Demystifying AI: Your Compass in the AI Jungle

We ground the topic for you: What AI is about, what AI does and what AI can do.


Artificial intelligence (AI) has not just been brought to life in recent years. It is a concept that dates back to the 1950s and has evolved considerably over the decades. Pioneers such as Alan Turing and John McCarthy laid the foundations for what we know today as AI. In the rapidly evolving business and technology landscape, AI has proven to be a key enabler.

However, the marketing hype and buzzwords have created a jungle that seems impenetrable for many people and companies. It is difficult for companies to take the first steps to learn more about AI and to understand and recognize the potential for businesses through the effective use of AI.

This blog post aims to provide decision makers with the knowledge they need to make informed decisions about the use of AI.


Understanding AI

AI is a broad field that encompasses various sub-areas, including machine learning, deep learning, natural language processing and more. The most well-known examples currently are Chat GPT and DALL-E. Amidst the buzzwords and marketing hype, it’s important to recognize what AI can realistically do and how it can truly change the world.

Each subfield has its own characteristics and applications, and understanding these is key to using AI effectively. Machine learning, for example, uses algorithms to analyze data, learn from it and make informed decisions based on what has been learned. In deep learning, a subset of machine learning, algorithms are structured in layers to create an artificial neural network that can learn independently and make intelligent decisions.


AI capabilities and business transformation

One way to dive into AI is to look at these capabilities. These range from so-called narrow or weak AI, designed for a clearly defined and unambiguous task, to strong AI or AGI (Artificial General Intelligence), capable of performing any intellectual task that a human can do.

Current developments in AI, and therefore everything we see and read about the subject, are all in the narrow or weak AI range. This ranges from systems that can play a number of different games such as chess or Go better than a human, to the LLMs (Large Language Model) in Chat GPT or AI systems that support the detection of diseases and recommend treatments.

Strong AI or AGI is currently being researched, but the path to it is unclear. Some experts suggest we could have it in just a few years, while others believe we may never achieve it. There is also the concept of super-AI, a hypothetical scenario in which the capability of AI surpasses human intelligence.

One of the most important aspects of AI is its transformative potential. Companies that can harness the power of AI will gain a strategic advantage. Experienced software development partners can play a crucial role in this tra


The Role of Data in AI

Data is the lifeblood of AI! It is the fuel that drives the algorithms and makes AI “intelligent”. Without data, AI would not be able to learn, adapt and improve. Here’s a deeper insight into the role of data in AI:

Quality of Data:

The quality of data has a direct impact on the effectiveness of AI solutions. High-quality data is accurate, consistent and relevant. It is free from errors and biases that could distort AI learning and lead to inaccurate predictions or decisions. Ensuring data quality often involves data cleansing and pre-processing steps that remove errors, process missing values and standardize data formats.

Amount of Data:

AI systems, especially those based on machine learning, often require large amounts of data to learn effectively. The more data the AI system has available to learn from, the better it can make predictions or decisions. However, it’s not just about having lots of data, but also about having different data representing different scenarios and cases.

Data Management:

Effective data management is crucial for AI. This includes collecting data from different sources, storing it securely and processing it efficiently. Good data management practices ensure that data is easily accessible to AI systems while protecting sensitive information.

Data Protection and Data Security:

With the increasing use of AI in various sectors, data privacy and security have become major concerns. It is important to take robust measures to protect data and comply with relevant laws and regulations. This includes anonymizing data to protect individual privacy and using secure methods to store and transmit data.

Ethical Considerations:

The use of data in AI also raises ethical considerations. These include ensuring fairness and avoiding bias in AI decisions, transparency of data use and obtaining informed consent from individuals whose data is used.

Data plays a crucial role in AI, and handling it effectively is key to realizing the full potential of AI.


AI and Corporate Strategy

AI is not just a technology, it is also a strategic business tool. Incorporating AI into your business strategy can lead to significant efficiency and productivity gains.

It is therefore important to align AI initiatives with corporate goals and take measures to measure the success of AI projects and their defined goals, i.e. the return on investment (ROI).

It is also crucial to consider AI in the context of digital transformation.

The importance of a data-driven culture and the need for continuous learning and adaptation to rapidly evolving AI technologies must be given particular consideration.



Understanding the fundamentals of AI is the first step in any organization’s AI journey. Only then is a fact-based assessment of opportunities and possibilities possible and can be applied to specific issues. The topic of AI is complex, but it can be started quickly and in a focused manner. The potential of your own organization can be identified and addressed in just a few steps, so that a structured approach can quickly produce initial results in the first AI projects.


About the Author:

Nicolas Friberg is a freelancer and associate partner at Riwers and covers the roles of business consultant, business coach and organizational architect.

Nicolas has many years of experience in supporting and implementing transformations in companies of all sizes, such as Swisscom or AIstler. In doing so, he relies on proven agile methods and, for some time now, on neuroscientific findings. In this context, he published the book “Mindful Evolution – Building Value-Driven and Learning Organizations” in 2024.

Since May 2024, he has also been a professor for “Learning Organizations and AI” at the Erasmus University in Basel and recently wrote the white paper “Thoughts on the Conscious Application of AI”