Know-how, trends
& insights

Large language models provide impressive answers – but not always the right ones. The Co-Star framework shows how companies can get more precision and consistency out of AI with smart prompting.

Good prompt engineering is the basis for ensuring that an AI system works precisely, consistently and in a customer-oriented manner. Here are the most important strategies and practical tips for those responsible for using or introducing AI in everyday service.

What is shadow AI and how can companies prevent its use by employees and the associated security risks?

Experience shows: The success of chatbots depends not only on the technology - but above all on how chatbots are planned, introduced and further developed. Here are a few tips.

Up to 80 percent of AI projects fail. This is not due to the technology - but to the implementation. Here are a few practical tips on how to overcome typical stumbling blocks in AI projects.

Prompting - this means entering a prompt or question into the AI solution so that it provides a correct and useful answer. But how does this work in practice? Here are five tips for clever prompting!

We are currently experiencing rapid growth and major advances in AI agents. It's all about collaboration, coordination and new, individualized offers.

Generative AI gives companies the opportunity to make email processing more efficient, increase customer satisfaction, and reduce the workload of employees.

Harald Henn shows how Agentic AI intelligently networks company systems, coordinates tasks independently and creates new potential and challenges for companies in the process.

Service expert Harald Henn explains the opportunities and risks of Agentic AI in customer service.

Large language models (LLMs) are currently outdoing each other not only through better performance, but also through ever larger so-called context windows. But what does that actually mean?

Technological developments are happening one after the other, and this also applies to the further development of LLMs. What used to be text-based language models are now omni-modal.

AI is more than just a tool for quickly answering questions – it can uncover in-depth patterns and thought processes. This requires the right prompts.

Germany is in the midst of an economic crisis. And if you not only want to survive it, but end it, the issue of efficiency gains is key. AI-based solutions and AI technology can bring significant benefits here.

If you've ever wanted to know how to give an AI better instructions or ask better questions, we recommend getting help from an AI!

To really meet customer needs, companies need to listen to their customers better. This requires analyses and predictions of customer behavior. But here, as the CEX Trend Radar 2025 shows, too little is happening.

How can chatbots and voicebots be used to access current and internal knowledge? This is where RAG comes into play.

Comprehensive testing is crucial for companies to be able to use large language models (LLMs) safely and effectively. This is because models that are not thoroughly tested can deliver incorrect or biased results.

One urgent security issue at the moment is the implementation of the EU AI Act - and therefore the protection of personal data through anonymization and pseudonymization. Tip: There is technical support for this.

Cyber threats are becoming increasingly sophisticated. This means that our defenses must become smarter as well.

With AI, it's easy to get carried away by technological breakthroughs and the potential. But we must not lose sight of one critical aspect: ethics.

There is hardly a sector of the economy that is not yet looking at the opportunities offered by AI. But instead of jumping on the bandwagon, many companies are afraid of - well, what actually?

AI is permeating companies and our work. We are confronted with new announcements almost every day. One of the main drivers for the implementation of AI is the pursuit of greater efficiency. A major obstacle is uncertainty.

Previously, the legal requirements for the use of AI in customer service were primarily shaped by laws and regulations governing the processing of customers' personal data. With the EU AI Act, additional regulations have been introduced.

The EU AI Act provides for the categorization of AI systems into risk groups. Many systems in customer service will probably be categorized as "minimal risk" or "limited risk".

AI-based training bots offer fascinating new opportunities for reflection, training and coaching in companies.

VIER CEO Rainer Holler on the successful preview of the AI Gateway.

These are the 6 AI trends for companies: Which strategies are right, which jobs will become obsolete?

With the introduction of chatbots and voicebots, you can automate numerous processes in order to act more efficiently and customer-oriented. Here are 10 typical processes that are particularly suitable for automation using bots.

Natural communication in human-machine interaction is becoming increasingly natural. This calls for new applications based on LLMs. At the same time, new challenges arise in terms of the security and performance of the applications.

At the end of 2022, ChatGPT appeared, a generative language model that eclipsed all previous ones. But what is different about ChatGPT?

In order to meet the wishes of its customers, VIER is gradually making new web interfaces available to its customers with immediate effect.

The majority of customer enquiries are still made by telephone in 2023. The problem: dealing with telephone enquiries is personnel-intensive and sometimes difficult to plan.

The world is overflowing with predictions about the AI bot ChatGPT. But let's take a sober look: What are the possible applications of conversational automation in customer service? And what is ChatGPT actually?

AI-based technologies help to automate simple tasks, but on the other hand this means: the remaining requests are complex and demanding. Support is needed.

The extremely powerful AI chatbot ChatGPT is the talk of the world – but what are the benefits of using it specifically in customer service?

I'm holding the first results of the Contact Center Trend Study in my hand and I can see: The potential of AI is not yet fully exploited!

Automation can be customer-friendly and service-oriented. And it can support employees instead of replacing them.

In the competition for qualified talent, contact centers often miss out. So it's high time to focus on the employee experience.

Language has been with us since our earliest childhood days, it is omnipresent and we produce it constantly - usually without thinking about this process. Becoming aware of the overwhelming importance of language enables us to make targeted use of its power.

Although AI and psychology are closely related, the two branches of science have mostly gone their separate ways. However, the two research approaches can now be combined. This leads to interesting findings and possibilities.

Where people still like to make a lot of phone calls, AI is able to provide effective support to make calls more successfully.

We all know the cost pressure in call, contact and service centers. Unfortunately, they are still far too often run as cost centers.