Generative AI in Customer Service: Everything You Need To Know

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We are in the midst of an AI revolution, where businesses all over the world are adopting autonomous systems to help speed up processes, increase efficiency, and slash costs. Of all the types of AI currently in use, Generative AI is the most popular thanks to public-facing tools such as OpenAI’s ChatGPT and Google’s Gemini.

Generative AI has transformative potential in customer service, where its ability to quickly generate answers, summarize conversations, and create personalized interactions can speed up customer cases and lead to more effective outcomes.

In this guide, we’ll focus on Generative AI specifically and how it supports customer service processes.

It’s essential, however, to remember that Generative AI is not intended to be used as a standalone technology within an enterprise business. Instead, it must be combined with other types of AI, such as Conversational AI or Agentic AI, to create dedicated AI Agents that are capable of autonomous customer support.

Keep reading to build your understanding of what Generative AI is and how it can be used to support your customer service processes.

Key Takeaways

Generative AI helps customer service teams interpret language, sentiment, and intent, then generate useful responses and content.
Gen AI automates routine tasks like call notes, FAQs, and case summaries, freeing up your human agents to focus on complex issues.
Agents get access to real-time transcription and translation services, as well as in-call context-aware suggestions for next-best actions.
Customers benefit from 24/7, multilingual support across channels, improving satisfaction and reducing wait times.
Internal data and powerful insights allow organizations to improve and scale AI based on real-world results.
Generative AI can also be used to improve internal HR processes, creating detailed knowledge articles, onboarding materials, or even acting as a self-service HR AI Agent.
Generative AI comes with some key challenges, which include preventing hallucinations, safeguarding data, and maintaining the right balance between automation and human touch.
Generative AI is just one part of the future of AI. Combined with other tools like Conversational AI or Agentic AI, organizations will deploy dedicated AI Agents that can offer fast, personalized support to customers.

What is Generative AI in Customer Service?

AI is a confusing topic with lots of frequent updates that make it hard to keep track. The media often refers to AI as a blanket term, when in fact there are different types of AI that all have unique purposes.

Generative AI, specifically Large Language Models, rely on massive amounts of training data to enable its understanding of human language. It then uses this to understand and interpret input given via voice, text, or even imagery. Generative AI is able to not only understand the language but also the intent, context, and sentiment behind it.

Most importantly, Generative AI is then able to create content in many different formats based on a given prompt. This has a vast array of potential applications, from generating FAQ answers to customers as soon as they ask them through to summarizing an entire conversation in a matter of seconds.

When paired with other types of AI within an AI Agent, Generative AI is responsible for the creation of information and resources used to either reply to customers or to improve existing assets such as your internal knowledge base. It can also listen in during human calls to offer support, such as translation and transcription.

Generative AI can be additionally finetuned with your own internal training data to make sure it has full knowledge of industry-specific processes and terminology.

Ultimately, Generative AI within customer service is a tool used to quickly and effectively generate content based on customer needs or your own internal requirements.

Benefits of Generative AI for Customer Service

So what benefit does Generative AI bring to your contact center AI solution? Here are some of the top benefits that continue to drive Generative AI adoption rates across the sector…

Increases Customer Service Productivity

Generative AI can automatically generate call summaries, logs, and wrap‑up notes in your desired format, which instantly saves time and turns tasks that were once regarded as a chore into instant actions.

With its ability to create instant content in response to customer queries, Generative AI rapidly improves customer service productivity and takes away some of the biggest time drains in your existing processes.

Improves Human Agent Efficiency

Generative AI isn’t solely customer-facing. Instead, it can be used to support your existing human agents to make them better able to carry out their roles. Generative AI can listen in on calls and generate suggestions for human agents to use, saving them from having to research answers on their own and allowing them to resolve cases faster.

In addition, by automating laborious tasks such as call summarization, Generative AI can save your human team valuable minutes on every call and free them up to focus on more complex challenges.

Offers 24/7 Customer Support

Generative AI Agents are available on a 24/7 basis and can be accessed across multiple channels. This allows organizations to create effective self-service solutions, where customers can interact with Generative AI to get answers and carry out basic tasks.

If the problem needs to be elevated to a human, the AI Agent can carry out all of the time-consuming verification steps and then pass the case to the most appropriate member of the team – saving your organization time and improving the likelihood of a positive customer experience.

Offers Real-Time Translation

Generative AI supports real-time translation in over 100 languages, allowing you to communicate with customers in their native tongue without the associated costs and complexities of external translators.

Expands Industry-Specific Knowledge

Large Language Models are not only limited by their original training data. They can also be provided with additional data for your company or industry via your own internal knowledge hub and other related articles. The AI will act as an expert in your field and can even begin authoring new content to quickly expand your knowledge base.

One potential use case for this is to use Generative AI to create an internal onboarding hub for new employees, which requires significant human hours to create and author. With Generative AI, the whole process can be completed autonomously with minimal human oversight.

Improves Insights

Generative AI can also be deployed in a reporting function that allows it to analyze all of your customer service call logs and generate deep insights that include pattern recognition, sentiment analysis, and more.

With Generative AI, you’ll benefit from predictive analysis that helps you forecast customer needs and behaviors ahead of time. You’ll be able to see patterns of behavior, rising trends and issues that cause customer sentiment to fall. You can use all of these insights to make more informed decisions about future growth.

Reduces Costs

One of the most compelling benefits of Generative AI within a customer service team is its ability to reduce costs. By speeding up customer cases, enhancing human agent productivity, and generating predictive insights used to improve your existing processes, Generative AI allows you to offer customer service at scale without the associated resource costs of hiring more human employees.

Generative AI In Customer Service Use Cases

How can Generative AI be used within your customer service team? Here are some of the most compelling use cases to help you get started…

FAQ Agent/‘Chatbot’

When paired with Conversational AI, Generative AI customer service agents can field common questions and provide instant answers. Compared to waiting for customers to call your service teams and then having to direct them to an FAQ page that was already available on your website, an FAQ ‘bot’ gives customers a sense of personalized support that quickly deals with everyday, low-complexity issues.

Aside from answering customer questions, an FAQ Agent can also be tasked with updating internal knowledge hubs and FAQs. Rather than tasking a human with hours of writing, Generative AI can update existing FAQs or create new ones in a matter of seconds.

Self-Service Agent

For low complexity tasks such as updating personal details, customers often prefer to self-serve rather than having to contact your team. Generative AI allows you to reduce call volumes by creating a self-service agent that can interpret customer requests and execute them by interacting with other backend systems, such as your CRM.

As with other examples in this guide, a Generative AI Agent built to offer self-service will be available 24/7 and provide instant, personalized responses to customers. This means customers will be more satisfied with your business, which will lead to improved customer satisfaction scores.

Agent Copilot

Even the most seasoned customer service representatives need support. Generative AI can be used to provide contextual, relevant information that enhances agent productivity pre, mid and post call.

Before the call, Generative AI can take a customer’s details and create a contextual handover that gives agents more insight into the case before they pick it up. Once the human takes over, the Generative AI Agent stays on the call and transcribes in real-time. It can identify questions and fetch responses, which it displays to the human agent’s screen to save them from manual research.

Once the call ends, Generative AI can create a complete summary in whatever format you require and store it in the most appropriate place, saving significant time for the human agent and allowing them to move on to a new call.

Interested in how Generative AI can support your existing team? See our Agent Copilot page to learn more.

HR/Training Agent

Generative AI can be deployed as an internal HR agent that supports team onboarding and training by generating new materials. It can also act as a self-service agent that your human team can interact with to carry out training, ask questions, and resolve issues – all with the same 24/7 service and instant response speeds Gen AI offers to customers.

AI Agent Training

No enterprise business has one single ‘AI Agent’ that carries out all of its automation. Instead, you’ll need to develop many different AI Agents that are trained to carry out specific processes. This requires building an internal team of AI specialists who can engage in designing conversational flows.

This team can use Generative AI to improve their own “bot building” capabilities, designing more efficient flows in a faster timeframe. Gen AI helps create potential conversational outputs and can also be used to fact-check responses against internal knowledge, acting as an editor and ‘guardrail’ to help reduce the risk of AI hallucinations.

What Are The Challenges When Implementing Generative AI?

Generative AI, like other AI technologies, poses some risks that must be carefully addressed ahead of deployment. Here are the main challenges faced by enterprise organizations to help prepare you ahead of time.

Functional Limitations

One of the most immediate issues businesses encounter when considering Generative AI comes from misunderstanding the scope of the technology. Tools like ChatGPT have led to false assumptions about how AI performs.

For enterprise customer service, Generative AI is not a standalone tool. You cannot deploy a Generative AI interface and expect it to be able to cope with a high volume of customer interactions.

Generative AI must be paired with other technologies, such as Conversational AI, to act as a more comprehensive AI Agent that can handle the unpredictable, nuanced nature of customer conversations.

Data Privacy & Security Challenges

Generative AI also carries some data privacy considerations, both in terms of how the AI is trained (input) and in what it generates (output). Any customer data used by the AI must be collected and stored according to your local privacy laws and your own internal security regulations.

To state the challenge clearly, you simply cannot afford to use Generative AI at enterprise scale without strict security controls in place.

As a result, enterprise organizations must consult with experienced AI vendors who can demonstrate competency and compliance in terms of data privacy risks. See Cognigy’s trust center to see an example of how a cutting-edge AI provider protects data for you and your customers.

Managing Hallucinations

Generative AI can sometimes ‘hallucinate’, where false information is presented as fact. This is an unacceptable risk in your customer service process, so it’s important that you understand how hallucinations can occur and what you can do to prevent them.

As mentioned under the ‘limitations’ header, Generative AI is not intended as a standalone when used in an enterprise organization. By pairing it with Conversational AI, the risk of hallucination is reduced. Not only can you design careful conversational flows to help keep the AI on track, but you can also build additional guardrails that force the AI to use specific terminology or approved language. The use of RAG (Retrieval Augmented Generation) for example, uses the LLM to understand your user’s intent, which you then use to perform a search of your support documents, find the answer, and simply use the LLM to rephrase the standardized answers into contextual ones. Thus, the LLM isn’t actually generating any content but sourcing it directly from approved documents.In addition, you can also deploy a fact-checking AI Agent that is tasked with checking and verifying the customer service agent’s output before it reaches a customer.

Balancing Human Interaction With Automation

Generative AI shouldn’t be viewed as a replacement for your human teams. One key challenge is in building a process that facilitates collaboration between automated systems and human workers, allowing them to embrace the technology and enjoy the benefits it provides.

Achieving a balance between human input and AI support isn’t only beneficial for your internal processes, it’s also vital to customer success. Customers don’t want to interact solely with Generative AI systems. Outside of low-complexity processes, customers prefer human support to help solve their issues.

Examples of Generative AI in Customer Service

Here are some real-world Cognigy clients who have used Generative AI to transform their customer service processes and deliver higher-quality automated support.

Henkel Delivers Instant Customer Responses

Henkel Consumer Brands (HCB) wanted a way to better meet instant, digital-first customer expectations. As an early proof of concept, the team worked with Cognigy to develop a stain support AI Agent that could recognize stains and recommend solutions that featured Henkel’s Persil product range.

This early example is a clear demonstration of the value of Generative AI, allowing Henkel to quickly and efficiently resolve customer concerns about stains in the moment they occur rather than having to speak to a support team or try to solve the problem manually. The early success of this AI Agent led to the team scaling other AI Agents across multiple business areas.

Read the full case study to see how Generative AI can support tailored, personalized recommendations that solve customer needs and position your brand as their expert guide.

Bosch Makes HR Efficient

Bosch has over 400,000 employees across more than 60 countries, making it difficult to provide reliable HR support at scale. As part of their wider AI adoption process, the Bosch team created ROB, an HR AI Agent that could simplify the HR process and support employees worldwide.

ROB allows employees to self-serve many key tasks such as updating personal details, changing bank information, and accessing career development resources and company policy documents.

Generative AI empowers ROB, allowing it to cater to a multilingual audience and provide support on a 24/7 basis across platforms such as Microsoft Teams. To date, Bosch has rolled ROB out to 25 countries and is continuously building on its success by learning from past employee interactions.

Best Practices for Generative AI in Customer Service

Generative AI has the potential to transform your organization, but only if you follow the guidance we’ve laid out in this article. To help summarize the major points, we’ve created these best practices that help guide enterprise clients on a more successful deployment journey…

Start with narrow, high-volume use cases that provide quick wins and can scale over time. Examples such as an FAQ AI Agent that deals with high-volume queries are ideal.
Embed Generative AI within a broader AI ecosystem that includes Conversational AI and Agentic AI to deliver reliable, context-aware responses.
Plan integrations ahead of time and test functionality to make sure Generative AI fits within your existing tech stack without causing any issues.
Acknowledge limitations and manage expectations by setting clear guardrails via conversational flow design. Build human oversight into every process.
Design escalation pathways so customers who are determined to access human support can do so in a timely manner.
Onboard and train employees in how to utilize AI Agents to their advantage. Use Agent Copilot features to augment teams and make them more efficient.
Use Generative AI to augment human agents, supporting them before, during, and after customer calls to improve efficiency, increase productivity, and reduce attrition rates.
Monitor performance continuously to identify new opportunities for growth and refine existing AI Agents using real-time data.

The Future of Generative AI in Customer Service

AI automation is continually evolving. As features improve, the most important factor for all organizations to remember is that AI is not about replacing humans entirely and is instead about augmenting your processes to increase efficiency. As we move into an ever more demanding digital future, AI must be used to support and collaborate with humans rather than to oppose them.

Generative AI will help foster better human/AI working relationships by fostering knowledge, generating tailored responses, and summarizing interactions, allowing human teams to focus on empathy, problem-solving, and relationship-building. This shift won’t replace people; it will empower them to deliver faster, higher-quality support at scale.

Generative AI also opens new opportunities for personalization. By connecting with CRM data and customer histories, AI can anticipate needs, offer proactive solutions, and even adapt its tone to match a customer’s preferences. In the near future, service will feel less like “contacting a call center” and more like engaging with a trusted, always-available advisor.

Agentic AI can also be viewed as a new evolution in the AI field. Like Generative AI, it is informed by Large Language Models but is able to take more conclusive autonomous action, which makes it better able to plan and carry out tasks. As the field continues to advance, look for a future where Agentic AI is responsible for many day-to-day customer service tasks.

Power Up Your Enterprise With Cognigy AI Agents

Pair Generative AI with Conversational AI and deploy powerful AI Agents at scale in your business. Slash customer waiting times, speed up problem-solving, and deliver personalized, engaging customer support at an enterprise level with Cognigy.

Our platform is built for the needs of large organizations and allows you to design and deploy AI Agents in a fraction of the time compared to other types of technology integrations. Cognigy can help you stand at the forefront of Generative AI in a matter of months from your first enquiry with our team.

Frequently Asked Questions

What Is the Difference Between Generative AI vs Chatbots in Customer Service?

Chatbots follow scripts and handle simple, repetitive questions. Generative AI goes further—it understands natural language, adapts in real time, and creates human-like responses. See our chatbots vs AI Agent article to learn more.

How Does Generative AI Improve Customer Service?

Generative AI makes service faster and more natural. It understands intent, pulls answers from knowledge bases instantly, and delivers personalized guidance. For agents, it summarizes calls and suggests next steps, boosting productivity while customers enjoy quicker, smoother resolutions.

Can Generative AI Completely Replace Human Agents?

No. Generative AI is powerful for routine queries and scale, but it doesn’t work as a standalone and must always be overseen by human agents. The future is hybrid: AI handles repetitive work, freeing agents to focus on high-value conversations that build stronger customer relationships.

How Secure Is Generative AI?

With the right safeguards in place, Generative AI is enterprise-grade secure. You must build guardrails and ensure data encryption is in place to keep your business secure. Use continuous monitoring and establish internal AI governance to keep responses accurate, reliable, and aligned with brand and customer expectations.

How Can Generative AI Integrate Into an Existing Customer Service System?

Generative AI connects easily through APIs to CRMs, ticketing, and knowledge bases. It enhances both agent and customer channels with contextual answers, summaries, and recommendations. Integration ensures consistency across voice, chat, and digital touchpoints while scaling support seamlessly.