This article is about Generative Artificial Intelligence (AI) in customer service

What does Generative AI mean in customer service?

Generative AI in customer service is a form of customer service that is provided partly or entirely by AI tools, typically in combination with human support staff. AI tools that focus on customer service use technologies such as Natural Language Processing (NLP), Machine Learning (ML) and Generative AI to accomplish tasks that don’t require the expertise and finesse of a human support team.

With the increasing use of AI tools, many support staff have shown mixed reactions to the introduction of this technology. While there are obvious benefits to automating some parts of customer service, there are also risks involved.

What are the advantages of Generative AI in customer service?

Shorter response times

When customers have a problem, they don’t want to wait long for help. Almost a third of customers expect a response within an hour or less. When you buy a product or subscribe to a service, you want it to work, and when it doesn’t, it can be very frustrating.

AI tools shorten response times by automating routine tasks, such as answering frequently asked questions or processing simple tasks using chatbots and AI assistants. This helps to shorten the processing time of customer inquiries in two ways: customers with simple inquiries receive immediate help, and your support team has more time to deal with more complex problems.

Personalized experiences

In addition to faster responses, AI can also offer customers more personalized support. AI tools are good at analyzing and understanding customer behavior, preferences and history with your business. With this data at hand, it’s easier to provide support that’s tailored to each customer, whether that’s through an interaction with a chatbot or a member of your customer service team.

By offering personalized experiences, AI technology enables stronger customer relationships to be built, loyalty to be increased and a positive brand perception to be created. In the long term, this leads to improved competitiveness.

Faster training of employees

One of the biggest challenges when training new team members is to provide them with product and guideline knowledge in a short space of time. It is not unusual for new employees to feel insecure for a while after their training.

AI assistants and knowledge database tools can point new employees in the right direction and help them gain confidence more quickly.

Improved accuracy and increased quality

The same AI tools can help your team provide more accurate support. AI can use contextual information about the specific customer, as well as data from previous interactions, to provide more relevant and tailored help rather than just relying on standardized responses.

Cost savings

One likely key benefit of Generative AI in customer service that many business leaders are thinking about is cost savings. While AI cannot (and should not) replace human customer service agents, it will likely lead to a reduction in basic support roles in the future. Instead of just using these savings, it would be better to reinvest them in other parts of your support department. For example, this could mean redoubling your knowledge management efforts, offering customers more learning resources such as live courses or videos, or even training your team members in AI technology so they can take over the management of your AI operations.

What are the risks of Generative AI in customer service?

Hallucinations

Generative AI has many advantages, but it is important to be realistic about the current state of the technology. Generative AI can be prone to hallucinations – situations in which Generative AI does not know the answer to a question and therefore simply makes one up. It may be fine for a chatbot to perform simple tasks such as resetting passwords or checking the status of an order itself. However, people should always take responsibility for important requests.

Chatbots don’t know what they don’t know

When an AI chatbot responds to your customers, its response is based on the data it has been trained with. These are usually sources such as knowledge base articles, previous support discussions and product instructions. Knowledge base articles are constantly becoming outdated, and it’s hard to keep up with new features, product enhancements and changes in problem-solving processes. This also requires resources.

Training your Generative AI is not a one-off task. If you’re relying on an AI chatbot or typing assistant, you’ll need a dedicated person – or even a dedicated team, depending on the size of your business – to keep the knowledge base up to date, ensure the Generative AI is only referencing high-quality customer conversations, and monitor customer feedback to identify issues in the system.

Your Generative AI will only be as good as your data.

What options are there for using Generative AI in customer service?

There are already a large number of different AI tools available today, all of which can be used effectively in customer service. Here are some examples of the applications for which Generative AI can be used in customer service.

Helpdesk automation

When you think of customer service software, helpdesk software is probably the first thing that comes to mind. Helpdesks enable support teams to centralize customer communications across multiple channels, facilitate collaboration and effectively track customer issues. While helpdesks have long had rule-based automation – often referred to as workflows – as a standard function, Generative AI goes one step further.

For example, a standard helpdesk workflow can route tickets to a specific agent based on static criteria such as a keyword in the subject line or a message from a specific domain. A Generative AI-powered helpdesk can take the process further by analyzing the content, sentiment and urgency of an email’s text and routing it to the team member who can best respond.

Similarly, Generative AI can leverage these natural language processing (NLP) and machine learning (ML) capabilities to automatically tag issues, suggest new and more consistent categorizations, filter out spam and automated responses to relieve the queue, suggest canned responses or relevant content based on the customer issue, or summarize long conversations to bring team members up to speed when they take on a case.

Utilizing these Generative AI capabilities helps reduce the risk of human error, makes the customer service process faster, improves the customer experience and gives your team time to focus on more complex and valuable tasks.

Writing assistants

Not so long ago, computer-generated text was easy to recognize. However, with better large language models (LLMs) such as OpenAI’s GPT-4, Generative AI can simulate human speech quite well. Many support solutions make use of this technological improvement and integrate writing assistants into their functions.

In customer service, writing assistants can be helpful in two main ways:

• They can help agents to improve or change texts written by humans. This includes spelling and grammar checks, adjusting the length or tone or translating the text to support multilingual service.
• You can write texts in response to customer queries without (or with minimal) human intervention. In this scenario, the AI can draw on existing information such as knowledge base articles and previous conversations to create an answer independently.

The main advantage of a writing assistant is efficiency.

Chatbots and voicebots

Generative AI chatbots can provide a much better experience than rule-based (legacy) chatbots. Chatbots or voicebots powered by Generative AI are not limited by a specific flow, have full access to your documentation, can learn from previous experiences and can create conversational interactions that feel natural to the customer. They also enable more personalized support, as they are able to communicate in a customer’s native language and have access to customer data that can help Generative AI better tailor its responses.

Of course, generative AI chatbots are not perfect and cannot replace the expertise of your support team. Chatbots and voicebots can give false information due to hallucinations or bad data and should only be used for low-risk conversations. However, if they are well-trained and thoughtfully implemented, they can be very useful for answering frequently asked questions in a matter of seconds. See also the example of Helvetia Insurance.

Knowledge databases

Research shows that customers want quick answers and like to look for solutions to their problems themselves. Self-service options like knowledge bases are a great way to fulfill both expectations while having the benefit of keeping frequently asked questions out of the queue.

The standard help center that is part of your support software already has many features that benefit both users and agents – widgets that allow access to knowledge base articles from anywhere on your website, integrations with ticketing or conversation editors that make it easy to include links to relevant articles in your responses to customers, and methods to display relevant documentation to enable proactive support.

Knowledge databases improved with Generative AI offer your customers and your team even more benefits. For example, traditional knowledge bases have search engines that display results based on keywords. While this sometimes returns the correct result, it can often display content that is completely irrelevant to the user’s search query, especially if the query was written as a complete sentence. NLP makes it possible for a search engine to understand search queries as questions and even provide an answer in conversational language instead of just displaying a list of possible articles.

Another big advantage of Generative AI knowledge bases is their ability to become more accurate over time. Machine learning enables the software to learn from customer interactions and continuously improve its results. AI can also use contextual information, such as the type of device the customer is using to access your help center, to ensure that the content displayed on their screen is easy to see.

Finally, AI knowledge bases can help your team keep information up to date by flagging outdated content, suggesting (or even writing) new articles to fill content gaps, and flagging inaccuracies between data sources. Since chatbots and writing assistants rely on high-quality data, support for knowledge maintenance can be extremely valuable.

Sentiment analysis

In customer support, we are all very familiar with measuring sentiment using tools such as NPS or CSAT surveys. These can be helpful indicators of how customers feel about our product, our service or our support. However, not everyone responds to surveys. Often you need to immerse yourself in the language a customer uses in a call, email or chat to really understand how they feel.

With traditional tools, this means manually searching through conversations, usually as part of the quality assurance process. Generative AI mood analysis tools can offer great added value here. By using NLP, the system can sift through large volumes of conversations and search for terms typically associated with satisfied and dissatisfied customers. For completed calls, the system can classify and tag the calls, making it easier for managers to review them.

For live interactions, the system can use mood analysis to set priorities. For example, if a customer email is flagged as angry, it can be moved up the queue, or if a customer is frustrated with a chatbot experience, it can be proactively routed to a human to avoid further annoyance.

Analyzes

As processing large amounts of data is Generative AI’s specialty, it’s no surprise that reporting and analysis tools benefit from the technology. Generative AI can carry out this analysis for you. Instead of just seeing dashboards with pie charts and bar graphs, Generative AI can actually summarize all the information, highlight important details or alert you to potential risks. It can also make recommendations for changes that could improve service, increase efficiency or enhance customer satisfaction.

How do you find the right AI tool for customer service?

The decision on which option to choose depends heavily on the goals you want to achieve, the problems you want to solve and the resources available.

Together with Prof. Dr. Claudia Bünte, I offer courses like this one to show you different generative AI tools in customer service and marketing and teach you how to find the right tool.

The next course will take place in Berlin in September and in Zurich in November. Until then, the following summarized tips may also help you.

Goals

The first thing you should do when considering new software is to define which problems you want to solve with the new tool:

• Is your team overloaded with repetitive tasks or questions?
• Are some agents good at troubleshooting and solving technical problems, but have difficulty communicating their findings in writing?
• Do you need a better way to analyze data than your helpdesk’s standard dashboards?

These are all problems that can be solved by using Generative AI, but some tools are better suited to certain problems than others. If you don’t have a specific problem in mind and instead are just curious and want to try out a few tools, that’s good to know too!

Mandatory functions

Once you have defined your goals, you should consider which non-negotiable functions your new software must have. These could include things like:

• Integration with the other tools in your technology stack.
• The ability to scale with your business as your team and customer base grows.
• Support with implementation.
• The ability to control what content your Generative AI tools access.
• Compliance with your company’s security and privacy policies and any local or federal laws that your company must comply with.

User friendliness

If your new software has a cumbersome and confusing user interface, your team won’t use it. Look for tools that are intuitive, fit easily into your existing workflows and work well with the software you already use.

Technical know-how

As well as user-friendliness, you should also consider the technical skills of your team. If you don’t have a developer on your team, look for a solution that is essentially ready to go.

Integrations

If you choose a specific Generative AI option, you should consider whether it can be integrated into your existing customer service software such as the helpdesk or knowledge base system.

Budget

If you’re thinking about buying Generative AI software, you should consider more than just the prices listed on the tool’s pricing page. In addition to the subscription fee, there may be other costs associated with adding Generative AI to your setup, such as development costs if you require a custom implementation.

“Your Generative AI tool budget must cover more than just the software costs.

Customer support

With new tools often come new challenges, and there is nothing more frustrating than being stuck and not being able to reach anyone to help you. Always look for platforms that are backed by a responsive support team to help you with any problems.

Conclusion: Generative AI in customer service

I think this article shows very well: Generative AI in customer service has come to stay and every company should start now at the latest to introduce the first Generative AI applications and gain experience.

However, the introduction of generative AI tools in customer service requires much more than “simply buying new software”. Take a look at our specialist book“Kundendialog-Management- Wertstiftende Kundendialoge in Zeiten der digitalen Automation” or the book“Generative KI in Unternehmen“. Both books show in a very practical way what it means to introduce Generative AI in customer service or in the company and how you can proceed.

Further useful tips

Would you like further support with your AI project? Then hopefully these offers are just right for you.

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Then write me a message with your wishes and questions and we will find an offer for you. Just send me a message via WhatsApp or email.

Or come directly to my WhatsApp group – where I regularly post use cases, news, best practices, events and much more about chatbots, ChatGPT and co.