Generative AI

What is generative AI?

Generative AI (generative artificial intelligence) comprises AI systems that can independently generate new content – be it text, images, audio or even videos. Unlike discriminative AI models that classify data or make predictions, these models generate something new that is often amazingly human-like. Well-known examples are text generators (such as ChatGPT for speech) and image generators such as DALL-E or Stable Diffusion, which create images from text input. Generative AI usually uses deep learning models (especially neural networks) with special architectures such as transformers or GANs (Generative Adversarial Networks).

How it works

Generative AI models are trained on large amounts of data in order to learn the statistical patterns of this data. A language model, for example, processes millions of sentences and learns which word typically follows which. This enables it to continue a coherent text from a prompt. Similarly, image generators learn patterns for shapes, colors and structures from a large number of images. Generative models often work with probabilities: In each step of content creation, the next element (e.g. next word or image detail) is selected according to the learned distribution function. Modern generative AI such as GPT-4 is able to create contextual contexts and thus produce very coherent and appropriate content.

Areas of application

There are many possible applications for generative AI:

• Text generation: automatic creation of emails, reports or even literary drafts. Generative responses are used in customer service chatbots to respond more flexibly to inquiries.

• Image and video generation: Graphic designers can use AI for inspiration to generate designs. In film, landscapes or characters can be created synthetically. However, there is also potential for misuse (e.g. deepfakes: realistic-looking fake videos).

• Music and audio: AI composes pieces of music or generates human voices (speech synthesis) for audio books and assistants.

Generative AI in customer service

In customer communication, such as in contact centers, it can play a decisive role in increasing service quality and efficiency. Here are some key areas of application:

• Automated customer queries and responses: generative AI can act as an intelligent chatbot and answer frequently asked customer queries automatically. This allows inquiries to be processed faster and more accurately, and agents can focus on more complex issues.

• Voice and text summarization: Generative AI can help to summarize key content during long customer calls or chat conversations. This allows agents to get a quicker overview of conversations and saves teams time when following up.

• Training and support for service agents: AI can provide suggestions on how to conduct a conversation, tips for the next step or suitable wording. In this way, it supports agents as a "digital co-pilot" and improves service quality.

Advantages in the contact center

Efficiency through automation

• Generative AI enables companies to automate repetitive tasks and standardized responses. As a result, a higher volume of inquiries can be handled without the need for additional staff, which significantly increases the efficiency of customer service. AI is able to answer frequently asked questions quickly and process inquiries directly, reducing waiting times for customers.

Relief for service employees

• Generative AI can act as a support system for service employees by providing contextual suggestions on how to conduct the conversation, helpful information or relevant answers. This support saves agents valuable time and allows them to focus on more complex issues, reducing workload and increasing employee satisfaction.

Analysis and optimization

• Generative AI can provide valuable insights into common customer needs and issues by analyzing customer requests and interactions. Companies can use this data to improve their products or services in a targeted manner and optimally adapt their offers to customer wishes.

Personalization of customer communication

• A major advantage of generative AI is its ability to individualize. It can analyze requests in the context of the individual customer profile and generate tailored responses. This makes customers feel personally addressed, which contributes to stronger customer loyalty and a positive brand experience.

Challenges

Generative AI comes with challenges. One of the biggest problems is hallucinations – the model invents facts or details that do not correspond to reality. There is also a risk of copyright problems if generated content is strongly based on training data. Misinformation through AI-generated texts and the effects on creative professions are also being discussed in society. Guardrails and guidelines are therefore being developed in order to use generative AI responsibly so that opportunities (automation, creativity boost) can be exploited and risks mitigated.