Artificial intelligence (AI) meets us in more and more areas of everyday life. We have been using smart voice assistants intuitively for a long time, have answered all sorts of questions quickly by ChatGPT and benefit (especially in our professional environment) from numerous AI automations. Related terms such as “Machine Learning,” “Neural Networks,” or “Generative Technology” are now quite familiar to many. But what do they mean exactly how is all of this connected and how does artificial intelligence ultimately work in practice? We want to explain this in an understandable way using specific examples.
What is artificial intelligence? Definition and disambiguation
Before we delve into the specifics of how AI works, it is essential to clarify a few basic terms. Otherwise, important questions would remain unanswered when describing the examples. Artificial intelligence is essentially a branch of computer science that imitates certain cognitive abilities, i.e. thought processes that you constantly and naturally use, often unconsciously, in everyday situations such as recognising, comparing, deducing, and making decisions.
In the core processes, AI information is made available as data. This data can consist of text, images, numbers or other digital content. An AI system ranks them according to specific criteria, connects them, and uses them to identify emerging patterns in order to solve tasks.
The majority of modern artificial intelligence is created by so-called machine learning. Appropriate Systems or System Components can follow more than hard-programmed processes. You learn rules from examples. The more data they analyze, the better their predictions, decisions, or products become — and the more dynamically they can react to inputs or other data.
Machine learning
Machine learning (ML) describes methods that computers use to become smarter from examples. An algorithm receives data and recognises typical patterns within it. It then uses these patterns to classify or derive new meaningful information.
You can think of it as a workout: Each model receives many examples and compares its own results with a target value. If the result differs, the system adapts its internal rules. In this way, forecasts improve step by step.
In contrast to classic programmes, there is no defined solution. Instead, the system develops its own strategy to achieve the best result. This makes machine learning (ML) particularly suitable for tasks that are too complex, extensive or dynamic to program and evaluate manually, such as sophisticated image analysis, speech recognition and predicting user behaviour.
Neural Networks
Neural networks form the technical heart of many modern AI applications. They are inspired by biological nerve networks. In the human brain, neurons transmit signals via synapses. An artificial neural network digitally recreates this process: It consists of many “nodes” that process signals and pass them on to each other.
Each of these connection points has a weighting that determines how strongly a signal is relayed. During training, the network reacts more and more precisely as the node prioritisation is adjusted step by step.
As soon as the neural network has seen enough examples, the result is a model that can interpret unknown data.
Deep learning
Deep learning (DL) expands the principle of neural networks to provide extra depth. You can use this principle as a connection structure with multiple hidden layers that do not route signals directly from input to output, but instead incorporate intermediate stages. Each of these layers extracts a different detail from the data.
To illustrate this, let's take a look at image recognition: The first layer analyses simple lines; the next combines them into shapes; and subsequent layers combine corresponding shapes to form complex objects, such as faces or vehicles. The more layers a network has, the more precisely these features are identified.
NLP and LLM
Natural language processing (NLP) is the basis for machines understanding human speech. NLP can perform many tasks, such as analysing words, deriving meanings, recognising tone of voice and structuring texts. A typical example is sentiment analysis, in which systems assign moods to texts.
Large language models (LLMs) are the development of these classic methods. They are based on deep learning and are trained using vast amounts of text. Through this training, they can deliver coherent, understandable, contextualised answers. LLMs have long been able to do more than answer questions; they can also write content, rewrite texts, explain connections, and combine knowledge.
NLP primarily works based on rules or statistics, whereas LLMs can learn abstract patterns from texts and build flexible models from them. Consequently, they can communicate more naturally and comprehensively, and act more dynamically.
Generative AI
Generative AI is a special form of artificial intelligence that is mainly based on machine learning and deep neural networks. Rather than just analysing existing information, it creates new content. Systems of this type learn typical patterns from large data collections and then use them to create new products, such as text, images, videos, audio or code.
You control generative models using prompts, which are often written or recognised in natural language thanks to LLMs. A prompt is a clear instruction to which the system responds. Based on the learned structures, the AI then generates a result that, although new, matches the training data in terms of style and content.
These capabilities make such systems important tools in creative work, development, marketing and data analysis.
Strong AI vs. weak AI
Even though artificial intelligence often delivers impressive results, systems of this kind are still consistently defined as weak AI. This form solves tasks within clear limits. It can understand, interpret, classify, decide and generate, but not develop its own goals.
The opposite would be strong AI, which does not yet exist. This hypothetical version would fully depict human thought processes. It could plan, evaluate, and solve problems independently, without prompts or fixed data structures. It would therefore be capable of mastering any intellectual task. Even the most powerful deep learning models are far removed from such capabilities.
And how does AI work in practice?
Of course, that always depends on the use case. The AI elements defined in the previous section can be used together in a variety of contexts. Now let's take a look at some central areas of application and how the relevant components interact using typical practical examples. Data and its analysis are always decisive
AI-powered chat and voice assistants
Chat and voice assistants are among the AI applications that you are most likely to use in everyday life. As soon as you talk to your smartphone, ask a chatbot a question while shopping online or ask ChatGPT to explain a text to you, for example, several AI components work together in the background.
First, the respective system interprets your input. With spoken inquiries, the speech is converted to text. Next, a model analyses the meaning, recognises intentions, and filters out the most relevant information. This step is based on natural language processing (NLP) methods and enables the assistant to understand you.
This is followed by finding answers to your query. Many modern systems use large language models (LLMs) for this. They recognise patterns in your formulations and compare them with existing data (in an e-commerce context, for example, enquiries from previous shop visitors). They then suggest suitable content and finally generate an understandable response. This enables them to perform many more functions than just executing short commands: you can explain facts, offer ideas, structure texts, provide advice on problem solutions or create new content.
AI in administration
Many public and private sector administrations and companies use AI to simplify processes, process enquiries more quickly, and free up employees to focus on more important tasks.
An illustrative example is provided by automated bookkeeping. Intelligent systems read documents, classify positions and sort numbers into the correct categories. This is achieved through image recognition, text recognition and pattern analysis. The data can then be migrated to other workflows, such as approval or payment processes and reports.
This chain creates continuous added value. Ideally, there would be fewer errors, shorter processing times and greater transparency. At the same time, planning improves. Systems can create forecasts based on past data, for example about incoming payments or budget developments.
In addition to accounting, many other areas benefit from similar mechanisms. Chatbots can answer customer queries, intelligent tools can prioritise complaints or automatically sort emails, and predictive models can distribute resources efficiently. All of these applications follow the same principles: analyse data, recognise patterns and derive decisions.
AI in marketing, advertising and (e-) commerce
Marketing is one of the areas in which artificial intelligence is particularly versatile. Models analyse data on user behaviour, provide SEO tips and help to create personalised content. As a result, organisations can reach their target groups much more precisely and with less effort.
A typical process starts with analysing large sets of customer and behavioural data. The AI then identifies patterns that allow conclusions to be drawn about interests or purchase probabilities. Based on this, content can be adapted, ads automated or campaigns optimised. Generative technologies can also be used to create texts, images, or videos for various platforms.
Through the connect multiple workflows the result is a comprehensive marketing automation: Leads are generated and evaluated, and then provided with the right content before being handed over to suitable sales representatives. E-commerce provides AI with additional recommendations for potential buyers, analyses demand trends and optimises product descriptions. All these tasks use the same core components you already know: machine learning, pattern recognition and language models.
AI in industrial manufacturing
Many industrial processes continuously generate large amounts of data. Sensor values, temperature data, camera images and machine states are just a few typical examples. AI models can, of course, also analyse such information and interpret specific patterns within it. This is how they help people to gain an overview of manufacturing processes and optimise them.
Optical quality control is a good example of this area of AI application: cameras capture every component and neural networks classify abnormalities in real time, allowing errors of all kinds to be identified at an early stage, before they incur higher costs. In addition, forecast models support planning by assessing investment failure risks. This form of predictive maintenance prevents unplanned downtime and optimises the lifespan of machines.
AI is also used in energy management, where systems analyse consumption data and suggest efficient settings to use resources in a targeted manner. In intelligent power grids, they can even predict loads and adjust capacities accordingly. Overall, there are clear benefits: production processes are designed to be safer, more effective, more energy-efficient and easier to control.
AI in Medicine
In medicine, artificial intelligence impressively shows how a supposedly simple pattern recognition technique can be extremely helpful. Imaging techniques such as X-rays, MRIs and CT scans produce detailed images containing huge amounts of information. AI models support doctors by analysing the finest structures and identifying abnormalities that are very difficult for the human eye to perceive, and search systems can provide clues that would otherwise be overlooked in hectic everyday life.
At the same time, they speed up work processes: blood values, laboratory analyses and medical documents can be automatically evaluated and made available. In research, AI also improves the evaluation of complex data sets. It recognises connections that may be relevant for new therapies or diagnostic methods.
Despite this progress, one thing remains clear: the decision on treatment lies solely with healthcare professionals. However, strong AI could shift this paradigm.
Conclusion
Artificial intelligence has been around for a long time and is established in many areas of life. You encounter voice assistants, automatic product recommendations and intelligent management solutions every day, often without consciously viewing them as AI. The technology behind it almost always uses similar components and combines them into powerful processes: collecting data, recognising patterns, deriving results and using them to provide answers, forecasts or even completely new content.
Through rapid development, AI aims to become even more integrated into all possible processes in the future. Whether in industry, healthcare or everyday digital life, intelligent systems help to make more informed decisions and simplify processes. For you, that means staying tuned! Because those who understand AI can assess its potential and use it to their advantage from the outset will ultimately reap real economic benefits.
FAQ
What is the difference between strong and weak AI?
Weak AI solves tasks within a given framework and does not define its own goals. It recognizes patterns, evaluates data and delivers results within clear limits. Strong AI, on the other hand, describes a (so far) theoretical form of artificial intelligence that could completely simulate human thinking skills. She would be able to make decisions completely freely, plan independently and solve problems without guidelines. Such systems do not yet exist.
Where is AI being used?
Artificial intelligence is already being used in many areas today: voice assistants, medical diagnostics, administration, Customer service automation, industrial manufacturing, marketing and research are just a few examples. In all of these fields, appropriate systems analyze data, recognize connections, perform routine tasks and support people in making decisions.
Does AI also have disadvantages?
Yes, in addition to many benefits, artificial intelligence can also bring certain risks. When systems receive faulty data, they make wrong decisions, which quickly add up in the event of insufficient control. In addition, automated processes can significantly change processes and replace human labor. In addition, the handling of sensitive information requires clear rules so that it is processed in accordance with the GDPR and, last but not least, interpreted correctly. Especially when used professionally, it is therefore very important to fully understand the processes of AI systems used, to identify potential risks and effectively eliminate them. Comprehensive AI advice from a proven AI agency is always recommended in this context.
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Jens ist Mitgründer von bakedwith, einer Boutique-Beratung für smarte Automatisierung und KI. Er unterstützt mittelständische Unternehmen und Konzerne dabei, Prozesse zu optimieren, manuelle Arbeit zu reduzieren und durch effiziente Workflows Wachstum zu erzielen. Zuvor war Jens Growth Lead bei OMR und begleitete als Freelancer zahlreiche Unternehmen bei der Optimierung ihrer CRM- und Automationssysteme. Seine Leidenschaft gilt der Verbindung von Wachstum und Effizienz – damit Teams sich auf das Wesentliche konzentrieren können.
