customer who dictates the rules, shaping the services landscape with their needs, requests, and satisfaction. That's why the majority of businesses adapt as well. They change their strategies and wonder how to start an AI company. Millions of dollars flow into AI investment in hope to increase customer satisfaction and retain clients.

The generative AI in customer service has already shown marvelous results, and companies don't intend to stop. A quick glance at the current trends will show us the direction the technology takes in transforming customer service.

Behind the Scenes of Generative AI

Before we actually integrate generative AI into customer service, how are the models built? The creation and the training of the model go through a number of steps:

  • Data collection To train the AI model, we collect big amounts of data. Based on the task we want a model to accomplish, we gather data in different formats, such as texts, images, videos, etc. Further, the data is cleaned and annotated before passing to the training.
  • Model selection The developers then choose the model that will understand and process the data. Whether it will be a large language model (LLM), such as ChatGPT, or a conversational AI model, such as Amazon Lex, the choice will shape a model's further performance.
  • Technology implementation The usage of machine learning and natural language processing algorithms allows setting up the model correctly. Deep learning techniques assist its continuous learning, improving its performance and generating precise and human-like responses.
  • Model training During the training process, a model learns to recognize the intent and generate the response. Fine-tuning helps to adjust and improve the model.
  • Integration As soon as a model is ready and trained, the integration with the customer service tools begins. Usually, the integration is done through APIs, but may also involve direct backend integration with CRM tools.
  • Testing As in the development of all products, the testing allows checking whether the created model fulfills the requirements of customer support tasks. After you go live, the regular checks allow adjusting and upgrading the model.

With new tools and technologies that work separately or altogether, the customer support can tackle regular tasks differently. The improvement of a customer journey starts here.

Generative AI for Customer Service: Current Functionalities

While some businesses use AI to write texts, automate tasks, or create analytics, customer service combines them all. All automation and process improvements aim to streamline the operations and refine customer experience. According to Forbes, customer service is in the top contributors to customer loyalty. The interaction with the customer is no longer limited by solving their problems. It's about building long-term partnerships that share common values.

Turning to generative AI for customer service, businesses create a new level of relationships with customers, showing that they care. People mistakenly think that generative AI for customer support is limited by chatbots answering customer inquiries. In reality, AI tools help us understand customer intentions better, offering quicker solutions.

Text Generation

Thanks to the conversational flow and intent recognition, generative AI in customer service helps to create content quicker. In most cases, the algorithms analyze the intention, the historical data of interactions, the knowledge base of the company, and generate the response. Deep learning algorithms are constantly training, which necessitates an agent to validate a final response.

Such automation benefits interactions through chats, emails, and social media. It saves agents time on processing customer requests. As a result, the reduction of the waiting time increases the customer satisfaction.

Personalized Communication

With generative AI for customer support, companies can use the historical data and suggest products based on the customer's previous choice. The functionality well known to us from online shopping has moved on to customer support, allowing tailored responses and a more personalized experience. As Medallia states, personalization is now a necessity, influencing customers' brand choice and loyalty.

Sentiment Analysis

From merely understanding what the customer writes and says, tools now recognize their emotions and behavior. Artificial intelligence and natural language processing have changed the way of interaction. Sentiment analysis is one of the most valuable examples of generative AI in customer service. After collecting and processing data, then scoring sentiments, we use precious analyzes to provide support.

As a result, tailored responses to customers' requests not only amaze with personalizations, but also allow to proactively eliminate issues. Or, at least, quickly reduce the level of distress.

Predictive Analytics and Reporting

AI capabilities in providing predictive analytics is what makes businesses shift from reactive to proactive customer service. The generative AI for customer service collects data from customer's profile, historic interactions, and website activity. It can also analyze a customer's interactions on social media. The model's analysis of this data further builds predictions and generates patterns on the customer's behavior.

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The analytics and reporting help with recommendations of products, predictions of any issues, or the likelihood of customer's contact. The predicted customers' needs allow agents to reach out to customers proactively, eliminating any potential issues.

Multi-Language Support

Businesses operating in different countries don't always have the possibility to provide support in different languages. Agents speaking multiple languages can simply be hard to find. With generative AI for customer service, translations in real time come in handy. The tools can also help customer support agents to generate the response in the specified language based on the customer's request.

Knowledge Base

Knowledge base bots are smart assistants trained on huge datasets. Smart enough to resolve customer queries in real time, they suggest relevant sources or actions to take. As if a customer was using a search engine, they receive proposed solutions for self-serve actions.

With the knowledge base tools, customer service saves time for the agents, while at the same time providing quick solutions to the customers. With the knowledge base, the generative AI and customer service match perfectly, creating a win-win outcome both for the business and the customer.

Benefits of Generative AI in Customer Service

While some might believe that the implementation of artificial intelligence is aimed at reducing team extension, in reality it transforms the customer service into a more meaningful space. As we once let machines count for us, why not trust them with repetitive and manual tasks, liberating time for strategical activities?

Generative AI tools bring to customer service the following gains:

  • Continuous operations AI now has a full arsenal of tools and possibilities to make operations ongoing. In case your business deals with a huge number of requests even outside operating hours, virtual assistants can help. Taking over a part of customer support's tasks, they provide constant support to the clients.
  • Reduced response time Prepopulated responses and sentiment analysis work in synergy to immediately react to customer's requests. While agents focus on more complicated cases, virtual assistants help with general inquiries, such as the delivery time or return procedure. Customers get quick solutions in no time.
  • Personalized approach The communication with customers becomes tailored and personalized. The agents better understand customers' needs and react accordingly. The tools based on natural language processing algorithms decipher the conversation nuances in the same way the human agents do. Well-trained models are even capable of recognizing customers' emotions and satisfaction.
  • Proactive support AI tools enter into the game not only during direct interactions with customers. The reports identify potential issues, allowing customer service agents to take proactive actions. The algorithms help to offer individual recommendations for further purchases. Besides, they can identify the potential drop-offs, preparing the basis for further marketing campaigns.
  • Reduced human errors AI tools in customer support are not only about external interactions with customers. They help to optimize internal processes, reducing the repetitive tasks. Machines process inputs, generate reports, and build insights, which reduces manual interventions and human errors.

Modern Customer Support: Smarter, Faster

As we have seen, the implementation of generative AI in customer support brings real transformations. This is a new reality that many businesses try to adopt and develop within. With smarter tools and virtual assistants, customer support agents can build real, long-term relationships with customers. From reducing waiting time to offering personalized recommendations, businesses focus their operations around the customer.

The agents save their time and solve tickets quicker, while customers receive better service multiplied to customized approaches. The likelihood that customers will return increases and their loyalty strengthens. In the coming years, the role of AI in customer support seems to only increase. For a smarter customer service, AI tools can be installed on all stages of the customer journey, from first contacts, to complaints, to feedback. Businesses that embrace these technologies and continuously adapt to the evolving landscape will be well-positioned to offer superior customer experiences, setting themselves apart in an increasingly competitive market.

Felix Rose-Collins

Felix Rose-Collins

Ranktracker's CEO/CMO & Co-founder

Felix Rose-Collins is the Co-founder and CEO/CMO of Ranktracker. With over 15 years of SEO experience, he has single-handedly scaled the Ranktracker site to over 500,000 monthly visits, with 390,000 of these stemming from organic searches each month.

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